Abstract

Objective: Death mechanisms of deceased organ donors in the United States provide valuable information about disease trends at the population level. It was unclear how the deceased organ donor pool was impacted by the onset of the Coronavirus disease 2019 pandemic in 2020. 

Methods: Using the metrics produced by Organ Procurement and Transplantation Network (OPTN) standard analytic files, we analyzed deceased donor death mechanisms between Jan 1^st, 2010 to December 31^st, 2020 in the US.  

Results: Deceased organ donors increased from 11,870 in 2019 to 12,588 in 2020 (6.0% increase) despite the pandemic. Over the 11-year period, deceased donors from drug-intoxication death (drug death donor, or DDD) had the largest percentage increase from 4.3% of all deceased donors in 2010 to 16.1% in 2020, while deceased donors from intracranial hemorrhage/stroke (39.6% to 25.3%), blunt trauma (22.3% to 17.2%) and gunshot wound deaths (10.0% to 7.7%) all experienced decline. Between 2019 and 2020, DDD was the only major death mechanism category that showed significant increase (from 13.5% to 16.1% of all deceased donors in 2019 and 2020, respectively). 

Conclusion: During the early COVID-19 pandemic, DDD was the only deceased donor category that showed a major increase. This was likely due to a sharp rise in national drug overdose death in the midst of economic and social disruption brought upon by the pandemic. 

Keywords: deceased organ donor, COVID-19, drug intoxication death

Introduction

In the United States, all organ procurement organizations and transplant centers report donor data to Organ Procurement and Transplantation Network (OPTN), including age, sex, race, mechanism of death, as well as donor risk type.(Israni et al., 2021) The annual number of deceased donors in the United States increased 29.5% from 2010 to 2017, with deceased donors from drug-intoxication related death (DDD) being the category with highest increase.(Abara et al., 2019) With the unprecedented disruption of healthcare delivery in the United States and the acceleration in drug overdose death during the early phase of the Coronavirus Disease 2019 (COVID-19) pandemic,(Centers for Disease Control and Prevention, 2020) it is unknown how COVID-19 has impacted the organ transplantation operation and the makeup of the deceased donor pool.  

Methodology

Aim of the study

The aim of this study was to examine the trend and pattern of deceased organ donors by death mechanisms during the early COVID-19 pandemic.

Research Design

This was a retrospective analysis of OPTN’s Scientific Registry of Transplant Recipients (SRTR) standard analytic files released on March 8th, 2021.

Hypothesis

The hypothesis of this study was that the deceased organ transplant donor pool was impacted by the COVID-19 pandemic. Specifically, deceased donors from drug intoxication death may experience a significant increase as a result of the measures taken to contain the spread of the pandemic. 

Consent and Ethical Issues

The data reported here have been supplied by United Network for Organ Sharing as the contractor for OPTN. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy of or interpretation by the OPTN or the U.S. Government.

This study did not require ethics review board approval because the study subjects were deceased and the dataset used was completely de-identified.

Data collection procedure

Using the metrics produced by OPTN’s SRTR standard analytic files released on March 8th, 2021, we examined characteristics of deceased organ donors between Jan 1^st, 2010 and December 31^st, 2020.  Deceased donor counts included all donors for whom at least one organ was recovered for transplant. Mechanisms of death (asphyxiation, blunt injury, cardiovascular, drug intoxication, gunshot wound, intracranial hemorrhage (ICH)/stroke, natural causes, and seizure) were analyzed. Nationwide age-adjusted annual rates of drug intoxication-related deaths from 2010 to 2020 were obtained from National Vital Statistics System (NVSS).(Hedegaard et al., 2021; Multiple Cause of Death, 1999-2020 Request, n.d.)

Statistics

Descriptive statistics and frequencies were calculated by year to assess trends. Trend analyses were performed using R version 3.3.3 software (R project, Vienna, Austria). Graphics were created by using STATA (Stata Statistical Software: Release 17. College Station, TX). 

Results

The annual number of deceased organ donors steadily increased from 7,943  in 2010 to 10,286 in 2017, consistent with previous report by Abara et al.(Abara et al., 2019) The increase continued uninterrupted from 2019 (11,870 ) to 2020 (12,588, 6.0% increase, Figure 1A). Of the top 5 death mechanism categories: ICH/stroke remained the largest: from 3,143 (39.6% of total deceased donors) in 2010 to 3,181 (25.3%) in 2020, with trend slope for percentage of total deceased donors at -1.4% (p<0.001) (Figure1B).  Blunt trauma increased in counts from 1,770 in 2010 to 2,166 in 2020, but decreased in percentage of total from 22.3% to 17.2% (trend slope -0.6%, p<0.001). Gunshot wound similarly saw a small increase in counts from 795 to 963, while decreased in percentage from 10.0% to 7.7% of all deceased donors from 2010 to 2020 (trend slope -0.2%, p<0.001). Deceased donors from Cardiovascular disease rose from 1,047 (13.2%) to 2,465 (19.6%) during the study period (trend slope 0.6%, p<0.001). DDD led all death mechanism categories with the largest increase in both count ( 342 to 2,028) and percentage of total (4.3% to 16.1%) from 2010 to 2020 (trend slope 1.3%, p<0.001). 

Between 2019 and 2020, DDD was the only death mechanism category that showed significant increase: from 1,604 (13.5%) in 2019 (Figure 2A) to 2,028 (16.1%) in 2020 (Figure 2B), p<0.001, while the other categories showed no statistically significant change.

Percentage of DDD of all deceased donors from 2010 to 2020 was plotted against NVSS’s age-adjusted national annual drug overdose deaths in Figure 3. The two curves were nearly superimposable starting from 2015. An exponential model predicted the % DDD/national drug intoxication-related death for 2020 at 2.40% and the drug intoxication-related death for 2020 to be 80,936 in the United States, while the final statistics released by NVSS was 91,799.(Hedegaard et al., 2021; Multiple Cause of Death, 1999-2020 Request, n.d.) 

Discussion

Despite the unprecedented impact of the COVID-19 pandemic on the global healthcare delivery, OPTN data showed that deceased organ donors rose appropriately from 2019 to 2020, attesting to the resiliency of the organ donation and transplantation system. 

Deceased organ donors remained largely unchanged with an average of 7,486 per year prior to 2010, after which the ongoing U.S. opioid crisis has resulted in an increase in eligible organ donors from drug overdose deaths.(“Causes of Death among Deceased Donors,” n.d.) The DDD increase accelerated after 2013 when universal donor testing for Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) was mandated, and guidelines for categorizing increased risk donors established.(Seem et al., 2013) The passage of HOPE Act allowing organs from HIV infected donors to recipients also with HIV infection (Boxer, 2013) as well as the FDA approval of direct acting antiviral drugs to treat HCV in 2015 further boosted the usage of DDD donor organs that were otherwise deemed unusable previously. These milestone events led DDD increase by 125% between 2013 and 2016 (from 560 to 1,262, or 6.8% to 12.7% of total deceased donors). This trend, however, slowed and plateaued in 2017 (13.4%), 2018 (13.1%) and 2019 (13.5%), only to see a sharp rise in 2020 (16.1%). Given the close mirroring of DDD of the national drug intoxication death (Figure 3), the most plausible explanation for this significant increase was due to the increased mortality from drug intoxication in the year 2020, when the pandemic related economic hardship, disruption of healthcare, and loss of in-person social support resulted in increased anxiety, depression and social isolation.(Englander et al., 2020) This model projected the national drug overdose deaths at 80,936 for 2020, as compared to 74,511 in 2019. This projection was very close to the reported 81,000 drug overdose death in the one year period ending May 2020 as reported by CDC,(Centers for Disease Control and Prevention, 2020) a record breaking number that was believed to be related to COVID-19. While comprehensive prevention strategies are being adapted to address the drug overdose epidemic in the context and aftermath of the COVID-19 pandemic, it remains to be seen how the drug overdose death curve will respond to these interventions, with the DDD data from OPTN (usually available in March of the following year) offering a precious 9 month-lead time for public health efforts to react prior to the NVSS data becoming available towards the end of the year. 

Conclusion

Analysis of OPTN’s deceased donor data showed that number of deceased organ donors with drug intoxication as the mechanism of death rose sharply from 2019 to 2020, reversing a trend of minimal change from 2016 to 2018, likely due to the profound impact of the COVID-19 pandemic on the national drug death crisis. 

Limitations

The findings in this report are subject to the following limitations: demographic and clinical data were detailed by the annual OPTN/SRTR data report: Deceased Organ Donor (Israni et al., 2019, 2021) and hence not included in this brief communication; second, data are limited to donors from whom at least one organ was recovered and do not include persons who might have been considered for donation but from whom no organs were recovered. Therefore, the mechanism of death tally may not fully reflect all persons considered for organ donation. 

Acknowledgements

I would like to thank my family for their support and my science teacher Mrs. Shinae Park for her encouragement. 

References

Abara, W. E., Collier, M. G., Moorman, A., Bixler, D., Jones, J., Annambhotla, P., Bowman, J., Levi, M. E., Brooks, J. T., & Basavaraju, S. V. (2019). Characteristics of Deceased Solid Organ Donors and Screening Results for Hepatitis B, C, and Human Immunodeficiency Viruses—United States, 2010-2017. MMWR. Morbidity and Mortality Weekly Report, 68(3), 61–66. https://doi.org/10.15585/mmwr.mm6803a2

Boxer, B. (2013, November 21). S.330 – 113th Congress (2013-2014): HIV Organ Policy Equity Act (2013/2014) [Legislation]. https://www.congress.gov/bill/113th-congress/senate-bill/330

Causes of Death among Deceased Donors. (n.d.). ATC Abstracts. Retrieved June 12, 2022, from https://atcmeetingabstracts.com/abstract/causes-of-death-among-deceased-donors/

Centers for Disease Control and Prevention. (2020, December 17). HAN Archive—00438 | Health Alert Network (HAN). https://emergency.cdc.gov/han/2020/han00438.asp

Englander, H., Salisbury-Afshar, E., Gregg, J., Martin, M., Snyder, H., Weinstein, Z., & King, C. (2020). Converging Crises: Caring for Hospitalized Adults With Substance Use Disorder in the Time of COVID-19. Journal of Hospital Medicine, 15(10), 628–630. https://doi.org/10.12788/jhm.3485

Hedegaard, H., Miniño, A. M., Spencer, M. R., & Warner, M. (2021). Drug Overdose Deaths in the United States, 1999-2020. NCHS Data Brief, 426, 1–8.

Israni, A. K., Zaun, D., Rosendale, J. D., Schaffhausen, C., McKinney, W., & Snyder, J. J. (2021). OPTN/SRTR 2019 Annual Data Report: Deceased Organ Donors. American Journal of Transplantation: Official Journal of the American Society of Transplantation and the American Society of Transplant Surgeons, 21 Suppl 2, 521–558. https://doi.org/10.1111/ajt.16491

Israni, A. K., Zaun, D., Rosendale, J. D., Schaffhausen, C., Snyder, J. J., & Kasiske, B. L. (2019). OPTN/SRTR 2017 Annual Data Report: Deceased Organ Donation. American Journal of Transplantation: Official Journal of the American Society of Transplantation and the American Society of Transplant Surgeons, 19 Suppl 2, 485–516. https://doi.org/10.1111/ajt.15280

Multiple Cause of Death, 1999-2020 Request. (n.d.). Retrieved June 12, 2022, from https://wonder.cdc.gov/mcd-icd10.html

Seem, D. L., Lee, I., Umscheid, C. A., Kuehnert, M. J., & United States Public Health Service. (2013). PHS guideline for reducing human immunodeficiency virus, hepatitis B virus, and hepatitis C virus transmission through organ transplantation. Public Health Reports (Washington, D.C.: 1974), 128(4), 247–343. https://doi.org/10.1177/003335491312800403

About the author

Allison Zhu

Allison is a rising Senior student at the Lawrenceville School in New Jersey. She has a strong interest in analyzing the impacts of social determinants of health at the population level. Two topics she is most interested in contributing are childhood obesity and illicit drug use in teenagers.

The post Impact of the COVID-19 Pandemic on Organ Donation from Deceased Donors in the United States appeared first on Exploratio Journal.

Abstract

COVID-19 virus has invaded the world, just like how cancer spreads from one body part to another and slowly invades our whole body. A virus is a very small particle that can cause an infection and has the ability to only replicate in its host cell. COVID-19, first identified in Wuhan, China, is a type of Coronavirus whose infection has caused the world’s deadliest pandemic. This infection is caused by the airborne virus named SARS-CoV 2 virus and can cause symptoms from mild cold and cough to serious respiratory problems. But the problems are not limited to physical health, it can extend to mental well-being as well. Many have suggested k that people might go into depression after contracting COVID-19 as a result of external factors like weakness, loneliness, a lack of social life, lower incomes etc. While these factors may be a part of the reason, what if the real root cause of this problem is the entry of the virus into the brain and manipulating its functioning? This review article will discuss this possibility. It will also discuss the possible mechanisms of the COVID-19 virus to enter the brain. For example- It could enter through the olfactory nerve, cribriform plate, or the Angiotensin converting enzyme 2 which is present in the blood brain barrier.

Introduction

The word ‘pandemic’ has been circulating in our mouths for quite some time now. We have been living under the rule of a virus named Coronavirus or more specifically COVID-19 for the past 2.5 years and these years have been difficult for everyone. This pandemic has shaken the world completely and things seem to be far from normal. Taking rapid antigen tests is a part of our daily routine now, and masks and face shields have become an accessory. Despite knowing the seriousness of this virus and its effects, people have started to take it lightly but have you ever wondered that once this pandemic ends, it still can have long term effects on us?

The COVID-19 pandemic is an ongoing global pandemic that is caused by severe acute respiratory syndrome coronavirus 2. This virus was first identified in Wuhan, China in December 2019. Unfortunately, this virus spread around the whole world and the World Health Organization declared it a pandemic on 11th March, 2020. As of August 1st, 2022, there have been more than 577 million cases and 6.4 million confirmed deaths and this makes it one of the deadliest pandemics in history. (Wikipedia, 2022)

This is an air-borne disease and transmission can also occur if contaminated fluids reach the eyes, mouth or nose. COVID-19 can be symptomatic as well as asymptomatic making it even more harmful and an infection to be extremely cautious of. Some of the symptoms that appear in infected patients are fever, headache, loss of taste and smell, sore throat, nausea, diarrhea and many more. COVID-19 virus not only affects physical health but also mental health, it has also been found that those infected are also suffering from mental disorders like depression and anxiety, due to which the cases of depression and anxiety have skyrocketed ever since the pandemic. In fact, it has been found that among more than 3900 people who contracted COVID-19 between May 2020 to January 2021, 52% of them showed signs of depression. One in five COVID survivors develop mental disorders like depression, anxiety or dementia. A survey funded by the Alberta Charitable Health Foundations found that 50% of the 6000 people that were surveyed had anxiety, 40% were depressed and about 85% had moderate to high stress. Symptoms under the umbrella of anxiety and depression that have been reported alongside the pandemic are excessive nervousness, fear, worry, lack of energy, and agitation. These symptoms suggest that the external factors of the pandemic (loss of job, fear of contracting the virus etc) may be responsible for the upswing in depression and anxiety, however, it is also possible that the virus itself may be able to enter the brain, directly causing the above mentioned symptoms. (Weber, 2020; Government of Canada, 2021; WebMD, 2022)

This paper will address the question of whether a virus, such as the current COVID-19, could directly impact how we feel, or our mental state. We will first discuss the different ways that other known viruses have been known to either have direct or indirect access to the brain and what mechanisms we propose they have used.

Viruses with indirect access

From what we currently understand, there are both indirect and direct ways that a virus can impact brain function and human behavior. Here we will cover several examples of viruses, one of which can have both direct and indirect access, and one that only can access indirectly. Influenza is a viral infection that attacks and deteriorates your respiratory system which includes lungs, throat and nose. Influenza can attack any age group and its symptoms include fever, headache, chills, muscle aches, persistent cough, ear pain etc. Influenza patients also show symptoms of anxiety and depression and studies have found that it is said to have an indirect relation with anxiety, depression and PTSD. If a patient is under stress, or anxious or depressed then his/ her immune responses to the Influenza vaccines reduces drastically. The reason behind this is that when a person is anxious or depressed or is going through mental health issues then there are changes in the neuroimmune pathways which causes stress related immune disregulation which decreases the body’s influenza antibodies’ response to the virus and hence influenza can be more serious in patients who have anxiety or depression. Studies and surveys are being conducted to find a deeper relation between influenza and depression and anxiety. However, studies have shown that state anxiety which is a temporary emotional condition followed by tension and fear of a particular event or activity has positive effects with Influenza antibodies. Such people tend to take extra personal hygiene care because they have the fear of the pandemic. Similarly, when people with ARDS (intensive for the influenza infection) are admitted in ICU, they are likely to develop PTSD. Interestingly, despite so many studies showing evidence for indirect linkages through neuroimmune pathways to the neurological diseases and effects, Influenza has shown to have a direct connection with one neurological disorder which is the stroke. The seasonal variation in stroke has been related to respiratory tract and influenza infections. Patients with increased respiratory tract infections are more susceptible to stroke. Influenza virus can cause stroke through the inflammatory mechanism. During the infection, the respiratory tract releases cytokines into the blood that induced macrophage inflammatory protein-2 and other inflammatory mediators in the ischemic brain. This is followed by disruption of the blood brain barrier which causes an increased rate of intracerebral hemorrhages after tissue plasminogen activator treatment. Stroke is generally caused by a pre-existing state of inflammation due to atherosclerosis, obesity, or infection. The systemic inflammation due to lipopolysaccharides may aggravate neuroinflammation in cerebral ischemia and thus cause a stroke. Studies are being conducted to provide deeper evidence and connection of the systemic inflammation (neural inflammation) with the influenza virus. (Coughlin, 2012; Mayo Clinic, 2021; Mohammad,2011)

HIV (Human Immunodeficiency Disease) is a retrovirus that can cause a breakdown of the body’s immune system. Contracting HIV virus can lead to the development of AIDS (Acquired Immunodeficiency Syndrome). This syndrome marks the array of diseases and malignancies and is diagnosed by the presence of T cells, cancer and opportunistic infections. It can be transmitted through blood, sexual contact or perinantal care. HIV has a very interesting indirect mechanism related to neural disorders. Although neurodegenerative disorders like depression, PTSD, stress may develop after contracting HIV, the reverse can also take place. People who are diagnosed with PTSD, stress related disorders or depression are more likely to engage in sexual activities, drug and alcohol use and as a result be exposed to the virus and develop AIDS. Evidence from various surveys also proves that patients with depression, PTSD were diagnosed with HIV/AIDS as well. Patients with PTSD had advanced symptoms of HIV, prior history of trauma and greater perception of stigma. Patients with HIV may develop depression as a result of disease progression. Some antiretroviral treatment may cause the CNS to be toxic and depression and disruption of sleep may develop as result of the treatment. Further studies are being done to find a more detailed link between HIV/AIDS and neurodegenerative disorders. (NASW, 2019; Coughlin, 2012)

Viruses with direct access

There are a few viruses that seem to have direct access to the brain. For example, Herpes simplex encephalitis is a contagious infection caused by the herpes simplex virus which is a neurotropic double stranded DNA. Its symptoms include fever, headache, nausea, fluid-filled blisters etc. This infection impacts the temporal and frontal lobes of the brain which leads to edema, hemorrhage and necrotic changes in the brain parenchyma. It has been found that the herpes infection has a direct connection with neurodegenerative disorders. In some hosts, this virus may produce changes in the cellular processes that are required for the normal function of the neurons. Followed by the infection, there are changes in the permeability of the blood brain barrier because of which it is easy for the virus to disrupt the blood brain barrier and infect the brain parenchyma and this increases the movement of the virus, cytokines and the immune cells into the brain. Since the immune system responds as soon as the virus enters, the immune cells that enter the Central Nervous System to disrupt the virus may be responsible for the chronic inflammation of the brain tissue which can give rise to disorders like stroke, Alzhemiers etc. Moreover, it has been found that stress, depression and anxiety are also related to the infection caused by the virus. Stress and psychological disorders can result in shingles in people who are not vaccinated. Lastly, depression decreases the vaccination’s immunity response to the virus due to which the effect of the virus can increase in depressive people. As one starts aging, there is a decrease in the immune response to the zoster vaccine due to which the virus may activate again (if they were infected earlier) or can easily enter the organism’s body. (Coughlin, 2012; Liu, 2018; Duarte, 2019)

Hepatitis is a viral infection that causes the inflammation of the liver. It can be caused by three types of viruses namely- Hepatitis A, Hepatitis B and Hepatitis C. The liver is an important part of our body, it helps in performing numerous functions like filtering of the blood, processing nutrients and fighting infections. If the liver is damaged, then all the other organs would be adversely affected. Depression, fatigue and neurocognitive deficits are the major symptoms of this disease. The mechanism of the virus to get into the brain is similar to the way the herpes virus enters the brain. The hepatitis virus too disrupts the blood brain barrier which increases the exposure of the CNS to the virus, cytokines and the immune cells which in turn inflates the tissues of the brain. These intracerebral changes lead to the symptoms of the infection which are depression and neurocognitive diseases. HCV ribonucleic acid has been found in the peripheral blood mononuclear cells, cerebrospinal fluid and the brain of chronically infected patients. Additionally, depression may lead to increase in psychological stress, and physical symptoms of the disease. (CDC, 2020; Coughlin, 2012; Yeoh, 2018; Stanculete, 2017)

Given the above information, it’s possible that the Sars-CoV virus has direct access to the brain and could directly cause the symptoms, like Herpes and Hepatitis. This paper will present the evidence above as well as several recent studies around the mechanisms of Corona viruses and those similar to answer this question.

Methods

For this article, research was done by reading through and extracting information from many articles. The main search engines used to collect research articles were Google Scholar and Wikipedia for other information around the viruses. Our research included data and statistics from 2018. The main keywords used while researching were depression, anxiety, COVID-19, SARS, age based groups, mechanisms of the entry point of the virus, angiotensin converting enzyme 2, names of various viruses like Influenza, Hepatitis C, Herpes and HIV. Our study mostly included studies that examined individuals from all age groups except for connection between COVID-19, depression and age based groups which included people from 18 to 40 years of age. This paper primarily talks about effects and mechanisms of COVID-19 and SARS into the brain in a human body except for the entry point of SARS into the brain through the olfactory nerve which was for now tested in wild mice which was under the control of human cytokeratin 18 promoter. This review investigates if COVID-19 virus can enter the brain or not and cause neurological disorders like depression, anxiety, PTSD etc and its mechanism of entering the brain if it is possible. An important factor while searching the articles was to make sure to find the direct mechanism of the entry of COVID-19 into the brain rather than through entering other organs (ex-lungs) first.

Results

Points of entry and virus structure

Studies are showing that, it is possible that SARS-CoV, similar to MERS-CoV, infects the lungs and the brain, however, the timing of infecting the 2 organs is different. The virus attacks the brain much later than the lungs which shows that the virus follows a hematogenous route of infection which indirectly affects the functioning of the brain. Furthermore, SARS-CoV infected the vital brain stem nuclei in wild mice which was under the control of the human cytokeratin 18. There was no initial inflammation of the neuronal tissues but at day 4, signs of inflammation were seen. But did the COVID-19 virus enter the brain directly or indirectly and if it entered directly, what could be the possible mechanisms? (Natoli, 2020)

Below we cover several possible entry points for the virus given the work on it or similar viruses.

Olfactory nerve The olfactory nerve is one way through which the SARS CoV could get into the brain. The olfactory nerve is a cranial nerve that contains sensory nerve fibers relating to the sense of smell. The afferent nerve fibers are responsible for transmitting impulses about odors to the Central Nervous System. The yellow coloured fibers depicted in Figure 1 are the olfactory nerve fibers. Since the nose is one of the ways by which the virus can enter our body, so as soon as the virus enters through the nose, it can come in contact with the olfactory nerves since the olfactory nerves and nose are connected so the COVID-19 virus could get attached to any of the fibers of the olfactory nerve and it could easily enter the brain in no time. Moreover, it also explains the symptom of loss of smell when we are diagnosed with this infection. The most probable reason is because the virus affects the functioning of the olfactory nerve fibers due to which they cannot transmit the impulses of odor to the brain and hence we cannot smell anything. An experiment that was tested on wild mice which was under the control of the human cytokeratin 18 promoter showed that the virus infected the respiratory epithelium and then the alveoli, however, the kinetic profile of the viral antigen showed that the virus infected the olfactory bulb first and then gradually infected the subcortical and cortical regions thus showing that the virus infected the brain. (Natoli, 2020; Wikipedia,2022)

Enzyme activation through Blood Brain Barrier Although COVID-19 is suspected to have side effects on the respiratory system of an organism, studies have shown that it can have an effect on the brain too through various mechanisms. One such mechanism is the angiotensin converting enzyme 2 that is present in the blood brain barrier. The blood–brain barrier (BBB) is a physicochemical barrier that constrains the entry of molecules and toxic substances into the brain. It is composed of Blood endothelial cells that limit the entry of molecules with molecular weights higher than 500 Da into the brain. However, many infectious agents manipulate the machinery of the brain endothelial cells to gain access to the brain. As mentioned earlier, one such machinery is the Angiotensin converting enzyme 2 which can be found on the membranes of the cells in the intestines, kidneys, heart and gallbladder or can also be found in the soluble state. It can also be found in the cytoplasm of the cell bodies of the neurons. It is an important part of the renin angiotensin aldosterone system which is responsible to keep the body’s blood pressure normal. The CoV spike glycoprotein has a very high affinity for the angiotensin converting enzyme 2 (a major reason is due to the sluggish movement of blood in the brain due to which it can easily come in contact with the above mentioned enzyme) and immediately binds with it thereby causing changes and damage to the body’s blood pressure system. This will affect the normal functioning of molecular delivery into the brain which would be a major pathway for the virus to enter the brain. If the virus enters the brain, it would cause dysfunction of the brain and these metabolic changes in the brain can lead to various neurological disorders. As a result, there would be changes in the functioning of the mitochondria of the endothelial cells in the brain which would lead to higher chances of stroke. Lastly, post mortem reports of those infected have detected the SARS-CoV N protein and the RNA polymerase gene fragment in the neurons along with the angiotensin converting enzyme 2 in the neurons and the endothelial cells of the brain which suggests that SARS has an entry point into the brain through the angiotensin converting enzyme 2. (Natoli, 2020; Wikipedia, 2022; Kim, 2021)

Cribriform plate: The Cribriform plate is a sieve like, spongy structure between the anterior cranial fossa and the nasal cavity. It is a part of the ethmoid bone and its deep grooves support the olfactory bulb. Since this cribriform plate is sieve like and is connected to the nasal passage as well as the olfactory bulb, there is a possibility that when the COVID-19 virus enters our body through the nose, it passes through this cribriform plate where it has access to the olfactory bulb and in turn to the olfactory nerve. (Baig, 2020)

Age-related or demographic evidence

Have you ever wondered that some age groups might be more prone to the COVID-19 virus entering their brain than others? Have you thought that even though we have physically recovered from COVID-19, we can still suffer from its long-term effects? Specifically, some age groups might have less mortality rate than others but they surely have longer after effects.

Young adults between 18-29 years of age are the most susceptible to suffering due to COVID-19 even though they have recovered from the infection. As shown in the Figure 3 and Figure 4, there were more than 14,000 cases just amongst 18-29 years of age making it the highest number of cases amongst all age groups and more than 50% of people between 18-29 years of age suffered from loneliness after the pandemic respectively. The probable reason behind this data is the physical maturity of the brain.

It has been found that the brain is underdeveloped at least till 25 years of age and is under the process of developing between 25-30 years of age. Different regions of the brain develop at different times. Significant changes in brain anatomy and activity take place during young adulthood, especially in prefrontal regions that are important for planning ahead, anticipating the future consequences of one’s decisions, controlling impulses, and comparing risk and reward. Hence, due to this if the virus enters the brain of an 18-29 old adult, then it can easily disrupt the barriers of the brain and dysfunction its cells because the brain is still developing, so the barriers of the brain cannot fully protect the brain since it is still achieving maturity. So, once the virus enters the brain. It can manipulate its functioning and its various regions and mechanisms due to which a person might feel lonely, depressed or anxious. Moreover, during depression, a person likes to detach themselves from their family and friends which increases their lonelinessThis proves the reason as to why adults between 18-29 years of age have increased rates of loneliness during the pandemic. (The New York Times, 2012)

Discussion

The pandemic indeed is very scary and dangerous for us in many ways, the majority of which causes harm to our physical and mental well-being. Everyone says that the virus has to mutate enough to die one day but little did they know that even though the virus is no longer alive, its effects are.

This study not only helped us gain information about the various mechanisms of COVID-19 virus into the brain but also about the effects of prevailing viruses, which we treat as normal, on the brain. It can be predicted that the mechanisms of the COVID-19 would be very similar to the prevailing viruses and hence, we can build upon those viruses to formulate various hypotheses for COVID-19 and how it can cause depression or anxiety. In some cases, depression or anxiety can worsen a person’s condition if he/she is infected with a virus. For example, in the case of HIV and Influenza, if a person is suffering from depression previously, and he/she is infected with HIV or Influenza, then depression would deteriorate a person’s condition even more because the immunity of the antibodies, that are supposed to defend the body by fighting against the virus, decreases. But, Influenza can infect the brain directly also through the inflammation of the neuronal tissues and thus enter the brain. In addition to Influenza, viruses like Hepatitis C and Herpes can damage the brain by disrupting the blood brain barrier, due to which the viruses come in contact with the Central Nervous System and cause dysfunctioning of the brain leading to depression, anxiety or other neurological disorders.

The main form of platform used for the above and following information was Google Scholar and all the statistics and data are collected after 2018. All the trials that are studied throughout this paper are mainly executed on humans or by using human kinematics in another organism.

Various studies and evidence have displayed the various mechanisms through which the COVID-19 virus could also enter the brain, disrupt its functioning and cause depression or anxiety. For instance- the virus could enter through the nose and come in contact with the olfactory nerves through which it can easily access the brain and its endothelial cells and damage the normal functioning of the brain. Another mechanism is through the blood brain barrier which contains the Angiotensin converting enzyme 2. COVID-19 virus has high affinity for this virus due to which it can easily bind with it and manipulate into a completely different function, thus interfering and disrupting the functioning of the brain.

Lastly, it is probable that there might be some correlation between the age of a person and number of cases of depression during the pandmeic. It has been found that the number of cases of COVID and loneliness between 18-29 years has tremendously increased during the pandemic. This can be related to the maturity of the brain. The brain does not develop until 30 years of age, until which the virus can easily attack the developing regions of the brain and enter. Like all the other viruses it would disrupt its functioning, thereby causing neurological disorders like depression which increases the rate of loneliness.

Clinicians and people must be cautious of this virus. Even though people have physically recovered from it, it would still impact their mental well-being because of which it is important to come in contact with the virus itself in the first place. There have been many waves of COVID-19 and it only gets worse with each succeeding wave, hence, it is important to keep in mind that irrespective of whether there is a wave or not, we should continue to maintain social distancing, wear masks, sanitize our hands from time to time. Everyone should realize the seriousness of this pandemic and protect themselves and others.

Since, it is probable that this virus has several entry points to the brain and can cause depression or anxiety, clinicians can start early therapeutic treatment for patients who have been diagnosed with COVID-19 so that depression is less likely to affect the patient seriously. Early treatment can increase patients’ awareness about what is likely to happen next and so they have prepared themselves for it, and also reduce the solemnity of depression. Moreover, to further investigate the route of COVID-19 into the brain, researchers could find or develop a chemical that could be injected in COVID-19 patients such that when these patients go for MRI or any other radioactive diagnosis, the route of the virus can be seen. The chemical that would be injected would be responsible for following the path of the virus which can be viewed under radioactive radiations. Lastly, researchers could find more similarities between the structures of other viruses and COVID-19 and if there are similarities in the various parts of the viruses like proteins or their internal matter, then there is a chance that the mechanisms of COVID-19 to enter the brain are similar to those of other viruses.

References

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Duarte, L. (2019, January 30). Herpes Simplex Virus Type 1 Infection of the Central Nervous System: Insights Into Proposed Interrelationships With Neurodegenerative Disorders. Frontiers. Retrieved August 11, 2022, from https://www.frontiersin.org/articles/10.3389/fncel.2019.00046/full

Government of Canada. (2021, May 31). Symptoms of anxiety and depression during the COVID-19 pandemic. Canada.ca. Retrieved August 9, 2022, from https://www.canada.ca/en/public-health/services/publications/diseases-conditions/sympto ms-anxiety-depression-covid-19-pandemic.html

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About the author

Sana Gupta

Sana is in her senior year of high school in Canada with a passion for Life Science. She takes keen interest in research opportunities to explore new dimensions in the medical field. Sana is also associated with many non-profit organizations and is the President of two clubs at her school. In her spare time, she enjoys dancing, listening to music, swimming, playing badminton, and making new friends.

The post Did the invasion of the brain by COVID-19 lead to increased cases of depression and anxiety? appeared first on Exploratio Journal.

Background

Dengue is a vector-borne, viral infection commonly found in tropical and sub-tropical areas of Central America, South America, Africa, Asia and Oceania [1]. It is caused by 4 types of viruses (DENV-1, DENV-2, DENV-3 and DENV-4), and is spread by the Aedes albopictus and Aedes aegypti species of mosquito, both of which thrive in areas containing standing water like puddles, pails, water tanks and tires [2]. The first dengue case was picked up at Pulau Pinang in 1901, and since then dengue has been a major problem affecting the Malaysian healthcare system and population as a whole for more than a century. By the 1960s, dengue had become endemic and in 1962 the first confirmed case of dengue-haemorrhagic fever was discovered in Pulau Pinang, eventually leading to the first epidemic outbreak in 1973 [3]. 

The current COVID-19 pandemic and the interventions put forth by the government in response have had their effects on recent dengue incidence rates, particularly in the year 2021. From the epidemiologic weeks 1 to 32 of 2021, a total of 16,565 dengue cases were reported, a drastic decrease of 47,423 (74.1%) from the same period in 2020 [4]. 

There have been a few different Movement Control Orders (MCO) implemented throughout the period of this study. The first MCO which spanned from 18^th March 2020 to 3^rd May 2020 [5] contained sanctions for all Malaysians traveling abroad, 14-day quarantines for those returning from overseas [6], general prohibition of mass movements and gatherings including religious, sports, social and cultural activities [6] and the entry of foreign persons into the country [6]. Some specific locations like Simpang Renggam in Johor were subjected to the Enhanced Movement Control Order (EMCO) for 14 days at a time during the period of the first MCO if a large cluster was detected within the area [7]. All residents living in such areas were forbidden from exiting their homes, outsiders were not allowed into the area and all roads into the area were blocked [7]. The Conditional Movement Control Order (CMCO) was implemented from 4^th May 2020 to 9^th June 2020, and had more relaxed regulations to stimulate the national economy [8]. Most activities and businesses were allowed to operate as long as social distancing was obeyed and interstate travel was not allowed except for work purposes [9]. The Recovery Movement Control Order (RMCO) was implemented from 10^th June 2020 to 31^st August 2020, and allowed interstate travel outside of areas under EMCO and certain religious activities at mosques [10]. Tourism businesses were allowed to open from 1^st July provided that the number of people in crowds was kept to 200-250 people and social distancing measures were obeyed [11]. Private pre-schools, kindergartens and day-care centres were also allowed to operate and many activities such as weddings, seminars, cinemas etc were allowed [12].

This paper examined and analysed the Dengue and COVID-19 incidents more extensively to investigate the reason and the details of this figure, as well as discusses the possible causes of such a drastic decrease.

Objectives

1. To show that the dengue incident rates in Malaysia had behaved unusually and dropped significantly in 2021.

2. To explore the potential contributing factors to the declined dengue incidence rates.

Methodology

This study was conducted during week 44 of 2021, so only data up to that point was used.

In order to illustrate that the dengue incidence rate of 2021 was significant, comparisons with the weekly trend of the Dengue incidence of the previous years was made. The number of reported dengue cases each week starting from week 1 of 2015 to week 44 of 2021 was recorded from public press releases posted on iDengue [13]. Any missing pieces of data were filled by dividing the cumulative number of cases over the number of weeks in the gaps. The epidemiologic weeks were plotted against the dengue incidence rates of each year. 

Correlations between COVID-19 incidence rates and dengue incidence rates were explored. Data on COVID-19 incidences per week from 2020 to week 44 of 2021 was retrieved from the Johns Hopkins University COVID-19 data repository. The maximum value from the COVID-19 case data set was divided by the maximum value from the dengue case data set to end up with a value of 44. All COVID-19 incidences per week were then divided by 44 to normalise the data for graph-plotting. The new COVID-19 data was plotted with the dengue incidence rate from 2020 to week 44 of 2021. The periods of the different Movement Control Orders (MCO) were labelled.

Dengue incidence rates during 2021 may have been low due to underreporting: fear of exposure to the COVID-19 virus and the overwhelmed healthcare system are possible reasons. To investigate this, the ratio of positive dengue tests to total number of dengue tests was analysed. This part of the study only covers the city of Ipoh. Dengue NS1 test data was retrieved from Pantai Hospital Ipoh. The data included the total number of negative and positive tests for dengue from 2015 to 2021 in the population of Ipoh. The total number of positive tests from each year was divided by the total number of tests carried out in the respective years to find the positivity rate. The percentage of positive tests for all years were then analysed.

The method of judgement was as follows: 

• If the percentage of positive tests was significantly lower than previous years, it was a sign of over testing
• If the percentage of positive tests was significantly higher than previous years, it was a sign of undertesting.

Next, the mortality rates of dengue throughout the years from 2018 to 2021 were analysed. The total number of deaths by dengue each studied year in Malaysia was recorded. The data was taken from the World Health Organizations Dengue Situation Update Number 634 [14]. The results between the years were then compared.

The idea of a correlation between vaccination and dengue incidence rates was also explored inside this study. The cumulative number of full vaccinations (2 doses) for each week from week 9 to week 44 of 2021 in Malaysia was recorded. The data was taken from the University of Oxford’s “Our World in Data” website [15]. Each successive value had been deducted by the previous value in order to find the number of complete vaccinations each week. To normalise the data for graph-plotting purposes, the greatest value of complete vaccinations in a week was divided by the greatest number of dengue cases in a week. The value that came out was 666. All values of complete vaccinations each week were then divided by 666 and plotted with the dengue incidence rate of the corresponding weeks.

Results

Graph 1 shows clearly that the dengue incidence rates of 2021 stray far from the trends of the previous years, behaving entirely differently. The most obvious observation is that the number of cases in 2021 appears to be significantly lower than the previous years, with very little deviance from an approximate range of 300-500 cases. Dengue cases usually rise in the months of June to August (week 23 to week 32), and November to February (week 43 to week 60), during the monsoon seasons, as it leaves many areas with standing water which mosquitoes can thrive in [16]. However, dengue incidences in 2021 do not seem to follow this trend which has been consistent for all past years, pointing towards 2021 being highly unusual.  

Graph 2 shows that dengue incidence rates had begun to decrease continuously around the same time as the beginning of the increase in COVID-19 cases. This could indicate a possible correlation between the two, but more investigation and study is required in order to make any conclusions. During March to June, there is a drop in dengue cases compared to earlier years that is not far from the previous years, but the MCOs contribute to the progressive drop of dengue incidence. Besides that, there does not appear to be much correlation between dengue incidence rates and COVID-19. 

There was a general decrease in percentage of positive tests from 2015 to 2018. The rates then increased in the years of 2019 and 2020 then decreased to 1.16% in 2021. It should be noted that the total number of tests done in 2021 was only 614, which is a 79.9% decrease from the average of the past years (2999.5). The low percentage of positive tests shows that undertesting was unlikely. This suggests that the low incidence of Dengue during 2021 is unlikely to cause by under detection.

From Table 2, it can be seen that the total number of cases recorded as well as the total number of recorded deaths were significantly lower than previous years. The total deaths in 2021 were 116 lower than 2020, a decrease of 87.2%. The mortality rate in deaths per 1000 people for 2021 was 0.0751%, drastic decrease from the yearly average of the previous years which was 0.158%. 

Mortality rates are a reliable way to visualise real dengue incidence rates, as deaths will always be reported. This method of analysis and the results decrease the possibility of underreporting being the cause of such low recorded incidence rates. This data points convincingly towards dengue cases dropping in 2021.

Graph 3 reflects no correlation between complete vaccinations and dengue, implying that the COVID-19 vaccinations have no effect on immunity against dengue. Although, majority of COVID-19 vaccinations were only completed near the end of the dengue data recorded, so claiming that there is completely no correlation would not be a reliable conclusion. They may have been a further decrease in dengue cases after week 44. Further study must be done.

Discussion

The data shows unequivocally that there is a significant drop in dengue incidence in the year 2021 compared to previous years, although this decrease seemed to begin around week 30 of 2020. A few speculations were made at an attempt to explain such a decrease. Firstly, one of the possible contributing factors to the decrease was that the cases may have been underreported as the healthcare system may have been overwhelmed, and people who potentially had dengue may have been afraid to seek treatment due to fear of contracting COVID-19 from healthcare facilities. Furthermore, the low dengue mortality rates during 2021 reflects the reduction of dengue incidence, as deaths are less likely to be unreported. The low positive rate of dengue tests and low dengue mortality rate convinced us that the drop of dengue cases in 2021 is unlikely to be caused by under reporting or under detection.

The next hypothesis is that under the movement control orders, people had more time to clean their homes and get rid of potential breeding grounds for vectors. Studies have shown that more frequent cleaning does lead to lower dengue incidence rates [17] [18]. This can only be ascertained with data from a vector (Aedes aegypti and Aedes albopictus) surveillance study.

The restriction on travel might also have affected dengue incidence rates heavily. Previous studies have shown that lesser travel is correlated with lower dengue incidence rates [19]. There have been many travel bans throughout the COVID-19 periods in Malaysia. Further study and analysis of the dengue incidence rates in these time periods is required to draw any conclusions. Unfortunately, month by month data of specific regions was not accessible.

Another potential contributing factor is the closing down of schools and workplaces because of the MCOs and transition to online schooling/working. In 2020, there were 5 million students among the population of 32.37 million in Malaysia [20]. The students were also equally distributed across every household across the nation, making the abstention from schools all the more significant. If this speculation is true, it would prove fruitful to look further into school and working environments as potential reservoirs of Aedes mosquitoes. 

The dengue test data collected had two limitations: patient specific data could not be collected and the data only represented the population of Perak. Different trends and conclusions could be made if all test types and a bigger part of the population was included.

The significant drop of dengue cases during the COVID-19 pandemic has generated a plethora of hypotheses that could be made for further investigation. This pandemic and the MCOs implemented in response have led to a natural experiment in which there was a nationwide change of lifestyle in Malaysia. Hence, a lot of opportunities for further research into the control of dengue and other infectious diseases have opened up. 

Bibliography 

[1] World Health Organization, “Dengue and Severe Dengue,” 10 January 2022. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue.

[2] Centers for Disease Control and Prevention, “How to Prevent the Spread of the Mosquito that Causes Dengue,” 25 January 2012. [Online]. Available: https://www.cdc.gov/dengue/resources/vectorcontrolsheetdengue.pdf. [Accessed 17 January 2022].

[3] A. Rudnick, “Dengue fever epidemiology in Malaysia, 1901-1980,” ResearchGate, vol. 1, pp. 1269-1272, 1986. 

[4] The Star, “Health Minister: Drastic drop in dengue cases in Malaysia this year,” The Star, 7 August 2021. [Online]. Available: https://www.thestar.com.my/news/nation/2021/08/07/health-minister-drastic-drop-in-dengue-cases-in-malaysia-this-year. [Accessed 17 January 2022].

[5] Bernama, “MCO extended another two weeks to May 12 – Muhyiddin,” Bernama, 23 April 2020. [Online]. Available: https://www.bernama.com/en/general/news.php?id=1835248. [Accessed 17 January 2022].

[6] New Straits Times, “Covid-19: Movement Control Order imposed with only essential sectors operating,” New Straits Times, 16 March 2020. [Online]. Available: https://www.nst.com.my/news/nation/2020/03/575177/covid-19-movement-control-order-imposed-only-essential-sectors-operating. [Accessed 17 January 2022].

[7] N. S. Sham, “COVID-19: PKPD dikuat kuasa di dua kawasan di Simpang Renggam,” Astro Awani, 26 March 2020. [Online]. Available: https://www.astroawani.com/berita-malaysia/covid19-pkpd-dikuat-kuasa-di-dua-kawasan-di-simpang-renggam-235454. [Accessed 17 January 2022].

[8] Bernama, “Perintah Kawalan Pergerakan bersyarat akan dilaksana – Muhyiddin,” Bernama, 1 May 2020. [Online]. Available: https://www.bernama.com/bm/news.php?id=1837419. [Accessed 29 January 2022].

[9] Bernama, “Essence of conditional Movement Control Order,” Bernama, 1 May 2020. [Online]. Available: https://web.archive.org/web/20200502065534/https://www.bernama.com/en/general/news.php?id=1837487. [Accessed 29 January 2022].

[10] The Sun, “Interstate travel among activities allowed from Wednesday – Muhyiddin,” The Sun , 7 June 2020. [Online]. Available: https://web.archive.org/web/20200608041411/https://www.thesundaily.my/home/interstate-travel-among-activities-allowed-from-wednesday-muhyiddin-HN2539119. [Accessed 29 January 2022].

[11] C. Loo, “More sectors to be reopened under RMCO from July 1,” The Sun, 26 June 2020. [Online]. Available: https://web.archive.org/web/20200626081436/https://www.thesundaily.my/covid-19/more-sectors-to-be-reopened-under-rmco-from-july-1-YD2630375. [Accessed 29 January 2022].

[12] The Sun, “Pre-schools, kindergartens to open on Wednesday, preparations underway for the little ones,” The Sun, 29 June 2020. [Online]. Available: https://web.archive.org/web/20200629010541/https://www.thesundaily.my/home/pre-schools-kindergartens-to-open-on-wednesday-preparations-underway-for-the-little-ones-AB2636604. [Accessed 29 January 2022].

[13] Menteri Kesihatan Malaysia, “Demam Denggi Dan Chikungkunya,” 2015-2021. [Online]. Available: https://www.moh.gov.my/index.php/database_stores/store_view/17?items=25&page=6. [Accessed 17 January 2022].

[14] World Health Organization, “Update on the Dengue situation in the Western Pacific Region,” World Health Organization, 2 December 2021. [Online]. Available: https://www.who.int/docs/default-source/wpro—documents/emergency/surveillance/dengue/dengue-20211202.pdf?sfvrsn=fc80101d_106. [Accessed 17 January 2022].

[15] E. M. L. R.-G. C. A. C. G. E. O.-O. J. H. B. M. D. B. a. M. R. Hannah Ritchie, “Our World in Data,” University of Oxford, 2021. [Online]. Available: https://ourworldindata.org/covid-vaccinations?country=MYS. [Accessed 17 January 2022].

[16] H.-Y. Y. et.al, “The effects of seasonal climate variability on dengue annual incidence in Hong Kong: A modelling study,” Scientific Reports, vol. 10, 2020. 

[17] P. R. B. S. Krishna Prasad Bhandari, “Application of GIS Modelling for Dengue Fever Prone Area Based on Socio-Cultural and Environmental Factors,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37, no. 8, 2008. 

[18] T. Chareonviriyaphap, “Larval habitats and distribution patterns of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse), in Thailand,” Southeast Asian J Trop Med Public Health, vol. 34, no. 3, pp. 529-535, 2003. 

[19] H. H. e. al, “Dengue viruses circulating in Indonesia: A systematic review and phylogenetic analysis of data from five decades,” Reviews in Medical Virology, vol. 29, no. 4, 2019. 

[20] The Straits Times, “All 5m students in Malaysia back to school for first time since Covid-19 outbreak,” The Straits Times, 5 April 2021. [Online]. Available: https://www.straitstimes.com/asia/se-asia/all-students-in-malaysia-back-to-school-for-first-time-since-covid-19-outbreak. [Accessed 30 January 2022].

About the author

Ze Shan Chan

The post The drastic drop in dengue cases in Malaysia during the COVID-19 pandemic appeared first on Exploratio Journal.

Abstract

In COVID-19, older adults have been disproportionately vulnerable to illness, death, and psychosocial repercussions. The proposed study aims to explore intersections between the COVID-19 pandemic and ageism among youth. Young adults recruited from the United States will read one of the three passages framing the pandemic in terms of its effects on the U.S. overall, the elderly of the U.S., or the young population of the U.S. and then complete critical measures of COVID-19 attitudes and behaviors. Participant ageist attitude will be included as a moderating variable and perception of the coronavirus as a personal threat will be included as a mediating variable. Conditional process models will be used to examine the direct and indirect effects of risk information on vaccine hesitancy and public health behaviors via the mechanisms of pandemic threat perception across levels of age bias. It is predicted that framing the pandemic in terms of its danger towards young adults will increase reported intentions to adhere to public health guidance via the mechanism of increased perceived threat, particularly for those high in ageism. Potential findings will have strong social, political, and cultural implications for establishing sustainable healthcare systems and intergenerational relationships.

Keywords: ageism, COVID-19, coronavirus, risk perception, public health, information framing

The COVID-19 pandemic has changed nearly all aspects of the daily lives of most individuals, yet those above 65 are disproportionately affected. Through August 2021, older adults have consistently been more prone to pandemic-related illness and death than younger people (Surveillances, V, 2020; Covid et al., 2020); they account for less than 20% of all COVID-19 cases but more than 70% of deaths worldwide (WHO, 2021). Older adults are vulnerable not only to negative physical effects of the coronavirus but also to long-term psychosocial repercussions due to social isolation. 

Along with the COVID-19 pandemic, there has been a parallel surge of ageism: from the media’s benevolent portrayal of the older population as a frail, vulnerable group, to hostile discrimination and abuse (Swift et al., 2021). The elderly face detrimental health impacts while having to cope with isolation, loneliness, anxiety, and depression (Banerjee et al., 2020; Bergman et al., 2020). The emergence of ageist attitudes and intergenerational divides have led to critical mental health ramifications on older adults. 

Additionally, non-compliance behaviors among adolescents exacerbate the negative impacts on public health. Adolescents are commonly identified with more non-compliance with preventive policies, especially with social distancing, than other age groups (Harris et al., 2021; Nivette et al., 2021; Roy-Chowdhury et al., 2020). Young adults are less likely to display severe COVID-19 symptoms and often consider themselves as immune and invulnerable to the virus (Ayalon et al., 2021; Surveillance, V, 2020). Therefore, as young adults frequently engage in social activities, those infected have high potential of transmitting the virus to others, which ultimately puts the elderly at risk (Andrews et al., 2020).  Recently, the Delta variant has become a pressing concern, as infection rate and hospitalization increase dramatically among the younger population (NBC, 2021). Internationally, health officials have been stressing the importance of adhering to health and safety guidelines. However, compliance to recommended health behaviors may be contingent on one’s attitude towards older adults. Accordingly, the current study sought to investigate the conditional indirect effects of reminders of coronavirus risk on adherence to pandemic-related public health behaviors (i.e., vaccine hesitancy, staying home, mask-wearing, physical distancing) as moderated by young adults’ ageism and mediated by perceptions of coronavirus risk.  

Participant Ageism

Prior research has demonstrated that ageist attitudes are often strong predictors of adolescent risk-taking behaviors. Young individuals with higher age bias also tend to report higher levels of risk behaviors (e.g., drug use, alcohol use, sexual behaviors) in daily life (Popham, et. al., 2011; Kennison, et. al., 2012). This is consistent with the terror management theory, which suggests that adolescents with strong ageism may attempt to dissociate themselves from their future older selves by seeking experiences that stimulate sensations of youth, strength, and invulnerability (Popham, et. al., 2011). Thus, the present study hypothesizes that youth with higher levels of ageism will also be more likely to engage in risky behaviors and subsequently report lower levels of engagement in COVID-19 protective behaviors. Although little research has been conducted on the specific effects of ageism on COVID-19-related health behaviors in youth, studies indicate that ageist attitudes may be a predictor of behavior change during the pandemic (Vale, 2020). 

Risk Information and Risk Perceptions

Risk information indirectly results in attitude and behavior modifications by increasing risk perception and altering emotional responses. Risk information, in the form of novel risk information or reminders of previously-known risks, is positively associated with levels of risk perceptions over time (Gerrard et al., 1999). Numerous studies have found that youth behaviors in risk-taking contexts, such as smoking and sexual behaviors, are responsive to risk information, especially when that information is personally relevant (Dupas, 2011; Gilbert et al., 2017). Moreover, authentic risk perceptions signal the need for preventive measures, and thus influence people’s decision-making and catalyze health behavior modifications in daily life and clinical settings (Edwards, et. al., 2001; Schmälzle, et. al., 2017). Amid past pandemics, such as the MERS outbreak in South Korea and the H1N1 influenza in Britain, increase in the public’s perceived susceptibility to the virus predicted increased preventive behaviors (Bish et al., 2010; Choi et al., 2018). In the COVID-19 pandemic, those perceiving greater risks also tend to report higher compliance with recommended health and safety precautions (de Bruin et al., 2020). Therefore, the present study intends to investigate the link between risk information, risk perceptions, and behaviors in the framework of the coronavirus pandemic.

Research Hypothesis: We hypothesize a conditional indirect effect of exposure to information about the increasing risk of the coronavirus for young adults on COVID-19 health attitudes and behaviors (i.e., vaccine hesitancy, staying home, mask-wearing, physical distancing) that will be moderated by participant ageism and mediated via the mechanism of increased perceptions of personal threat. We predict that exposure to information about the increasing risk of the coronavirus for young adults will cause participants to report increased coronavirus threat perception, which results in reduced vaccine hesitancy and increased health behaviors. 

Method

Participants

Participants will be recruited from undergraduate university subject pools from various universities in different areas of the United States. Participants will report their age, gender, race, and political orientation.

Measures

Ageism. Ageism will be measured on the 13-item Ambivalent Ageism Scale (AAS) validated (e.g., Cary et al., 2017) and used (e.g., Vale et al., 2020) by numerous peer-reviewed research studies. An example item is, “It is good to tell old people that they are too old to do certain things; otherwise they might get their feelings hurt when they eventually fail.” Participants will respond to each item on a 7-point Likert scale from 1 – “Strongly Disagree” to 7 – “Strongly Agree,” resulting in a range of possible values on the composite variable from 13 to 91. Higher value indicates greater level of ageism. See Appendix A.

Risk-information. Risk-information will be a manipulated (experimental) independent variable. Participants will be randomly assigned to three condition groups. In each condition, participants will be provided with information that emphasize the continued pandemic-related risks, its widespread impacts, and the importance of vaccination and safety measures. Participants in the threat to older population condition (Condition 2) will read a passage of information emphasizing the continued vulnerability and risks for the older population. Participants in the threat to younger population condition (Condition 3) will read a passage emphasizing the increased vulnerability and risks for the younger population. More details are reported below under “Procedure” and full text is available in Appendix B.

COVID-19 threat perceptions. COVID-19 threat perceptions will be assessed using the 7-item Fear of Coronavirus-19 Scale (FCV-19S) (Ahorsu et al., 2020). The reliability of the scale is validated in different populations (e.g., Perz et al., 2020). An example item is, “I am afraid of losing my life because of coronavirus-19.” Participants will respond to each item on a 5-point Likert scale from 1 – “Strongly Disagree” to 5 – “Strongly Agree,” resulting in a possible range of values from 7 to 35. Higher value indicates greater pandemic-related threat perceptions. See Appendix C.

COVID-19 health attitude and behaviors. Attitudes toward COVID-19 vaccinations will be assessed using three items, “All universities should mandate coronavirus vaccines for all students who are medically able to be vaccinated”, “Employers should mandate vaccinations for all workers who are medically able to be vaccinated, regardless of how otherwise young and healthy those workers are”, and “Everyone who is medically able to receive a vaccine should choose to be vaccinated.” Participants will respond to each item on a 7-point Likert scale from 1 – ”Strongly disagree” to 5 – “Strongly agree.” COVID-19 health compliance behaviors will be evaluated using a 3-items assessing how the framing manipulation has prospectively changed their likelihood to engage in certain behaviors, selected from public health recommendations by the World Health Organization (WHO, 2021), including: (i) stay at home, (ii) adhere to physical distancing (1.5-2m), and (iii) wear a face mask. Participants will indicate on a 7-point Likert scale, from 1 – “I will be much less likely to do this” to 7 – “I will be much more likely to do this,” resulting in a range of values from 3 to 21. 

Procedure

Participants will complete an informed consent document and are randomly assigned to read one of the three passages containing information on the number of cases, deaths, and hospitalization among (1) the U.S. population, (2) older adults, and (3) younger adults. All three passages will contain information regarding the rise in cases since the summer of 2021, the threat of the more contagious Delta variant, and the effectiveness of vaccination and preventive behaviors such as social-distancing and mask wearing. Then, participants will report to measures of COVID-19 threat perception, health attitudes, and health behaviors. Finally, participants will complete measures of demographic and personal characteristics (i.e., age, gender, and race) and read a debriefing statement regarding the purpose of the study.

Results

Conditional direct and indirect effects will be examined using PROCESS Model 8 (Hayes, 2013) with risk information as the primary independent variable (x); age-related bias as the moderator (w); perceived coronavirus threat as a mediator (m). Vaccine hesitancy was as the dependent variable (y1) in the first analysis and reported adherence to public health behaviors as the dependent variable (y2) in the second analysis. All indirect effects were computed for each of 10,000 bootstrapped samples and were considered significant if their 95% confidence intervals did not include zero (these bootstrapped effects do not produce exact p values). Non-dichotomous variables were mean centered. The conceptual models are illustrated in Figures 1 and 2. 

Discussion

The current study was designed to investigate whether ageism and framing the impact of the coronavirus pandemic in terms of the increased risk of young adults, rather than of the general population or the older population, would predict greater coronavirus threat perception, reduced vaccine hesitancy, and increased health behaviors. We expect that participants with high age bias will report less compliance behaviors and that participants receiving information regarding young adults will also report less compliance behaviors. Overall, we expect to be able to conclude that age bias and framing of risk information have a strong influence on public health outcomes, such as vaccine hesitancy and compliance to safety guidelines.

Implications

The results of this study (if consistent with the hypothesis) will have significant implications for public health and social policy. Ageism in young adults promotes reckless behaviors that jeopardize lives and entire healthcare systems. Reminders of pandemic-related risk that are age-specific and relevant effectively induce attitude modifications and reduce risk behaviors in youth. This accentuates the need for stronger social efforts, including mitigation and information campaigns targeted at young adults (Kolbe, et. al., 1993). In addition, fostering a culture that promotes the value of the lives of older people and reduces the youth obsession that feeds into anti-elderly bias is paramount, especially in a rapidly aging society (Ng & S. H., 1998; North, et. al., 2012; Meisner & B. A., 2021). 

Limitations

The study relies on participants’ self-reported response as a measurement of COVID-19 vaccine attitudes and health compliance behaviors. Therefore, the conclusions drawn from this study may be limited by the use of self-reported responses which might not accurately reflect actual behaviors, as participants may be unable or unwilling to report accurate estimates. Additionally, one’s vaccine hesitancy also varies across time, place, and vaccines, yet the assessment of vaccine hesitancy in this study lacks specific vaccine context, thus the results might not generalize to other countries or populations. 

Future Directions 

The proposed experiment assesses the effects of risk information on COVID-19 health attitudes and behaviors in the short term. Future research may examine whether the observed effects generalize across contexts and to what extent the effects are durable. As research demonstrates that manipulation of information in clinical settings affects patients’ decision-making and health outcomes (Edwards et al., 2001), future research could investigate the effects of framing pandemic-related risk information on public attitudes during the coronavirus pandemic. Future research may also explore the relationship between ageism, risk information, and response (i.e., attitudes and behaviors) in different age groups. 

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About the author

Vanessa Yu

Vanessa is an 11th grader at the United World College of South East Asia in Singapore. She is interested in social psychology, mathematics, and physics and also enjoys making and appreciating art.

The post Can Framing the Pandemic as Dangerous to the Young Reduce Non-Compliance Behaviors Among Young Adults in the COVID-19 Pandemic? An Examination of Age-Related Bias appeared first on Exploratio Journal.

Abstract 

How did the lockdown and social media use affect the body image, eating habits, self-esteem, and mental health of adolescent girls? This paper looks at the impacts the lockdown had on social media usage, body image, and eating/exercise habits. Research shows that adolescent girls found themselves with lower self-esteem, depression, anxiety, and disordered eating habits during and after the pandemic. These changes combined with increased social media use that typically promotes unrealistic body expectations and unhealthy eating and exercise habits lead to an overall decrease in young girls’ self-esteem and body image. Based on these findings, we can formulate ways to help everyone with these unhealthy habits if we ever go into lockdown again in the future and even in our normal daily lives as well. 

On March 12th of last year, schools began shutting down saying that they would be closed for the next two weeks out of precaution against COVID-19. Those two weeks quickly turned into three, and then four, and eventually they turned into months. Those months in quarantine were incredibly tough on everyone. Because everything closed down and everyone was stuck inside, peoples’ schedules changed drastically. For Katy, a freshman in high school on the dance team, covid was incredibly difficult. Halfway through her freshman year, her school shut down because of the pandemic. She was forced to do both school online as well as her dance classes. Because she spent most of her time at home, she found herself looking at social media much more often than before. She also began feeling insecure about falling behind on her goals for dance because of the pandemic, and as a result, she followed lots of fitness and dance influencers to try to learn and improve from them. Unfortunately, she only felt more insecure about how she looked and her abilities because those influencers did not post realistic habits. Those influencers she followed promoted unhealthy eating and exercise habits to “be a better dancer”, which led Katy to adopt those habits. Katy continued to feel bad about herself as she decreased the amount she ate and increased the amount she exercised. Katy now struggles with worsened body image and disordered eating habits that negatively impact her physical and mental health. 

How did the pandemic impact our lives? During the COVID-19 lockdown, a survey of U.S. social media users found that 29.7 percent of respondents were using social media for 1-2 hours additional hours per day. This significant increase was due to the extra time spent at home during the lockdown. Everyone’s normal lives were disrupted. Students were forced to do virtual school, many adults were forced to work from home, and many others even lost their jobs due to the pandemic. The uncertainty and confusion of the pandemic led to significant amounts of stress, anxiety, and depression for everyone. 

According to researchers at the University of Michigan conducting a survey from 977 parents of teens, they reported that 1 in 3 girls (aged 13-19) experienced new or worsening anxiety. Additionally, more parents of adolescent girls observed increased symptoms of depression, anxiety, and worry in their kids than parents of adolescent boys. The increase of social media usage also contributed to comparison and unrealistic expectations that worsened people’s self-esteem and body image, which often led to an increase in disordered eating and exercise habits for many. 

Specifically for adolescent girls, the comparison between before and after the pandemic shows the negative impact it had on adolescent girls’ mental health. According to the American Academy of Child and Adolescent Psychiatry in 2018, as many as 10 in 100 adolescent girls had an eating disorder. According to the Anxiety and Depression Association of America, around 25% of teenage girls displayed depressive symptoms. Of the total teenage population taking antidepressants, nearly two-thirds of them are adolescent girls. Therefore, a significant percentage of adolescent girls did have mental health issues and eating disorders in 2018, before the pandemic. Later studies prove that these issues worsened for many with the pandemic. Because their schedules were disrupted and they had more time at home, many adolescent girls adopted or increased their social media usage. Due to the pandemic lockdown and increased social media usage, the eating and exercise habits and in adolescent girls worsened. We can see how worsened eating and exercise habits would impact the mental health and self-esteem of adolescent girls. 

How social media usage affects the body image of young girls?

A research review that examined social media and body image concerns examined that increased social media use leads to more negative body image in young men and women. It specifically shows that appearance comparisons are the direct and important link between the usage of social media and negative body image. 

A research report examined the data from different studies. It showed the correlation between the usage of Facebook and the internalization of thin-ideal media. The study also reports that spending more time on Facebook/Myspace is associated with higher levels of body dissatisfaction and thin-idealization in adolescent girls (ages 14-22). Finally, it showed that elevated appearance exposure, such as posting or viewing, on Facebook was associated with more body dissatisfaction and thin-idealization amongst female high school students as well. 

From this data, we can conclude that heavier usage and interaction on social media such as Facebook is linked to much higher body dissatisfaction. Due to the increase of global social media and internet usage during the COVID-19 pandemic lockdown, we can see how the increase negatively impacted young girls. On many social media platforms, many users tend to edit, photoshop, or alter their photos in order to achieve an “ideal photo”. This is because of the abundant access to photoshop and editing apps online. This allows influencers and other social media public figures to alter their photos to make themselves look more “thin”, “attractive”, or “likable”. Too often, young girls compare themselves to unrealistic social media photos that they are convinced are the norm, and as a result, they feel depressed and dissatisfied when they don’t fit into those norms. Additionally, because young girls are at such a vulnerable age where societal influences can have a strong impact on them for life, these factors are already leaving a destructive impact that will last a long time. 

A study that surveyed 144 girls between the ages of 14-18 years old in the Netherlands and the impact normal and retouched Instagram photos had on their body image proved that the manipulated Instagram photos directly led to lower body image, specifically in those with higher social comparison tendencies. The manipulated photos were also rated more positively than the normal ones. 

This study randomly exposed participants to either 10 original Instagram photos or 10 manipulated photos. Afterward, the participants completed a survey containing various questions regarding their own self-esteem and their opinions about the photos they saw. Regarding the participants’ self-esteem, the study showed that on a scale from 0-6, girls with higher social comparison tendency that were shown manipulated photos had the lowest body image of 3.7. The data shows that 63 participants showed a lower tendency to make social comparisons, and 81 showed a higher tendency to make social comparisons. The participants also said in the survey that they could identify the manipulated photos better than they could identify the original photos. 

From this data, we can conclude that exposure to digitally manipulated photos in adolescent girls, especially those with higher social comparison tendencies, will lead to lower body image in the girls. This is because the participants rate the manipulated photos in the study “more desirable” than the natural photos. Additionally, the data also proves that the majority of the participants could tell that the manipulated photos were manipulated. This shows that the participants are aware of the photos manipulation, yet they still have the tendency to compare themselves to the fake photos. Because manipulated photos are so commonly seen in social media, and the media in general, those with higher social comparison tendencies naturally continue to compare themselves and alter their own images in order to fit into what is deemed “desirable”. As a result, the girls are left stuck with lower body image, which has led and will lead to unhealthy responses, such as depression, over-exercising, and/or disordered eating. 

How did the pandemic impact the eating and exercise habits of adolescent girls?

A study done in Australia compared those with eating disorders to the general population regarding their eating and exercise habits during the pandemic. This study launched a survey on April 1, 2020, in Australia to 5,469 participants, 180 of those self-reporting previous eating disorder history, to determine the changes in eating and exercise behaviors in people during the COVID-19 pandemic lockdown. Between both groups, the groups with the pre-existing eating disorders and the group without pre-existing eating disorders, both groups showed an increase in disordered eating during the lockdown period. The eating disorder group showed an increase in pre-existing behaviors, and the regular group developed disordered eating habits as well. The data is divided into three main groups, the general population, eating disorder, and the anorexia nervosa subgroup.

In total, all three categories reported an overall increase in restricting habits amongst those with previous eating disorders and a slight increase in those without them. For binging, the eating disorder group and general population group both showed an overall increase in bingeing of 35%, while the anorexia nervosa subgroup reported 21% of the participants with an increase in binging habits. Exercise trends are similar for all three groups, there is a significant percentage of the population showing an increase, no difference, and less exercise. For the eating habits reports, none of the categories showed a significant percentage of the population reporting a decrease (<14%), but all of the exercising categories did. 

This study proves that due to the lockdown, those with previous eating disorders increased their disordered eating habits. Additionally, those without previous eating disorders developed disordered eating habits. There could be many reasons for this. For many, the additional time spent in quarantine left them with the time to think about eating/restricting and act on their thoughts. Another factor was the unstable and rapid changes throughout the pandemic. No one knew what the world was going to look like in a few weeks, let alone a few months, and this uncertainty led to lots of stress for many. Another factor of the pandemic was the financial and emotional uncertainty. Many feared losing jobs, housing, food, schooling, and loved ones due to COVID-19 as well. This additional stress may have led to bad coping mechanisms, like obsessing over eating, and exercise habits for the need to feel control over their lives. 

A study conducted in the UK explored how the COVID-19 lockdown influences the eating habits and body image of adolescent girls. 

There are five different categories for the survey data, and participants would rank how well they thought they fit with the categories on a scale of “strongly disagree” to “strongly agree”. The participants were divided into three groups for each category, no (previous) diagnosis, other diagnoses, and eating disorder diagnosis. The first set of data from the survey responses is regarding “difficulty to regulate/control eating”. According to the data, a majority of all three groups said that they agree with the statement. The second category was “more preoccupied with food and eating”. The majority of all three groups also said that they agree with this statement. The next two categories are “exercising more” and “thinking about exercise more”. A significant percentage of each of the groups said that they agree and strongly agree with both statements. The last category is “more concerned about the way I look”. A significant percentage of all three groups said they agree with this statement as well. 

Overall, the data shows that the participants with an alternate diagnosis or an eating disorder had greater difficulty with their relationships with eating, exercise, and body image. At the same time, the people that had no previous mental or ED-related diagnosis reported an increase in negative relationships with eating and exercise during the lockdown. This proves that the lockdown did have a directly negative effect on many different types of people, those with and without a mental diagnosis. While everyone was on lockdown stuck in their homes, many people had lots of spare time on their hands. Specifically for those with an eating disorder/other mental diagnoses, they obsessed over food, exercise, and body image more than they would have when they were busy with their normal lives. Those with pre-existing eating disorders dealt with increased disordered eating habits, more so than the general population and even those with other mental disorders. 

The rapid and unreliable changes in our lifestyles also contributed to increased anxiety amongst many. The fear of losing one’s job, loved ones, or one’s own life during the lockdown was incredibly stressful, which oftentimes led to harmful coping habits like disordered eating or over-exercising. 

Additionally, during the lockdown, there was an overall trend of increased social media usage. This is understandable, given that the main form of communication while in lockdown was through technology/social media. However, the concentrated exposure to misleading/glamorized images may have led people to partake in over-exercising and disordered eating habits which overall, may have worsened their mental and physical health. 

Moving forward, there are many things we can do to help those struggling with disordered eating, body image, and exercise issues if we go back into lockdown again, or even if we don’t. We can help make social media a more safe and real place for everyone. Reminding ourselves that we should not compare ourselves to social media photos is really important. The reality is, we don’t know where these photos came from, they could be photoshopped, edited, and/or posed to look “better”. Additionally, choosing to follow influencers and content creators on social media that don’t promote harmful eating and exercise habits, and blocking/reporting those that do is a great and simple way to prevent being exposed to these harmful ideas. 

If we ever go back into a lockdown again, we cannot control the outside world, but we can control how it impacts us. Trying to cope with tough events using healthier coping mechanisms, such as exercising normally or going for walks outside, is a great way to deal with any stress or anxiety. It is important to look out for ourselves and others during these difficult times, and we must remind ourselves that it is okay to not be functioning at our best because these are not normal circumstances. 

There are many factors that may have contributed to adolescent girls developing/worsening their habits during the lockdown. These factors include dramatic lifestyle changes, stress, previous eating disorders, and mental health issues, excessive social media usage, and additional time spent being stuck inside. All of these factors may have contributed to low self-esteem, body image, eating disorders, and poor mental health in adolescent girls, and many are still dealing with the lasting effects of these issues today. 

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Phillipou, A., Meyer, D., Neill, E., Tan, E. J., Toh, W. L., Rheenen, T. E. V., & Rossell, S. L. (2020, June 1). Eating and exercise behaviors in eating disorders and the general population during THE COVID‐19 pandemic in Australia: Initial results from the COLLATE project. Wiley Online Library. https://onlinelibrary.wiley.com/doi/full/10.1002/eat.23317.   

April 20, 2021 | P. (2021, April 28). How to spot teen depression during covid. Scripps Health. https://www.scripps.org/news_items/5319-teen-depression-during-covid-19-pandemic-what-to-look-for.   

Robertson, M., Duffy, F., Newman, E., Prieto Bravo, C., Ates, H. H., & Sharpe, H. (2021). Exploring changes in body image, eating and exercise during the COVID-19 lockdown: A UK survey. Appetite, 159, 105062. https://doi.org/10.1016/j.appet.2020.105062 

About the author

Grace Julian

Grace is currently a Junior at the Episcopal Academy. She enjoys learning about history, psychology, and political science, helps run her school’s political blog, and is a leader of the community service executive board. In her free time, she enjoys playing water polo and reading.

The post Examining the effects of the COVID-19 lockdown on adolescent girls appeared first on Exploratio Journal.

Introduction

This paper will examine and evaluate the current condition of the Indian economy the problems encountered in the last 5 years, including the Covid-19 crisis 2020-21, demonetization in 2016 and the introduction of the Good and Service Tax (GST) in 2017. In 2014, the people of India, evidently dissatisfied with ten years of the Indian National Congress in power, elected the BJP to the Lok Sabha (House of Commons). Boasting a landslide victory, Mr. Narendra Modi became the 14th Prime minister of independent India. Although the pandemic opened the floodgates to an economic turmoil that was long in the making, the abysmal mismanagement and incompetence of the government have made this more complicated. 

The paper will give you a detailed account of the policies and problems with the Indian economy since June 2016. The paper will focus on the impact of demonetization, the introduction of the Goods and Service Tax and the devastation caused by the Covid-19 pandemic. The paper shall also give attention to the impact on investment in the country (namely FII, Institutional and Retail) in similar context. 

Looking at the historical developments, after the decline in economic growth in the wake of the global financial crisis in 2008, Indian economy started its recovery in March 2013, more than a year before the Modi government came to power. According to RBI’s annual report for FY21 released on May 27, the recovery cycle lasted until September 2016. After it became a secular decline in growth since the third quarter of 2016-17. While there is no official explanation for the deceleration in growth, many economists believe Modi government’s sudden decision to demonetize 86 per cent of India’s currency on November 8, 2016, was the trigger that set the economy on a downward spiral.

The demonetization was followed by the hurried implementation of a poorly designed GST. As the combined effect of demonetization and GST spread through the economy that was burdened with massive bad loans in the banking system, the GDP growth rate steadily fell from 8 per cent in FY 2017 to 4 per cent in FY 2020, before Covid further damaged growth. The result of the GST was a drop in per capita GDP, rise in unemployment, increase in poverty and a diminution in the size of India’s middle class. India’s per capita GDP has dropped to Rs 99,700; this was the level in 2016-17. On the unemployment front, India has performed even worse: against the norm of an unemployment rate of 2 to 3 per cent, the new norm in the unemployment rate was 6 to 7 per cent in the years leading up to Covid-19. The pandemic has made matters worse.

The pandemic had a further negative effect on economic development, according to the latest CMIE data, the unemployment rate is 14.5 per cent as of the end of May 2021 and the rural unemployment rate has crossed 7 per cent. This comes at a time when the labor force participation rate, which maps the proportion of people who are looking for a job, is falling. As growth has slowed, poverty has increased: according to Azim Premji University’s ‘State of Working India’ report, 2020, almost 230 million people have fallen back into poverty, against 270 million lifted out of poverty between 2000 and 2016. That’s not all. According to a new Pew Research Centre Report published before the second Covid wave surge, the Indian middle class has shrunk by almost 32 million people in 2020.

The consistent policy failures in demonetization and GST tax, exacerbated by the Covid-19 pandemic has meant a loss of jobs, lower wages, loss of savings, low consumer spending, lower business investment and lost capital. But these policies failures also mean slower growth prospects, rising poverty and shrinking middle class. The main indicators of a sound economy are weak and continued policy problems will have a further adverse impact on livelihoods. Surprisingly, while we are in the midst of a pandemic and talking mainly about how soon we will come out of it, there is little anxiety and concern about the economy’s huge underperformance, because of bad policies and mismanagement. [1]

Impact of Demonetization on the Indian Economy

Demonetization was a policy failure which has damaged the Indian economy. Demonetization is the act of stripping a currency unit of its status as legal tender. It occurs whenever there is a change of national currency in circulation from one unit to another. Normally the current form or forms of money are pulled from circulation and retired, often to be replaced with new notes or coins which are regularly used. Sometimes, a country completely replaces the old currency with new currency. Demonetization is a drastic intervention into the economy that involves removing the legal tender status of a currency. Demonetization has been used as a tool to stabilize the currency and fight inflation, to facilitate trade and access to markets, and to push informal economic activity into more transparency and away from black and gray markets; but that was not necessarily the case in India. 

Demonetization across the globe

Demonetization has traditionally been used as a way to solve confidence problems about the currency. In the USA, the Coinage Act of 1873 demonetized silver as its legal tender, to fully adopt the gold standard. This move was made to ward off any disruptive inflation as significant as new silver deposits that were discovered in Western America. The circulation of various coins, including two-cent piece, three-cent piece, and half-dime was suspended. The removal of silver from being circulated in the economy led to the contraction of the money supply. In turn, it contributed to a downturn throughout the country. The Bland-Allison Act remonetized silver as a legal tender in 1878, to put an end to recession and political stress from farmers and silver miners. More recently, the government of Zimbabwe demonetized its dollar in 2015. It was a measure to fight the hyperinflation recorded at 231,000,000 per cent. It removed the Zimbabwean dollar from the country’s financial system. It fixed the Botswana pula, the U.S. dollar, and the South African rand as the country’s legal tender to stabilize the economy.[3] [4]

Demonetization in India

Demonetization was tried as a tool to modernize a developing economy that is cash-dependent and to fight crime and corruption involving counterfeiting and tax evasion. Accordingly, the Indian government demonetized the Rs 500 and Rs 1000 face-valued currency notes in 2016, the two most prominent denominations in its currency system. These notes accounted for 86% of the country’s circulating cash. With no indications, India’s Prime Minister Narendra Modi announced to the country’s citizens on 8th of November 2016, these notes would have no value with immediate effect. As an alternative arrangement, citizens could deposit or exchange these old notes for newly introduced Rs 2000 and Rs 500 bills by the end of the year.

Theoretically, the Indian demonetization solved a serious problem. India suffers from a large informal (underground) economy, which is estimated at 25% to 40% of the nation’s GDP. Individuals working informally pay little or no taxes because their incomes are unreported. Regardless of whether the underground activities are legal (cleaning, gardening, or selling groceries) or illegal (unlawful drug dealing, gambling, terrorism, arms trafficking, corruption, or money laundering), they enable tax avoidance, evasion, and fraud, which reduces government revenues. As a result, community betterment projects, such as infrastructure repairs and improvements, may go unfunded, and for those projects that are funded, underground tax evaders are able to freeload off the nation’s taxpayer base. This fact helps to explain why initial reactions by India’s taxpayers were often ones of pride because the government seemed to be striking openly at those who flaunted their wealth without paying nearly their share of taxes.

Stopping or slowing the growth of India’s illegal underground activities was a desired goal because they undermine the nation’s moral fabric, but as a matter of social fairness, the government’s intent was broader, aiming to expose both the legal and illegal layers of its informal economy. Currently, fewer than 4% (and perhaps as low as 1%) of India’s population pays taxes. By making the ₹500 and ₹1,000 notes illegal, Modi hoped India’s thriving informal economy would be legitimized. Individuals who exchanged excessive amounts of old notes would be required to explain their sources. Old currency notes that were held beyond the deadline would lose their value, and those who continued to use, transfer, receive, or even hold the old banknotes would be subject to fines of either ₹10,000 or five times the value of the banknotes. By promoting the use of checks, credit cards, and other forms of electronic payments, demonetization might make underground transactions more difficult to hide. And, if demonetization was able to broaden the nation’s tax base and increase government revenues, it held the potential to reduce Indian tax rates, which could increase domestic investments and spur meaningful growth.[2]

Demonetization also tackled an economic distortion that currently favors India’s low-productivity, informal sector. High-productivity sectors, such as those in which multinational firms and export-oriented domestic companies compete, are visible and, therefore, less able to escape taxes. By bringing the informal economy above ground and taxing it at reasonable rates, demonetization could bring symmetry to India’s tax-based incentive structure. As a result, relative underground returns should fall, supplying more capital to relatively high-productivity sectors and, thereby, stimulating the nation’s growth and development.

The rationale behind this herculean task was to crackdown on the black money in the shadow economy of India. The government also hoped demonetization would be an effective measure to combat counterfeit notes, and cash being used to fund illicit activities such as terrorism and drug trafficking. An adjunct motive the government had was to push the economy towards a cashless one. It was thought that the cash crunch caused due to this legislation would encourage individuals to adopt cashless transactions using mechanisms such as credit cards and online banking.

Provisions were made by the government for people to deposit cash they had in their homes into their bank accounts until the end of the year. Banks would only notify tax authorities when deposits above Rs 250,000 ($3,800) were made. This would aid the tax department in tracing individuals guilty of tax evasion due to hoarding of black money. People were also allowed to exchange their old notes for new ones at bank offices in the country. They could continue to withdraw cash of other denominations at ATMs. However, a cap of Rs 10,000 ($150) per day was placed in order to ensure that there was sufficient cash for everybody.

Impact of COVID-19 on the Indian Economy

The Indian Economy was significantly damaged by the Covid-19 pandemic. Losses from organized sectors amounted to an estimated nine trillion rupees in late March, projected to increase with the prolonging of the lockdown. Unsurprisingly, the most affected industries included services and manufacturing, specifically travel & tourism, financial services, mining and construction, with declining rates of up to 23 percent between April and June 2020. Towards the end of 2020, however, India saw some semblance of recovery across certain sectors. This was a result of easing restrictions, controlled infection rates and the festive season between October and November 2020. However, this did not last long. 

Economic activity started to decline again from March 2021, as the country faced its second wave of the pandemic and a harsh lockdown. As a result, GDP forecasts were expected to fall, putting losses at over 38 billion U.S. dollars if local lockdowns continued till June 2021. Unprecedented numbers in terms of infections and deaths recorded across the country led to another set of lockdowns in some parts, burdening the healthcare system in the midst of government controversy. International aid in the form of oxygen cylinders, PPE kits, ventilators along with funding was being sent from various countries to what looked like a dire situation.

Overall, from April to June 2020, India’s GDP dropped by a massive 24.4%. According to the latest national income estimates, in the second quarter of the 2020-’21 financial year (July-September 2020), the economy contracted by a further 7.4%, with the third and fourth quarters (October 2020-March 2021) seeing only a weak recovery, with GDP rising 0.5% and 1.6%, respectively. This means that overall rate of contraction in India was (in real terms, adjusted for inflation) 7.3% for the whole 2020-21 financial year. While economies worldwide have been hit hard, India has suffered one of the largest contractions. During the 2020-’21 financial year, the rate of decline in GDP for the world was 3.3% and 2.2% for emerging market and developing economies. Table 1 summarizes macroeconomic indicators for India, along with a reference group of comparable countries and the world. The fact that India’s growth rate in 2019 was among the highest makes the drop due to Covid-19 even more noticeable.[10]

In the post-Independence period, India’s national income has declined only four times before 2020 – in 1958, 1966, 1973 and 1980 – with the largest drop being in 1980 (5.2%). This means that 2020-’21 is the worst year in terms of economic contraction in the country’s history and much worse than the overall contraction in the world. The contraction is solely responsible for reversing the trend of global inequality, which has now started to rise after three decades (Deaton, 2021; Ferreira, 2021).

Macroeconomic indicators:

Despite India being ahead of most countries in being able to implement work-from-home measures, specifically in white collar work, jobs and earning deficits, along with instability in prices was expected. The months of the lockdown resulted in the free fall of employment, which slowly stabilized after the economy steadied in most parts of the country. Segments including consumer retail expected to see sharp falls ranging between three and 23 percent depending on the market. For the big players across segments, this meant operating at less than full capacity; for small businesses, however, it depended on how long they could ride out the storm. Overall, the pandemic changed daily lifestyles drastically.

If we compare India’s unemployment rate in 2020 with other countries, India has performed relatively poorly – both in terms of the world average and compared with a set of reference group economies* (with similar per capita incomes to India’s). Unemployment rates were more muted within the reference group economies, and were also kept low by generous labor market policies to keep people in work.

Despite the scale of the pandemic, additional budgetary allocation to various social safety measures has been relatively low in India compared with other countries. Although India might look comparable to the reference group in non-health sector measures, the additional health sector fiscal measures are less than half those in the reference group. More worryingly, the Indian government’s announced allocation in the 2021 budget for such measures does not show an increase, once inflation is taken into account.

Income, expenditure, consumption, poverty and unemployment

While the macroeconomic statistics provide a snapshot of India’s economic position, they hide the large and unequal impacts on households and workers within the country. An estimated 230 million people have fallen into poverty during this crisis at the same time both wealth and income inequality has been on the rise in India. Estimates suggest that in 2020, the top 1% of the population held 42.5% of the total wealth, while the bottom 50% had only 2.5% of the total wealth. Post-pandemic, the number of poor in India is projected to have more than doubled and the number of people in the middle class to have fallen by a third.

During India’s first stringent national lockdown between April and May 2020, individual income dropped by approximately 40%. The bottom decile of households lost three months’ worth of income. Microdata from the largest private survey in India, the CMIE’s Consumer Pyramids Household Survey or CPHS, shows that per capita consumption spending dropped by more than GDP. GDP recovery did not follow the bounce back in consumption during periods of reduced social distancing. Mean per capita consumption spending continued to be over 20% lower after the first lockdown (in August 2020 compared to August 2019) and remained 15% lower year-on-year by the end of 2020.

Official poverty data are unavailable and the CPHS data come with a caveat of “top” and “bottom exclusions”. For example, official statistics show a rural headcount ratio of 35% in 2017-’18. But the CPHS data estimate it at 25%, which suggests exclusions at the lower end of the consumption distribution. 

Despite these statistical concerns, CPHS does provide consumption numbers for a large sample of individuals, which can provide insights into changes in consumption levels arising from the pandemic. Table 2 reports the percentage of people who have monthly consumption expenditure below different cut-off values. The different cut-offs encompass the official poverty lines (which, in any case, have been considered too low by some commentators). Based on the latest CPHS

Essentially, expanding the money supply in the economy lowers interest rates enough for crucial investments in the economy to continue; this bypasses the otherwise impairing effects of rising borrowing costs at the time of recession and a cash crunch. 

Globally, the case for deficit financing through private borrowing varies vastly for different countries. In the USA for example, there is plenty of private funding that has now been pushed to savings because of the economic slowdown. This, coupled with the fact that interest rates are slumped to near zero and the real interest rate is negative, provides an opportunity for the government to go on a borrowing spree without care despite the debt to GDP ratio climbing up. Essentially there is almost no cost of borrowing this money and high debt levels may be sustained because there is no inflationary pressure. In countries like India, however, the inherently weak fiscal situation of Indian banks and actual deficit levels aside, the question of inflation remains a big challenge. [7] [8] [9] [10] 

Money Supply

India has been experiencing stubbornly high inflation, meaning that the average price level is structurally increasing. The CPI print for August 2020 came in at 6.7% (YoY), which means that in 2020 (with the exception of April) inflation had hovered above the 6% upper band inflation target range of the Reserve Bank of India (RBI). Core inflation rose despite an unprecedented -23.9% contraction of the economy in Q1. During an economic slowdown, lower demand for goods and services normally leads to lower price pressure, but the opposite is true for some goods in the COVID-19 crisis. This is due in part because money supply in the economy has been increasing at 20% annual rate. Monetary policy tried to bring cash in the hands of people and increase consumption following Covid 19. If inflation is not systematically controlled, we could be looking at much worse inflation numbers and a situation of hyperinflation in the long-term. The current situation also solves the dilemma of euphoria in the Indian equity markets. The bull run in the markets is being fueled by the immense amount cash which is being circulated in the economy, thus pushing the prices up. [28]

Impact of the GST on the Indian Economy

The Goods and Services Tax (GST) is a large project which aims to simplify the complex tax structure and enhance the economic growth of the country. GST is a comprehensive tax levy on manufacturing, sale and consumption of goods and services at a national level. The Goods and Services Tax Bill or GST Bill, also referred to as The Constitution (One Hundred and Twenty-Second Amendment) Bill, 2014, initiates a national Value Added Tax (VAT). GST will be an indirect tax at all the stages of production to bring about uniformity in the system. On bringing GST into practice, there was an amalgamation of many central and state taxes into a single tax. It would also enhance the position of India in both, domestic as well as international market by decomplicating existing systems. At the consumer level, GST reform removed the overall tax burden, which was estimated to be as high as 25-30%. A conclusive evaluation of the estimate is yet to be comprehended. Under this system, the consumer pays the final tax but an efficient input tax credit system ensures there is no cascading of taxes: tax on tax paid on inputs that go into manufacture of goods. In order to avoid the payment of multiple taxes such as excise duty and service tax at central level and VAT at the State level, GST would unify these taxes and create a uniform market throughout the country. Integration of various taxes into a GST system will bring about an effective cross-utilization of credits. GST aims to tax consumption, whereas the previous tax regime aimed to tax production. 

Impact

The introduction of the GST  meant the government forecasted the lowest economic growth rate of 6.5% for the financial year 2018, in the prior four years. Further, the central statistical organization has anticipated the negative growth (-3.3%) for the Indian manufacturing sector in the financial year 2017-18 as compared to FY 2016-17. In addition to above, a reduction was also been reported in the Indian Industrial Production. Moreover, the contribution of agricultural sector to the Indian GDP was reduced about 652.11 INR billion in the third quarter as compared to that of second quarter of the year 2017 . The Central Statistics Office has also predicted the negative impact of GST over the agriculture and farm sector. The GST has also influenced the Real Estate sector due to the enhancement in the cost of land, material and building. Further, the aviation sector had a yearly hit of Rs. 5700 crores. Of course there are some bright spots: with the implementation of GST on medicines, 1% tax has been reduced, resulting in cost reduction for the consumers. However, prices increase is expected over the diagnostics tests because of GST. Due to reduction of new work orders, and low activity, the service sector has been particularly affected by the GST. In July 2017, the output of service sector has been reported at lowest level in comparison to past four years. The Nikkei India Services Purchasing Managers’ Index (PMI), has indicated 45.9 for the service output in the month July 2017 which was lowest since 2013 September; this shows a start level of contraction.

On the other hand, the GST is a significant step in Indian taxation: One Nation, one market and one tax has proven popular. It has transformed the India into a single and unified market of 1.3 billion citizens. As per a survey, 50% enhancement has been reported in the base of indirect taxpayer. Further, automobile and real estate sector has expected the benefit due to the implementation of lower sales tax level in the GST. In line with above, the detailed impact of GST over the various sectors of economy has been as appended below. 

Indian economy is classified in three sectors — Agriculture, Industry and Services. Agriculture sector includes Agriculture (Agriculture proper & Livestock), Forestry & Logging, Fishing and related activities. Industry includes ‘Mining & quarrying’, Manufacturing (Registered & Unregistered), Electricity, Gas, Water supply, and Construction. Services sector includes ‘Trade, hotels, transport, communication and services related to broadcasting’, ‘Financial, real estate & professional services’, ‘Public Administration, defense and other services’. Further, every global, international and nation developments have significantly influenced on the Indian economy. Subsequently, the Indian economy has also been influenced due to the implementation of GST. The impact of GST implementation has also been felt in different segments of Indian economy, as per the below.

Agriculture

Agriculture, the primary sector of Indian economy provides employment to large proportion of Indian workforce and economic activity. However, it was damaged most by the GST. The contribution of agriculture sector to the Indian GDP has reduced to 3245.21 INR Billion in the third quarter of 2017, from 3897.32 INR Billion in the second quarter of 2017. With the implementation of GST, the prices of various agricultural inputs have also increased due to enhancement in GST rates. Further, Central Statistics Office (CSO) has forecast about the negative impact of GST on agriculture and farm sector. 

Manufacturing sector 

According to RBI, the manufacturing sector of India had felt the adverse impact due to implementation of GST. RBI has also forecasted for unfavorable conditions regarding revival of Investment activity in manufacturing section due to implementation of GST. Further, a downfall has also been observed in the industrial production in India.

Real Estate 

One of the most significant sectors of economy which had an adverse impact due to GST. With the implementation of GST, buyers will be paying 12% GST, which will be 3.5% more when compared to earlier taxes (4.5 percent Service Tax and around 4 percent of VAT). Further, the costs of land, material and building have also been increased due to GST. 

Textile industry & handicraft

With the implementation of the GST India’s apparel export was reduced 39% in value terms in October 2017. Further, GST has given a boost to textile import. Additionally, handicrafts industry has been badly hit due to implementation of GST. Earlier, handicraft was exempted from tax in more than 15 Indian states. However, 8 Indian states were imposing 5% VAT on handicraft items. At present, Handicraft has been bought within the GST tax slab of 12% and 18%.

Aviation sector and Banking Sector

Due to implementation of GST, the aviation industry may experience the yearly reduction in revenue of Rs. 5,700 crores (as reported by domestic airlines to Finance Minister). Further, GST has also severely influenced the Indian Banking Sector. 

Pharmaceutical Industry 

The Pharmaceutical industry has benefitted. Previously, medicines were taxed at about 13%, but fortunately only 12% GST has been introduced on medicines including ayurvedic. It may result in cost reduction for consumers. However, prices on diagnostics tests are expected to rise due to GST. 

Service sector

India’s services sector covers a wide variety of activities such as trade, hotel and restaurants, transport, storage and communication, financing, insurance, real estate, business services, community, social and personal services, and services associated with construction. GST has had a mixed impact on service sector. It is beneficial in some areas, but at the same time it is creating hurdle in ease of doing business. It is beneficial in items like seamless flow of credit, avoidance of double taxation but has increased a lot of compliance burden. Due to reduction of new work orders, and low activity, the service sector has felt the downturn particularly harshly, after the implementation of GST. In July 2017, the output of service sector has been reported at lowest level in comparison to past four years, immediately after the implementation of GST. The Nikkei India Services Purchasing Managers’ Index (PMI), has indicated 45.9 for the service output in the month July, 2017 which was lowest since 2013 September. 

Petroleum Industry 

The petroleum industry experienced a boost in its sales, as the GST was reduced on in comparison to the old taxes. Presently, the Petroleum products like peat, ores & concentrates, kerosene, tar, coal & ignite, petroleum coke & petroleum have a GST of 14.5%, 13.5%,12%, 7%, and 9.5% less as compared to the old tax rates, respectively. 

Finally, it is important to note, the implementation of GST has posed a great challenge to the Indian government. The shift from destination based taxed to the origin-based taxed has emerged as a big challenge for business organizations. Further, for proper implementation and administration of GST, adequate Information Technology (IT) infrastructure has also become the need of hour. The IT based skilled manpower having complete knowledge and training of GST has also been needed for achieving the objectives of GST. Further, the training and development of citizens has also been required for registration, filling and payment of GST. Due to the enhancement in tax rates in many items, the Indian economy may experience the inflation. [17] [18] [19] 

Comparison of Three Events

In order to examine the effects of the problems, it is necessary to compare the three events across a number of criteria and note what is most effective. 

GDP

From April to June 2020, India’s GDP dropped by a massive 24.4%. According to the latest national income estimates, in the second quarter of the 2020/21 financial year (July to September 2020), the economy contracted by a further 7.4%. The recovery in the third and fourth quarters (October 2020 to March 2021) was still weak, with GDP rising 0.5% and 1.6%, respectively. This means that the overall rate of contraction in India was (in real terms) 7.3% for the whole 2020/21 financial year. [9] [5]

Demonetization

With the gross domestic product (GDP) for the April-June quarter slipping to 5.7%, the reality of the economic slowdown could not be ignored. The World Bank had reduced the India GDP growth forecast to 7% for 2017-18 owing to demonetization and GST (Goods and Service tax). The slowdown was being cited as a delayed consequence of demonetization by the World Bank and while there were various other reasons at play, the steep decline had been credited to be an effect of demonetization.

Unemployment and poverty levels   

Post-pandemic, the number of poor in India is projected to have more than doubled and the number of people in the middle class to have fallen by a third (Kochhar, 2021). During India’s first stringent national lockdown between April and May 2020, individual income dropped by approximately 40%. The bottom decile of households lost three months’ worth of income (Azim Premji University, 2021; Beyer et al, 2021).

Table 3 shows households in the middle of the pre-Covid-19 CPHS consumption distribution saw large drops in spending after the first wave of the pandemic, helping to create a new set of people entering poverty. The percentage of poor people in the second lowest quintile of pre-Covid-19 consumption jumped from 32% to 60% within a year. This was driven largely by rural areas, where the headcount ratio for the second quintile almost doubled. T

This sharp rise in poverty after the first lockdown is consistent with a variety of surveys that highlighted the depth of the crisis (Azim Premji University, 2021). Year-on-year urban unemployment rate jumped from 8.8% in April to June 2019 to a staggering 20.8% in April to June 2020 (Government of India National Statistical Office, 2020). [10]

Demonetization

According to IAS Score (2019), close to 1.5 million workers were left unemployed after demonetization, but the rates were left largely unchanged. The unemployment rate can be contributed to the lower rates of investments occurring in the market. Ajit Karnik (2016) also agrees that demonetization had a negative impact on employment. The labor force participation rate (LPR) was lower than forecasted in October of 2017. The Centre for Monitoring Indian Economy predicted the LPR to be 49.67%. Once demonetization movement was announced the rate grew to 46.28% (Karnik, 2016). The rate hit a low of 45.8 % in November and December of 2016. For example, the road construction sector had to lay off close to 35% of workers due to demonetization. The manufacturing sector also had to lay off approximately 29% [21]

GST

According to the current weekly status approach or CWS, the unemployment rate stood at 8.9% in 2017-2018. Among women, the rate was 9.1% – higher than in usual status approach at 5.7%. Among men, the rate stood at 8.8%, higher than 6.2% in usual status. Unemployment in urban areas was higher than in rural areas – 9.6% according to the CWS approach, as against 7% in the usual status approach. The rate was at 8.5% in rural areas as per the CWS approach, compared to 5.3% in regular status approach.

A number of sectors were also impacted by these events. 

Tiles & Ply:

The tiles sector has been badly hit post demonetization, and the introduction of GST (rate of 28 percent) has further impacted growth as the price differential between unorganized and organized players increased, leading to tepid revenues. About four-fifths of tile and ply is used as new demand, with the balance being used as replacement demand. With the benign real estate activity post introduction of demonetization and RERA, the growth has been further impacted. Firms impacted include Kajaria Ceramics, Somany Ceramics, Century Ply and Green Ply.

FMCG:

Around 38-40 percent of FMCG sales happen through the wholesale channel which largely deals in cash and is not entirely tax compliant. The lower liquidity situation posts demonetization and prudent tax laws of GST have further reduced consumption. To offset the loss in demand, most FMCG players are increasing their direct reach and bypassing the wholesale channel.Firms affected include Emami, Bajaj Corp, Marico, Hindustan Unilever, Colgate.For FMCG products, the Wholesale channel was impacted due to liquidity crunch which led to increase in the market share for Modern retail. The modern trade which used to be 9 percent of overall FMCG revenue is expected to double in next 5 years. Firms affected include Future Retail, Avenue Super Mart, and V-Mart

Automobile (2 Wheelers):

Post demonetization, cash flows of rural India (majorly a cash economy) were impacted the most due to liquidity crunch. These impacted sales of 2-wheeler companies which have sizable sales in rural parts of the country. Firms affected include Hero Moto, TVS Motor and Bajaj Auto

Consumer Durables:

High ticket purchases, especially in the white goods space, were impacted post demonetization. However, sales did improve over subsequent quarters as the liquidity situation got better. Firms include Whirlpool of India and IFB industries. 

Luggage:

With the luggage industry being levied by a GST rate of 28 percent, it has widened the gap between the organized and unorganized players. This has made it difficult for the large players to completely pass on prices to customers. Firms affected include VIP industries and Safari industries. 

Jewelers:

Demonetization and GST have made it difficult for smaller jewelers to do business as the majority of their transaction were done with cash. As more than 70 percent of the jeweler sales used to happen through unorganized/regional players, there has been a structural shift in demand to organized players leading to strong sales growth. Titan industries, PC Jeweler and TBZ. 

Brokerage houses:

As per estimates, demonetization brought back almost Rs 2-3 trillion of money into the mainstream economy, which was supposedly lying ideal. With real estate in the doldrums and uncertainty in gold prices as well as the low rates of debt instruments, equities have emerged as the only investable asset class leading to huge inflows of money towards the same. This has led to healthy growth in revenues for brokerage houses. Firms who have benefited include Motilal Oswald, Edelweiss, JM Financial

MSME Sector:

The MSME sector in particular faced two major shocks in demonetization and the introduction of GST. MSMEs largely operate in the informal sector and comprise a large number of micro enterprises and daily wage earners,” added the report while mentioning that share of MSMEs in overall GDP is around 30 per cent (GOI, 2018), and the sector accounts for about 45 percent of manufacturing output and around 40 percent of total exports of the country.

MSMEs play a crucial role in the employment generation, and contribute significantly to overall economic activity, despite which the contribution faces several bottlenecks inhibiting them from achieving their full potential. About 97 per cent of MSMEs operate in the informal sector, and their share of informal sector in gross output of MSMEs is about 34 percent. As per National Accounts Statistics 2012, the share of informal (unregistered) sector manufacturing MSMEs in total GDP is estimated at around 5 per cent.

The year-on-year (YoY) growth of bank credit to the MSME sector decelerated gradually during 2015 to 1.6 per cent in April 2016 before exhibiting some recovery till October 2016. The deceleration in credit growth during 2014-16 was partly due to overall slowdown in economic activity, rising non-performing assets (NPAs) and reclassification of food and agro-processing units from MSME category to agriculture sector (as per the revised priority sector lending guidelines, issued to banks in April 2015).

On the exports front, the report added that the MSME sector contributes around 40 per cent to India’s total exports (GOI, 2018).  Among various items of MSMEs exports, gems and jewelry, carpets, textile, leather, handlooms and handicrafts items are highly labor intensive and depend heavily on cash for working capital requirements and payment towards contractual laborer. Hence, export shipments of these sectors were impacted by demonetization. [13] [22-26]

Conclusion

These three devastating events were very similar in the fashion that they disrupted the lives of the people. The magnitude of that disruption in each of the three cases was inversely proportional to the economic status of each individual which is obvious given the socio-economic structure of the Indian society. In theory, the purpose behind the implementation of demonetization and the tax regime of GST cannot be questioned, both these policies were realized with a different set of goals but the purpose, namely the control of black money and the implementation of one umbrella tax could prove to be beneficial. The problem however the fact these policies were not openly debated in the parliament meant they were almost arbitrarily introduced. A similar case could be made for the Covid 19 pandemic. The implementation of untimely lockdowns and then the mishandling of healthcare resources during the pandemic resulted in a hugely devastating second wave. 

This is the reality of an economy that has been severely mismanaged by the government. While many proponents of the current government would argue that business reforms and ease of doing business has improved and the amount of capital that has flooded the nation has created jobs and opportunities, the income gap between the rich and the poor has kept on compounding because of the same reason. Demonetization, GST, Covid 19 have economically worsened the lives of the lower middle class and the lower class and the people who are below the poverty line. On the ground stories and the real impact of covid on rural India can be understood and felt by reading  https://www.firstpost.com/india/how-second-wave-of-covid-19-has-decimated-indias-rural-economy-9689231.html. 

Investment during this time period is a very interesting factor to observe. While GST and demonetization slowed the growth of the Sensex for brief periods, the March 2020 crash was followed by a bull cycle which is continuing despite the worrying economic indicators. This could be because the retail Indian investor got exposed to the markets during the pandemic (include broker stats) and obviously the current fiscal policy of the central bank. Whether there is an alteration in the fiscal policy and the levels of money supply is yet to be seen and thus we cannot conclusively say right now if hyperinflation in the country is bound to occur which will impact the poor even more; or perhaps will there be some moderation and how that affects the market especially with the predictions that a bear market is not that far off. [29]For the lack of a better word, the Indian economy right now is in a confusing state of affairs. On one hand, you can see foreign investment and euphoria in the markets increase quarter-on-quarter and the economic recovery from Covid 19 itself has taken hold. However, there is an immense amount work to be done to pull the MSME sector and the whole informal economy itself, which constitutes nearly 40 percent of Indian GDP, to the levels where it was pre-pandemic, pre-GST and pre-demonetization. Competent government policies are needed to restore economic growth. 

References:

1. Impact of Demonetization, Goods and Service Tax and Covid 19 on the Indian Economy. Article DOI:10.21474/IJAR01/11649   DOI URL: http://dx.doi.org/10.21474/IJAR01/11649
2. https://www.moneycontrol.com/news/business/demonetisation-4-years-on-a-look-at-what-it-achieved-and-didnt-6086571.html
3. https://www.investopedia.com/terms/d/demonetization.asp
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6. The Implementation of Demonetization by Raju Rao https://pmworldlibrary.net/wp-content/uploads/2017/09/pmwj62-Sep2017-Rao-Demonetization-in-India-featured-paper.pdf
7. https://www.freepressjournal.in/analysis/indias-economy-is-ailing-and-its-not-just-because-of-covid-19-writes-a-l-i-chougule
8. Understanding India’s Economic Slowdown by R. Nagaraj https://www.theindiaforum.in/sites/default/files/pdf/2020/02/07/understanding-india-s-economic-slowdown.pdf
9. https://www.statista.com/statistics/1229773/india-estimated-economic-impact-of-coronavirus-on-industry/
10. https://scroll.in/article/999275/the-pandemic-in-data-how-covid-19-has-devasted-indias-economy
11. https://journals.sagepub.com/doi/full/10.1177/0972063420935541
12. https://journals.sagepub.com/doi/full/10.1177/0972262921989126
13. https://rbi.org.in/scripts/AnnualReportPublications.aspx?Id=1315
14. https://journals.sagepub.com/doi/full/10.1177/0019466220983494
15. https://www.ilo.org/wcmsp5/groups/public/—asia/—ro-bangkok/—sro-new_delhi/documents/publication/wcms_798079.pdf
16. https://link.springer.com/article/10.1007/s41027-020-00255-0
17. https://www.phdcci.in/wp-content/uploads/2020/08/Impact-of-GST-on-Economy-and-Businesses.pdf
18. https://madhavuniversity.edu.in/impact-of-gst-on-indian-economy.html
19. https://www.researchgate.net/profile/Mr-A-Dash/publication/318421150_POSITIVE_AND_NEGATIVE_IMPACT_OF_GST_ON_INDIAN_ECONOMY_ A_DASH/links/5968a117aca2728ca67bc406/POSITIVE-AND-NEGATIVE-IMPACT-OF-GST-ON-INDIAN-ECONOMY-A-DASH.pdf
20. http://www.theijbmt.com/archive/0932/2116231109.pdf
21. https://www.civilserviceindia.com/subject/Essay/what-are-the-positive-and-negative-impacts-of-GST.html
22. The Impact of COVID-19 Pandemic on Different Sectors of the Indian Economy: A Descriptive Study http:// www.econjournals.com https://doi.org/10.32479/ijefi.10461
23. https://www.economicsobservatory.com/how-has-covid-19-affected-indias-economy
24. https://www.bloombergquint.com/business/demonetisation-impact-on-indian-economy-what-we-know-three-years-on
25. https://www.moneycontrol.com/news/business/markets/top-five-sector-which-got-impacted-the-most-post-demonetisation-nitasha-shankar-2432495.html
26. https://www.business-standard.com/article/news-ani/msme-worst-hit-by-gst-demonetisation-says-rbi-study-118081800287_1.html
27. https://www.ijiras.com/2018/Vol_5-Issue_3/paper_44.pdf
28. https://tradingeconomics.com/india/money-supply-m3
29. https://economics.rabobank.com/publications/2020/september/indias-worrying-inflation-dynamics/
30. https://www.indiamacroadvisors.com/page/category/economic-indicators/money-and-banking/money-supply/
31. * The ‘Reference group’ refers to the closest peer group statistic under which India falls. The reference group for GDP per capita is the Emerging Market and Developing Economies (EMDEs) classification by the IMF. The reference group for the unemployment rate is the Low- and Middle-Income Countries (LMICs) classification by the World Bank. The reference group for the fiscal measures is the Emerging Market and Developing Economies (EMDEs) classification by the IMF. Source: Data on gross domestic product, constant prices (percentage change) is obtained from the World Economic Outlook Database April 2021, International Monetary Fund. India’s GDP contraction is 8% according to the IMF and 7.3% from recent national estimates. Unemployment rates (for youth, adults: 15+) are ILO modelled estimates as of November 2021 and are obtained from ILOSTAT, International Labor Organization and World Bank. Fiscal measures are obtained from Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic as of April 2021, International Monetary Fund.

About the author

Ishaan Marwaha

Ishaan is a Business Economics, Finance, and Entrepreneurship enthusiast. He currently reads in Grade 12 at Spring Dale Senior School, Amritsar, India. He is a winner of national-level business competitions and debates and takes a great interest in studying equity markets and the behavioral economics behind them. He is an avid investor and occasional swing trader. He wants to study Economics and Management at the University of Oxford at the undergraduate level.

The post Impact of Demonetization, GST, Covid-19 on Indian Economy and Investment appeared first on Exploratio Journal.

Abstract

This paper investigates why the mask compliance rates were significantly higher in Taiwan than in the United States during the COVID-19 pandemic. This distinction can primarily be represented by an individualist vs. collectivist mindset, associated with Western and Eastern countries, respectively. Mask wearing was influenced by collectivism; Taiwan’s proximity to the 2003 Severe Acute Respiratory Syndrome (SARS) epidemic and the subsequent policies implemented; cultural norms; psychological factors including higher risk attitude, sensitivity to social norms, and compliance with personal surveillance; and demographics including race, political ideology, and social class. Mask wearing is negatively associated with infection rates but is not fact based or logical: multiple psychological and cultural factors contribute to this compliance variability. Therefore, those that don’t comply are not purely defiant; individualists and collectivists just have a different belief system in what they value and how they behave. As a paper that explores reasons for noncompliance, from a public policy perspective, the message in compliance requests must be tailored to a specific belief system that serves an individual and group’s best interest while respecting personal values.

Keywords: COVID-19, mask-wearing, culture, individualist vs. collectivist, psychological factors

Why Mask Compliance Differed in the United States and Taiwan During the COVID-19 Pandemic: How Individualist vs. Collectivist Cultures Respond in Uncertain Times

COVID-19, a disease caused from SARS-CoV-2 virus, first detected in Wuhan, China, in December 2019, has been a test of responding to health regulations. Common symptoms include cough, fever, chills, loss of taste and smell, just to name a few. Most cases are mild, with symptoms persisting a few days, but some cases are very severe, requiring hospitalization. The virus has ravaged through borders and taken the lives of millions worldwide. Even though the severity of the pandemic varied by country and demographics, the COVID-19 pandemic was an experience that everyone dealt with. However, the responses, attitudes, and behaviors of the citizens of different countries shed light on how people deal during times of uncertainty. Two contrasting examples include the United States and Taiwan. These two countries have significant differences in mask wearing compliance, defined as wearing a mask when in close contact (within 6 feet) of non household members (Key, 2021).

In a literature search of studies on the mask compliance rates between Eastern and Western cultures, there were multiple studies on the compliance rates and reasoning behind this behavior in Western countries, but limited studies in Eastern countries. This would suggest that because the compliance rates are so high in Eastern countries, researchers aren’t conducting studies on why people complied or how to get people to comply, instead they’re more interested in why people DON’T comply.

According to a study conducted by the University of Southern California’s Dornsife Center for Economic and Social Research, approximately 83% of Americans agree that masks are an effective way to protect themselves from contracting Americans report actually wearing masks when in public places or in close contact with members not of the same household (Key, 2021). Another study found that 64% of Americans that report not wearing a mask responded, “It is my right as an American to not wear a mask” or “It is uncomfortable.” (Vargas & Sanchez, 2020).

The Taiwanese government, on the other hand, instituted a mask mandate with a fine between $100-500 USD for noncompliance (Ministry of Health and Welfare, n.d.; Ministry of Health and Welfare, n.d.). However, there were some reports of non-compliance in some cities in Taiwan. For instance, 604 fines were given in Kaohsiung within 1.5 days (Zheng, 2021) and 848 fines given in Taichung within 2 months (Hong, T. & Lǚ, Z., 2021). Both cities have a population of around 2.7 million, so based on this statistic it can be speculated that the non-compliance rate in Kaohsiung and Taichung is about 0.02% which is still significantly lower than the approximately 50% noncompliance rate in the United States. This finding raises questions on why there is such a big disparity.

The United States has over 330 million people with diverse backgrounds, socioeconomic levels, and beliefs. When the pandemic hit, those outside of the United States saw how a high-income country like the United States dealt with unprecedented circumstances. As of October 2021, the US has over 43 million confirmed cases and 688,000 deaths (World Health Organization, 2021).

Conversely, Taiwan is a densely populated island off the coast of Mainland China with over 23 million people. Due to its proximity to China, where the virus originated, and constant air travel to and from, Taiwan was expected to have the 2nd highest number of cases. However, this was proved to be incorrect. Taiwan along with other countries like Singapore and New Zealand were able to implement policies and community-based preventative measures to slow the rate of transmission and infection rates. By April 2020, the local transmission was at zero (The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, 2021).It stayed that way for about a year. When comparing infection and mortality rates, as of October 2, 2021, the confirmed cases per million people in Taiwan and the United States is 680 and 131,020, respectively. The confirmed number of deaths per million people in Taiwan and the United States in 35 and 2,103, respectively (Ritchie et al., 2020). These statistics illustrate the significant contrast in the severity of the pandemic in these two countries with the US infection rate about 200 times that of Taiwan and the US mortality rate about 60 times that of Taiwan. Why is there such a major difference? How did this happen? What lessons can other countries learn and what do the actions by Taiwan tell us about their attitudes and cultural norms?

Specific factors that can explain why the Taiwanese and Americans responded differently to the pandemic lie mainly in cultural differences. These distinctions include Taiwan’s past experience with SARS, established social norms, different healthcare systems and access to resources, an individualist vs. collectivist mindset that serves as the foundation for psychological factors, and diversity in the population.

Past Experience with SARS: Proximal vs. Distal Threat

Taiwan had a greater proximal distance than the United States did to the SARS epidemic in 2003. General psychological principles suggest that first-hand experience has a greater impact on someone than watching from far away. From Taiwan’s experience with SARS, the government put policies in place for controlling another global health crisis, such as universal mask-wearing, quarantine requirements instituted in February 2020, closing down borders to foreigners in March 2020, and contact tracing systems after the first identified case in China (Taiwan Centers for Disease Control, 2020). However, Americans had no prior experience with a pandemic to this level. Given Taiwan’s past experience in dealing with a health care crisis, the Taiwanese were more familiar than Americans were with healthcare recommendations when these preventative measures were put in place to curb the spread of COVID-19. Additionally, in the beginning of the pandemic, Americans were not directly involved or affected by the pandemic because of its origin in China. This feeling was bolstered by Trump’s rhetoric calling COVID-19 the China virus, resulting in some Americans believing that they could not get the virus because they had limited a relationship with China. For instance, they weren’t Chinese or planning on visiting China soon.

Differences in the Governmental Leadership

Health Care Services

Another reason is the difference in access to health care services. In the United States, there is no universal health care. Universal health care ensures that all citizens have access to health care services when they need it without financial burden. About 8% of the US population is uninsured (Keisler-Starkey & Bunch, 2020). Given the dozens of insurance companies, including in the public and private sectors, Americans pay different fees, resulting in the fragmented health care system that provides them varying degrees of access to certain medical services. The average annual health insurance in the United States is $5,940. This number fluctuates given location and different insurance tiers. Some plans can reach an upward annual cost of $8000 (Price, 2021).

Taiwan, on the other hand, has the National Health Insurance (NHI) System which provides universal health care to 99% of the population. The NHI provides citizens with “SMART” cards, which store a patient’s medical history and records.

After the first confirmed COVID case was identified in China, Taiwan took strict actions to prevent the transmission to its island, given the frequent flights between Mainland China and Taiwan. Taiwan already had a public health agency, the Central Epidemic Command Center (CECC), instituted after Taiwan’s experience with SARS in 2003. The CECC responded to the COVID-19 outbreak and followed pre-established protocols to control a pandemic and had access to other data from various government agencies.

On January 20, 2020, when the CECC was activated, patients’ medical history from the “SMART” cards was integrated with their travel history and data. From there, a system categorized each citizen into high risk or low risk for contracting the virus. High-risk individuals were those who had traveled to high-risk areas, such as Wuhan, and low-risk individuals included those who had not traveled abroad and had no preexisting health condition. After this integrated information was stored on a citizen’s “SMART” card, low-risk individuals were ordered to buy a week’s worth of masks and could live normal lives. High-risk individuals, on the other hand, were sent into a two-week quarantine after which they could join everyone else (Wang et al., 2020; Vox, 2021). Quarantines as such were effective because it controlled the spread and didn’t rely on quarantining only symptomatic individuals, as asymptomatic individuals have a high chance of transmitting the virus before developing symptoms, if they develop symptoms (Summers et al, 2020).

Taiwan also banned foreigners from entering and in March 2020, the CECC categorized everyone flying into Taiwan to be considered high risk so they all had to undergo isolation quarantine. To make sure no citizens left their quarantine facility, the CECC tracked people’s location using cell phone data. There were also daily phone call check-ins to monitor any possible symptoms as well as occasional in-person check-ins (Vox, 2021).Taiwan also instituted a fine between NT $200,000 and NT $1,000,000 (approximately $7000 USD and $36,0000 USD) for breaking quarantine rules (Ministry of Health and Welfare, 2020).

However, studies have shown that only relying on case-based preventative measures such as quarantine and contact tracing wouldn’t have been sufficient for controlling the pandemic. Instead, population-based measures, such as wearing masks and social distancing, were useful in the initial containment of the virus (Ng et al., 2020). Taiwanese attitudes towards wearing masks and having a collectivist mindset, discussed later in the paper, also helped enforce these measures. Additionally, the then Vice President of Taiwan, epidemiologist Chen Chein-Jen, had broadcast announcements to assist citizens in population based measures such as mask wearing, frequent hand washing, and preventing mask hoarding. Similarly, the CECC set a fixed price for masks and used funds and the military to increase mask production. By January 20, 2020, when the CECC was activated, the government had 44 million surgical masks and 1.9 million N95 masks (Wang et al., 2020).With an integrated health insurance system, quarantine requirements, and resource allocation for mask production, Taiwan was organized and prepared to contain the virus.

U.S. Response to COVID-19

Compared to Taiwan’s approach, the United States’ response to the pandemic was completely different. To start off, the federal government put the responsibility of controlling the pandemic onto the state and local governments. This led to a divided nation, with different states instituting different policies, resulting largely from political ideology (Lewis, 2021).

Additionally, during the beginning of the pandemic, there was limited testing and even so, testing criteria was too high, mainly for symptomatic individuals admitted to hospitals, likely to have COVID-19 (Lewis, 2021). The Centers for Disease Control and Prevention (CDC) also released a flawed test, reporting that it could fail 33% of the time (Temple-Raston, 2020). Furthermore, the CDC reported that the spread of COVID-19 likely started in January/February 2020. However, the surveillance systems for detecting the virus and reports of flu-like symptoms were insufficient allowing the virus to spread undetected for more than a month (Jorden et al., 2020).

There was also mixed information from then-President Trump, government agencies including the CDC and the World Health Organization (WHO), and the behaviors from local officials. Examples include Trump’s denial of the seriousness of the virus as well as government agencies changing their message for mask guidance in part due to medical supply shortages for hospitals and health care workers (Molteni & Rogers, 2020; World Health Organization, 2020). The mask guidance during the beginning of the pandemic sent confusing messages for further encouragement of mask-wearing. Until April 2020 for the CDC and June 2020 for the WHO, these agencies only recommended masks for those experiencing symptoms, but it has now been established that the virus can also spread from asymptomatic individuals. Consequently, it creates confusing mask guidance as well as making it hard to know who and which government agency to trust.

Lastly, the US had insufficient contact tracing and quarantine policies put in place, which seen from other countries, such as Taiwan and New Zealand, had a role in attenuating the transmission (Lewis, 2021).

Individualist vs. Collectivist

One way to further understand the striking difference between these two countries is by looking at contrasting social and cultural norms. These perspectives can differ broadly and are learned distinctions in behavior imposed by cultures, through family, friends, classmates, and more. Psychologists that study cultural differences have found a distinction between Eastern and Western culture which provides insight into the difference in pandemic responses. This distinction can be represented by an individualist vs. collectivist mindset, ideas put forth by Markus and Kitayama. An individualist mindset, associated with many Western countries, puts the individual or self above the group. These individuals value and have personal independence. Collectivists, on the other hand, associated with many Eastern countries, have strong social ties and a sense of belonging to their group. Collectivists are more likely to agree that they are willing to sacrifice their own self-interests for the well-being of the group and that their happiness depends largely on the happiness of those around them. Individualists are more likely to agree that they often do their own thing and that whatever happens to them is their own doing, emphasizing the responsibility for personal well-being (Lu et al., 2021).

To illustrate the prevalence of individualist vs. collectivist cultures, in collectivist cultures, it’s more normal to see families of multiple generations living together. In the United States, a record-breaking 64 million Americans live in multi-generational households, including sizable immigrant collectivist populations. Asian and Hispanic populations, many of which are considered collectivist countries, are rapidly increasing in the US. Asians and Hispanics are more likely than whites to live in a multi-generational household, with approximately 29% of Asians and 27% of Hispanics doing so (Cohn & Passel, 2018). This sense of belonging and community from collectivist beliefs, carried over into the United States, include taking care of elderly and 1 putting others’ interests before theirs, such as potentially sacrificing personal health, commitments, or time to help out. Research suggests that collectivists are more likely to care for elderly family members as a means to strengthen family ties whereas individualists are more likely to limit caregiving and use formal social services as a means of support (Pyke & Bengtson, 1996).

This individualist and collectivist mindset can be used to understand how individual and group rights and responsibilities influenced behavior during the pandemic. For example, individual rights include the personal freedom of choosing whether or not to wear a mask and take the vaccine. To further illustrate, an individualist is more likely to say that they don’t want to wear a mask because it’s uncomfortable whereas a collectivist is more likely to agree that discomfort is not a valid excuse for going against group norms. Individual responsibility entails taking care of one’s health, through social distancing and wearing a mask. For instance, an individual wearing a mask for their personal health and not contracting COVID.

Group rights mean that being part of a collective gives access to specific privileges: a right to health care and access to masks and vaccines. Being a member of a group also implies specific behavior expectations. This can include taking the vaccine and following policies such as travel restrictions, quarantine, social distancing, mask mandates, to prevent others from possibly contracting the virus. These important distinctions highlight the different reasons individuals give in mask behavior, with individualists more likely to put themselves before the group and collectivists prioritizing group needs.

Cultural Norms

Even before policies for stopping the spread of the virus were implemented, Taiwan and many other Eastern countries had a norm for wearing surgical masks when experiencing the common cold or similar viruses to protect others and for taking care of the elderly or groups that were at higher risk (Jennings, 2021). So during a pandemic, it seemed normal if not obvious to be wearing masks in public places, on public transportation, and walking around. This mindset and behavior echoes a collectivist mindset present in many Eastern cultures.

For Americans, on the other hand, the preventative measures seemed unusual and unprecedented, since they’ve never experienced a global health crisis to this scale before. Consequently, the pandemic was an anxiety-provoking experience with changes in daily routine, with economic, financial, and health threats, as well as immense uncertainty: lots of unknowns from long-term COVID-19 effects, how to deal with variants, and confusing guidance on preventative measures from government officials and agencies. As a result, the link between behavior and curbing COVID-19 transmission might not have been as straightforward for Americans as it was for the Taiwanese based on different experiences and how the pandemic was handled. Along with the diverse backgrounds of its citizens, the United States found itself divided. As policies such as mask mandates and isolation requirements slowly rolled in, some Americans refused to follow these rules.

Collectivism Predicts Mask-Wearing

It has been well established that masks are an effective way to slow the transmission of COVID-19. Studies have also shown that there is a negative correlation between mask wearing and infection rates. As stated earlier, a USC study reported an approximate 50% mask noncompliance rate in the United States and reports of noncompliance in Taiwan predict an approximate 0.02% noncompliance rate (Key, 2021; Zheng, 2021; Hong, T. & Lǚ, Z., 2021).

Furthermore, studies have also shown that collectivism is positively correlated with mask-wearing. This holds true not only to illustrate the Taiwan vs. United States distinction, but also amongst many individualist and collectivist countries. Countries that scored higher on a reserve-coded scale of Hofstede’s individualism index (represented as a collectivism scale) such as the Philippines, Indonesia, and Thailand, had higher mask compliance rates than individualist countries that scored lower on the scale such as Sweden, The Netherlands, and Finland. These results are after controlling for other factors (e.g., political affiliation and government stringency) (Lu et al., 2021).

This is true not only when comparing the United States to other countries but stays consistent in the United States, with people in more collectivist regions (states and counties) more likely to wear a mask. For instance, states such as New Jersey, California, and Maryland scored higher on the state-level collectivism scale sourced from Vandello and Cohen (1999) and in mask compliance compared to states such as Arizona, Ohio, and Wisconsin, which scored lower on both the state-level collectivism scale and in mask compliance (Lu et al., 2021).

Masks can create physical inconvenience and be uncomfortable. As said earlier, one study found that 64% of Americans that report not wearing a mask responded, “It is my right as an American to not wear a mask” or “It is uncomfortable.” (Vargas & Sanchez, 2020). These actions follow an individualist mindset of protecting personal choice and freedom, but disregard that their actions can affect others (Stewart, 2020). Conversely, collectivists are more willing to put aside their personal inconvenience for the collective welfare and well-being (Biddlestone et al., 2020).

As mentioned previously, there is a $100-500 USD fine for not complying with mask mandates in Taiwan, along with limited reports of noncompliance (Ministry of Health and Welfare, n.d.; Ministry of Health and Welfare, n.d.). In collectivist cultures, the rules are more strict, with hefty consequences for non compliance, because the norm is an expectation to follow the policies implemented. In individualist cultures, on the other hand, the mandates are less strict and more complex and ambiguous because individualists are less likely to comply with rules that sacrifice personal freedom for the well-being and welfare of others. Cultural and personal beliefs can influence how rules are put into place and how people respond.

Additionally, in the US, there is a large divide between democrats and republicans based on their political ideology. Republicans can be seen as more individualist because they value personal freedom and limited government interference in daily personal matters whereas democrats can be seen as more collectivist because they value greater government intervention in economics affairs and a balance between orderly society and liberty. When looking at the difference between mask compliance in democrats and republicans, a striking difference is revealed. Democratically leaning Americans, aligned with collectivist values, have a higher mask-compliance rate than republican leaning counterparts, aligned with more individualist values, have a lower rate of mask compliance (Xu & Cheng, 2020).

To conclude, it is crucial to note that lower mask compliance rates in the United States is not because of Americans being defiant against preventative behaviors, but because of contrasting belief systems and pandemic unpredictability. These findings do not suggest that Americans are associated with various personality traits but instead shed light on the distinct cultural norms affecting behavior.

Psychological Factors

Personal Freedom and Surveillance

Psychological factors, supported by an individualist and collectivist mindset, can also influence mask wearing behavior. The first factor is the idea of personal surveillance. Collectivists are more likely to agree that groups can intrude on an individual’s privacy, especially if it’s for the greater good, since collectivists are more likely to sacrifice their personal freedom for the collective (Bellman et al., 2004). Individualist cultures are more likely to put themselves before the collective to protect their personal freedom, a value that the nation was founded on. This can be seen through the reactions that Americans had towards tracking devices. Before the pandemic, tech companies shared consumer location data with the government to make it easier to track the location of Americans. According to results from a survey in December 2020 conducted on American adults, 42% of the men who responded and 52% of women who responded were very uncomfortable with this (Johnson, 2020). During the pandemic, other companies, such as Google and Apple, used consumer data to track potential exposure to COVID-19. Over 60% of US adults found this COVID-19 exposure tracking tool to be very or somewhat concerning for their privacy (Johnson, 2020).

In South Korea, a collectivist country, government surveillance and tracking has been implemented even before the pandemic. For example, the government has access to credit and bank transaction records to prevent fraud. This system was then repurposed during the pandemic to track where people went, from restaurants to subways. Additionally, because 95% of adults own a smartphone, data location, which was originally used in criminal investigations, is now used for contact tracing. Surveillance footage utilized for investigative purposes and can now provide real time, to the minute, tracking of someone’s location. Koreans can also get sent text messages for outbreak updates. The use of South Korea’s established government surveillance network made it easier to ensure public health safety. Even though there was some talk about privacy concerns, there are limited reports on noncompliance, emphasizing the collectivist tendency to allow personal surveillance for public health purposes (Fendos, 2020).

Risk Attitude

Risk attitude is another psychological factor that affects mask wearing and can be explained through the individualist vs. collectivist mindset. Recent studies show that risk aversion, defined as less likely to engage in risky behaviors, was correlated with compliance to engage in protective behaviors during the pandemic. This was not only true in a pandemic setting but in general, with individuals that have higher levels of risk aversion less likely to smoke or engage in heavy drinking. (Xu & Cheng, 2021).

During a study conducted on Italians, results revealed that emerging adults were more concerned with their relatives and other individuals/community members contracting COVID-19, potentially through them being an asymptomatic carrier, than testing positive for COVID-19 themselves. This collectivist mindset was correlated with a higher perceived risk of infection (Germani et al., 2020). This perceived risk was positively associated with engaging in protective behaviors such as mask wearing and social distancing, a US study found (Duong et al., 2021).

Mask-wearing behavior has similarly been observed and studied in many Asian countries, including Taiwan’s long-standing cultural norm of wearing surgical masks when experiencing symptoms, such as a sore throat and runny nose, as a means to protect others, mentioned earlier (Jennings, 2021).The collectivist mindset and risk perception associated with mask-wearing in different regions can help to support the reasoning behind the Taiwanese mask compliance.

Additionally, as said earlier, amongst the Americans that report not wearing masks, 64% of those Americans said that they didn’t wear a mask because it was uncomfortable or that it’s their right as an American to choose not to wear a mask (Vargas & Sanchez, 2020). An individualist mindset provides reason for these attitudes and behaviors present in some individuals.

Sensitivity Towards Social Norms

The Taiwanese have strong responsiveness to social norms. There is a sense of pressure for wearing masks in subways and public areas. The community will also shame those for non-compliance. For instance, this mentality towards social norms is epitomized in what one Taiwanese said in a CNBC article, “We have this phrase in Taiwan that roughly translates to, ‘This is your country, and it’s up to you to save it’” (Farr, 2020).The government policies also add to this, with hefty fines, up to $500, for non-compliance (Ministry of Health and Welfare, n.d.; Ministry of Health and Welfare, n.d.).

These distinctions can again be supported by an individualist vs. collectivist mindset, in terms of emotional reactions. For example, one study conducted by Matsumoto, Kudoh, Scherer, and Wallbott (1988) found that Americans and Japanese experienced similar emotional reactions but Americans experienced emotions longer, with greater intensity and more bodily symptoms such as verbal reactions, lumps in the throat, breath changes. To conclude the study, more Japanese agreed that acting on these events when coping with these emotional situations was unnecessary, showing a weaker association between emotion and behavior (Scherer, Matsumoto, Wallbot, & Kudoh, 1988). The findings can be expanded out and offer an explanation to how individualists vs. collectivists in the US and Taiwan behaved in mask compliance. The Taiwanese held each other accountable and were less likely to act on their emotions if they didn’t fully agree/want to wear a mask. Americans were more likely to act and go against these mandates, as can be seen through countless protests across many states, even if they had felt similar levels of emotion towards masks as some Taiwanese did.

One of the possible explanations for this is that many of the emotions experienced are ego-focused emotions, meaning they mainly concern the individual’s internal attributes or characteristics. Some examples include anger, frustration, and pride. Therefore, it is logical that individualists are more likely to attend to and act on these emotions than collectivists are, say if they feel their personal freedom is being violated, because these ego-focused emotions are at the heart of an independent self (Markus & Kitayama, 1991). Through the exploration of how psychological factors influenced mask compliance, the prevalence of an individualist vs. collectivist mindset underscores the application to attitudes and behaviors.

Differences in Diversity among Populations

The United States has great diversity with Americans having their own distinct identity, from various demographics, gender, race, ethnicity, and social groups. The United States is rapidly becoming more complex, with data estimates from the US Census Bureau showing that nearly 4 of 10 Americans identify with a race or ethnic group other than white (Frey, 2020; US Census Bureau, 2021). Some Americans then form subgroups with those of similar demographic identities, and base social behavior off of their beliefs and backgrounds.

One way of measuring ethnic diversity is based on an analysis of ethnic fractionalization, the probability that two random individuals from the same country are not from the same group (race, ethnicity, or other criteria). This can be done through Fearon’s analysis in which ethnic fractionalization is on a scale from 0 to 1, with 1 being the most ethnically diverse. When comparing the numbers on Fearon’s analysis, the United States is 0.49 and Taiwan is 0.274 (Alesina et al., 2002; Fisher, 2019).

Diversity is a descriptive factor in the individualist vs. collectivist mindset, with individualism associated with more heterogeneous cultures and collectivism associated with more homogeneous cultures. This diversity in mindset can explain why some states have higher mask compliance rates, as mentioned in the “Collectivism Predicts Mask Wearing” section (Lu et al., 2021).

]From a racial perspective, in a study conducted by USC, the group that was least likely to consistently wear a mask when in close contact with non-household members were whites, with a compliance rate of 46%. Compared to whites, other races including latinos, blacks, and others had higher compliance rates with 63%, 67%, and 65%, respectively (Key, 2021). Diversity in all demographics, from race, locale, and ethnicity, had significant contributions the way individualists and collectivists engaged in mask wearing. This emphasizes the dynamic intricacies of various societies in which no single factor can predict mask wearing.

Conclusion

Culture is an important factor in behavior that has intrigued me as someone who is mixed and spends time with those of various ethnicities, races, and social groups. When the pandemic hit, I spoke to many family and friends that had completely different views on how the virus affected them and what appropriate measures they believed should be taken. At times it was overwhelming and I sought to understand if there was an underlying cultural factor at the root of different attitudes and behaviors. I found that my relatives in Taiwan had one of the most striking contrasts compared to my relatives in the United States in the way they viewed how the government and our societies should be responding.

Since the onset of the pandemic, the infection and mortality rates have been significantly higher in the United States: the US infection rate is about 200 times that of Taiwan and the US mortality rate is about 60 times that of Taiwan (Ritchie et al., 2020). I chose mask wearing as my control factor because it is a universal way to lower the rate of transmission. From talking with my family and friends, I observed that mask-wearing was one of the most heavily debated topics.

The mask compliance rates are significantly higher in Taiwan than in the United States. Through my literature search, I found multiple demographic, cultural, and psychological factors, influenced by an individualist vs. collectivist mindset, that predicted mask wearing. Taiwan’s proximal distance to SARS in 2003 resulted in public health regulations that gave public health agencies access to patient medical and travel records for contact tracing and testing. Along with this, Eastern countries have norms for wearing masks to protect others. Race, locale, and political ideology was associated with mask wearing. Psychological factors involving higher risk attitude, sensitivity to social norms, and personal surveillance compliance were affected by a collectivist mindset. As a caveat, individualism tends to be correlated with Western countries but there is still a large percentage of Americans that do not associate with an individualist mindset. This results in greater diversity within the United States and Americans having differing views of cultural beliefs. Further, Taiwan’s cultural norms and policy preparedness proved to be significant in Taiwanese compliance with preventative measures.

At the heart of a collectivist is having compassion and taking in another perspective by wearing a mask to protect others. On the other hand, a reason individualists are not complying with mask mandates is not because of pure defiance but because they have a different belief system. For instance, for some individualists, it may be harder to conceptualize that they’re part of a collective and that their individual behavior is affecting the group.

These findings are important because it provides insights into how people react to governmental health regulations during times of uncertainty. Neither individualists nor collectivists are “better” than the other. There are specific attributes of each that may better serve during specific circumstances, such as a global health crisis, but I am not stereotyping individualists or collectivists with specific personality traits. I am not here to convince anyone to change their belief system but in global health crises it may be useful to adopt more collectivist actions while also taking steps to protect themselves. This can be achieved without taking away key components of identity and protecting personal values. One big question is how can we get people to comply without making them change their belief systems?

This paper explores the reasons behind noncompliance, so we can get insight into how to frame compliance requests for individualists and collectivists in different manners with the goal of showing that mask-wearing benefits the health of the public. For collectivists, explaining how mask-wearing benefits the group. Ironically, individualists that are not complying with mask mandates are presenting potential health risks to themselves and the group; these individuals are more likely valuing personal freedom over health. When framing compliance requests for individualists, it may help to emphasize that wearing masks acts in their own interests as well as establish the link between individual behavior and group health. These changes in reframing requests appeal to the individualist and collectivist belief systems while respecting personal values.

It is also important to note that extreme collectivism and extreme individualism can also harm self-interest. To further illustrate, extreme collectivism is primarily not taking into account individual needs and extreme individualism is solely focused on personal desires. Neither of these extremes act in one’s best interest because it fails to take into account other perspectives and people.

To conclude, in everyday experiences, it’s good to find some common ground. That way different perspectives can be acknowledged to create a more informed and dynamic view of the world. Sometimes it’s better to be an individual, sometimes it’s better to be a collectivist. In general, it’s hard to change belief systems to adopt other views but being able to empathize and understand why people are the way they are is beneficial not only in a pandemic, but in daily life.

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About the author

Alena Powell

Alena is a senior at Avenues: The World School in NYC. She is passionate about the social sciences, public policy, and global/cultural studies. Her academic interests are interdisciplinary and experiential as she hopes to continue immersion in different cultures, learning about various economic and government systems, and explore pathways to apply her knowledge through social impact.

The post Why Mask Compliance Differed in the United States and Taiwan During the COVID-19 Pandemic: How Individualist vs. Collectivist Cultures Respond in Uncertain Times appeared first on Exploratio Journal.

1. Introduction

For over a year now, COVID-19 has wrought unexpected and far-reaching adverse effects on the health, safety, economic and social well-being of societies across the world. Of particular concern is the impact of this pandemic on the quantity as well as the quality of learning of school-age children. Considering the adage, that children are the future of the nation, this negative impact of COVID-19 on school learning is a matter of grave concern both in the short term and the long-term.

This paper will explore these short as well as long-term effects on school learning in China.

Firstly, because during the epidemic, students could not go to school, they had to take online classes at home. This robbed them of face-to-face learning as well as the experience of in- person socializing with their peers. We would like to explore the short-term as well as the long-terms effects of this adverse change. The major short-term impact of the pandemic on schooling was the modality of instruction – classroom teaching and learning suddenly had to go ‘online’. What are the problems in online-teaching and learning?

Online classes face many problems, not all regions and families have the conditions to access the Internet. Some families do not even have mobile phones. If you have a mobile phone, you do not necessarily have a network or Wi-Fi. Therefore, online classes, virtually also to many poor families caused a burden. Of course, this is a hardware problem when these problems are solved. And new problems arise, because the teaching level of different regions is different, and there are differences in children’s performance. The national unified online course, teaching materials cannot seamlessly connect most of the students’ learning. Perhaps in the special period, the education department has done some special work, and it is the best way to solve the problem of children’s learning in front of the epidemic.

Besides the issues of inequitable or spotty availability of the internet and the related equipment, there is the critical question of professional training of the teachers. This pandemic took everybody by surprise. The teachers as well as the school administrators were expected to ‘learn overnight’ how to use the new technology as well method of teaching. That is not so straight forward since it needs professional development, training, and time to learn.

Secondly, at the family level, the parents are naturally very concerned about the students’ performance in the college entrance examination. In China, like in many other countries, high school entrance examination and college entrance examination are very important for students with very significant impact on the rest of their career and life opportunities. Therefore, these are the most concerned issues of parents and all walks of life. The impact of the epidemic will certainly affect candidates to delay the examination.

Thirdly, in many children’s minds, their idols are no longer stars. And more are great people like Zhong Nanshan, Li Lanjuan, Yuan Longping and so on. There is no doubt that the seeds have been planted in the hearts of children. I hope that when I grow up, I will become a person like them and make my own contribution to the society and the country. This outbreak. No doubt for the children, let them grow up more clearly aware of their coming to this society. What to do. What kind of people they should be has a huge impact on their values and world outlook?

2. COVID-19: A brief History of the Pandemic

In this section, I will briefly describe the history of the outbreak of the COVID-19 pandemic.

Caught off guard in the late Fall of 2019, the novel coronavirus pneumonia from Wuhan has been a shocking surprise for China. There are different opinions about the source. Some say it came from the seafood market in South China, while others say it came from the expatriates of last year’s Wuhan military games.

On the morning of December 31, 2019, the expert group of the National Health Commission arrived in Wuhan to carry out relevant testing and verification work. The investigation found that most of the pneumonia cases were the operators of South China Seafood City.

On January 1, 2020, the South China seafood wholesale market was closed, and the public places in Wuhan, especially the farmers’ markets, were locked down in the interest of public health and efforts to control the spread of this deadly virus. In addition, the disease prevention knowledge was widely publicized to enhance public self-protection awareness. People were urged to cooperate with the national and provincial pathogenic research especially with the National Health Commission to timely report the epidemic information to the World Health Organization.

It was clear that this lockdown had a significant effect on people’s daily life. People cannot go to the public since gathering together will make it easier to be infected. School cannot set very large events like concert, sports meeting. Everyone had to wear masks anywhere and anytime which make communication very difficult.

What happened to the transportation, the economy, the unemployment and how families were affected and in return how that affected the children.

1. Transportation: Do not take public transportation when you have respiratory symptoms such as fever. Take less public transportation to avoid infection.
2. The economy: Many shops closed: they could not have many profits because of the reduction on customers.
3. Families: people had more time to stay with their family members because they seldom go outside due to the pandemic. we have a deeper understanding of family affection. In the past, we always felt that the holidays, especially the Spring Festival holidays, were too short, and the family members gathered less and left more. We felt that it was not easy to get together. This epidemic situation made us spend a super long “holiday”. We all stayed at home, and the whole family ate, lived, slept, and slept together for such a long time as never before. Our understanding is more comprehensive and further. Family is deeper.

The change of thought is a deeper understanding of life. The outbreak of the epidemic caught many people by surprise. Due to improper prevention and control or inadequate understanding, many people were infected with the virus and lost their lives. The healthiest of us, while feeling lucky, are deeply saddened by the fragility of life and the inconstancy of life. It used to be said that the moon has its ups and downs, and people have their ups and downs. It is the true facts that touch the deepest part of the mind.

3. Impact on Education at the School Level: Short Term Effects

3.1 The nature of the short-term effects in China itself

The major short-term effect was the very suddenness of the crisis. It was extremely disruptive of the life as people knew it. The range of effects included closing of businesses, lay- offs, closing of schools, shortages of food and essential supplies and generalized increase in uncertainty. The situation was particularly serious for parents with young children since after school closure, their schooling was interrupted and at the same time the parents were obligated to take care of them.

Thus, the major short-term or immediate effects could be job-related for the adults which meant financial uncertainty and budgetary crisis. Secondly, disruption of family life especially jeopardized the education of the school-aged children. Thirdly, the women in the society had to share a disproportionate burden of this crisis. Besides facing the heath crisis that everybody else faced, they disproportionately lost jobs, and, at home, they were expected to take care of the household duties such as cooking, cleaning and, on top of that, make sure of home-schooling of their children.

The picture shows that during the epidemic, which city helped which particular area.

The modernization of national governance system and governance capacity will be further accelerated, and the level will be further improved, especially the ability of defense against major biological risks will get new breakthroughs. The outbreak of the epidemic has conducted a practical test on China’s governance system and governance capability, which not only shows the effectiveness and achievements of the governance system and governance capability, but also exposes its shortcomings and defects. It provides living data and information for accelerating the improvement and improvement of the governance system and governance capability in the future, and provides a reference for the development of digital, artificial intelligence, robot, blockchain and other modern industries The application and role of science and technology in crisis management and risk prevention and control provide a realistic basis, and provide a rare opportunity for China to turn crisis into opportunity and save danger. This is another wake-up call for China’s biological risk crisis after SARS, prompting China to speed up the modernization of its governance system and governance capacity.

There will be new changes in people’s values. The most glorious and critical moment of human nature. The novel coronavirus pneumonia animal disease animal novel coronavirus pneumonia has been confirmed. For a long time, people have pursued the idea of “everything is human food”. The virus infecting wild animals has caused SARS, Ebola, new crown pneumonia and other viruses to spread to humans. After this outbreak, there will be great improvements in legislation and people’s habits, animal ethics and values. We should deal with the relationship between man and nature with a more rational attitude, respect, and revere nature, and treat wildlife well. The actual combat of the epidemic prevention and control not only highlights the glory of human honesty, kindness, integrity, justice, love, and courage, but also has a new sublimation in dealing with the relationship between people. People sincerely respect the ordinary medical staff, soldiers, farmers, workers, and those who are not afraid of sacrifice and sacrifice for everyone What ordinary fighters, volunteers and donors have done shows the importance of harmonious relationship between people, which makes people turn their attention from power, wealth and fame to respect and revere life.

Thus, it is very clear that the COVID -19 crisis had severe and unanticipated immediate effects which left the society in a virtual shock.

The novel Coronavirus (COVID-19) had spread across 215 countries and territories in all 5 continents by May 12, 2020. On January 30, 2020, the unrelenting spread of COVID-19 prompted the World Health Organization to declare it a public health emergency, and on March 12, 2020 COVID-19 was declared a pandemic. This pandemic has caused great social and economic disruptions, leading to a decline in consumption, investment, services, and industrial production activities around the world. In particular, the insurance market in mainland China has been severely affected. The year-on-year (YOY) growth rate of gross premium in the first quarter of 2019 was 15.8%, while the rate was 6.27% in the first quarter of 2020, indicating a decrease of 9.53 percentage points in growth rate. In addition, the monthly YOY growth rates of premium in the first three months of 2020 were −12.53%, −21.35%, and 1.93%, respectively, showing a sharp decline compared to the rates of 23.97%, 10.19%, and 6.76%, respectively, in the same periods of the previous year.

While research on COVID-19 and equity markets globally is evolving (Ali, Alam, and Rizvi 2020; Haroon and Rizvi 2020), a literature has developed that examines the impact of pandemics on various energy issues (Apergis and Apergis 2020; Fu and Shen 2020; Gil-Alana and Monge 2020; Liu, Wang, and Lee 2020; Narayan 2020; Qin, Zhang, and Su 2020). In addition, there are also studies on socioeconomic factors (Fang, Long, and Yang 2020; Qiu, Xi, and Wei 2020), pandemic mitigation (Chudik, Pesaran, and Alessandro 2020), and households’ spending and consumption (Baker et al. 2020; Eichenbaum, Sergio, and Mathias 2020). However, there is no empirical study on the impact of the pandemic on the insurance market. The fundamental function of insurance is to protect people from risks. Encountering the catastrophic hazard of COVID-19 has highlighted the importance of a well-functioning insurance market. Thus, it is of great practical and policy value to study how the pandemic affects the insurance market.

In this empirical study, we employ monthly provincial-level panel data and fixed-effects models to study the impact of COVID-19 on China’s insurance market. We find that COVID-19 had a significant adverse impact on the insurance market in the short term due to the limitation of insurance marketing channels and the suppression of household insurance demand. The development of social security and digital insurance could help alleviate the negative impact of COVID-19 on the insurance market. This study provides insights into the impacts of COVID-19 on the Chinese insurance market.

3.2 Impact on Health

Risk of Severe Illness from COVID-19. People with moderate to severe asthma may be at higher risk of getting very sick from COVID- 19. COVID-19 can affect your nose, throat, lungs (respiratory tract); cause an asthma attack; and possibly lead to pneumonia and acute respiratory disease.

During the epidemic period, travel was restricted to varying degrees, and we spent several times more time at home than before. In the serious lack of exercise, subtle changes in our body function, which is the most affected metabolic function.

Our body will take in a large amount of toxins through diet, breathing, percutaneous absorption and other reasons every day. In addition, the waste generated by our own metabolism will deposit in the body, which will increase the burden of various organs of the body, and increase the risk of thrombosis, arteriosclerosis, hypertension and other cardiovascular and cerebrovascular diseases.

When the metabolism function of human body is normal, these substances will be gradually discharged from the body. But the pressure of modern life, including lifestyle and other factors, most people’s metabolic function is disorder, resulting in toxins cannot be discharged from the body. Especially during the epidemic period, many people appeared dizziness, insomnia, forgetfulness, abdominal distension, diarrhea, constipation, palpitation, shortness of breath, arrhythmia, and other “poisoning” phenomenon. This is a serious sub-health symptom, if left alone, will gradually develop into cardiovascular, cerebrovascular, tumor and other serious diseases.

Of course, these symptoms can be gradually improved by changing daily habits. However, due to the pressure of work and life, coupled with environmental pollution and food hygiene, it is almost impossible for modern people to really restore their body to a real healthy state. Currently, we need to take some measures for health intervention.

3.3 Financial Impacts

Here, quotes the extent of the unemployment due to the epidemic and extent of the closing of the businesses and loss to the economy and the impact on employment both men and women. See if you can clearly show that women suffered higher unemployment. The financial impact comes primarily from (a) being out of work and/or getting infected of ill because of the virus.

In Keynesian economics, “uncertainty” refers to the risk that cannot be insured. The recognition of uncertain risk cannot be judged by the actual loss caused by the event. Compared with many other epidemics, the mortality rate of the new coronavirus epidemic is not necessarily very high, but the main reason why people panic is the uncertainty of the mode of transmission and the method of prevention and control of this epidemic. It is this uncertainty of the new coronavirus epidemic that leads to people’s sense of insecurity, so that people take more cautious preventive actions than other epidemics, and reduce a large number of tourism, entertainment and other activities, and correspondingly reduce a large number of consumer activities，so at this stage, in order to avoid the occurrence of small probability events, people tend to “make a mountain out of a molehill” or even “make something out of nothing”, and take a wide range of preventive measures. This will inevitably increase the cost of economic activities.

The impact of the new coronavirus epidemic on the economy is a typical external impact, which is not from the internal economy, but from external factors. This external factor has two characteristics, one is sudden, the other is caught off guard. External shocks will certainly and are interfering with China’s economic growth, and even blocking some industries (such as tourism and entertainment), but in the long run, they will not block China’s economic growth and seriously unbalance China’s economy. Therefore, the nature of the new coronavirus epidemic crisis is not an economic crisis, but a social crisis and psychological crisis caused by the social public health crisis; the industry decline reflected by the crisis will not be normal, but a temporary phenomenon of rapid economic growth. We should have a clear understanding of this. The internal cause is the basis, and the external cause is the condition. Objectively speaking, China is a big country with a huge market, and domestic demand is becoming the leading force of the economy. External changes will have a considerable impact on China’s economy, but not enough to form a dominant impact. No matter how strong the external shock is, the key lies in the anti- shock ability of various industries, the anti-shock strategies of various departments and the willpower of Chinese people to cope with the external test.

3.4 Impact on Learning

Different families have different access to the Internet, so students may meet problems when having online classes.

For students who want to go abroad for further study, they probably meet lots of trouble. Those who are in China cannot go home to reunite with their families, while those who are abroad cannot go back to China to reunite with their families.

Since the epidemic erupted during the Chinese Spring Festival, most Chinese students were not able to get together with their family members and relatives.

In terms of study, there will certainly be some delay. I do not know about it in foreign countries. In China, most universities only start classes in September. In the first half of the year, online courses are the main course. How much can I learn? How much can you learn?

As I know, lots of graduators who wanted to enter American universities can only stay at home, having online courses, because when China over comes the epidemic, other countries like Japan, USA, UK also erupted the COVID-19 disaster.

Students who only had online courses could not experience the campus life abroad or at home. And because of the different time zones, students taking foreign courses had to sleep in the morning and study in mid-night. Their time had been inversed.

Schools across the nation are closing to stop the spread of COVID-19 and in the scramble to provide at-home learning, a major problem has risen to the forefront: millions of American students don’t have reliable access to the internet.

According to recent federal data, approximately 14 percent of U.S. families with school-age children lack high-speed internet. Most of those families are low-income or live-in rural areas. While there are plenty of best practice guides available for online learning, strategies for bridging the digital divide are scarce.

The COVID-19 crisis has forced education systems worldwide to find alternatives to face-to-face instruction. As a result, online teaching and learning have been used by teachers and students on an unprecedented scale. Since lockdowns – either massive or localized – may be needed again in the future to respond to new waves of the infection until a vaccine becomes available, it is of utmost importance for governments to identify which policies can maximize the effectiveness of online learning. This policy brief examines the role of students’ attitudes towards learning in maximizing the potential of online schooling when regular face-to-face instruction cannot take place. Since parents and teachers play a fundamental role in supporting students to develop these crucial attitudes, particularly in the current situation, targeted policy interventions should be designed with the aim of reducing the burden on parents and help teachers and schools make the most of digital learning.

Impacts of COVID-19 pandemic on international higher education and student mobility Research findings of this study on Mainland China and Hong Kong university students’ attitudes toward studying abroad have approved the negative impact brought by the COVID-19 pandemic on international higher education and student mobility. The barriers for students to pursue their further degrees overseas include travel bans, visa restrictions, and campus lockdowns in destination countries, as well as students’ and their families’ worries on health and safety.

Some practical reasons like the delays of English tests also prevent students from completing the application in time (Mercado, 2020). The impacts of the pandemic on international higher education are manifested in various aspects. As to student mobility, the decrease of international students due to the pandemic will bring a significant impact to overseas higher education institutions, especially for those that financially depend on the tuitions of international students (Marginson, 2020a; Tesar, 2020). For example, the UK universities would face an approximately £2.5 billion loss in tuition income in the new academic year (University and College Union, 2020). Meanwhile, with the international students becoming scarce resources, the competitions for them will increase in international higher education. Moreover, the rate of recovery from the pandemic and post-pandemic governance will become a significant factor for destination countries to attract international students (Goris, 2020; Marginson, 2020a). This study argues that the domestic job market will become competitive because college graduates will stay for jobs instead of studying abroad. The predicted global economic recession will exacerbate this effect after the pandemic (Mercado, 2020).

3.5 Psychological Impacts

The novel coronavirus pneumonia epidemic is a crisis for the public. Crisis events have three characteristics: sudden and unpredictable, bring tension and pressure to people, the usual initial method cannot solve people’s psychological crisis, is the normal response to crisis events, and people will have corresponding stress reaction, which will last about 6-8 weeks. Stress psychological reaction can be divided into emotional reaction, self-psychological defense reaction and behavioral reaction. The most common emotional reactions are anxiety, anger, guilt, fear, depression, helplessness and so on. When dealing with stress, individuals have an adaptive tendency in their internal psychological activities, that is, self-psychological defense reaction, to extricate themselves consciously or unconsciously from troubles and relieve their inner uneasiness, to restore emotional balance and stability.

Oxford Department of Psychiatry, also found that people with a pre-existing psychiatric disorder were 65% more likely to be diagnosed with COVID-19 than those without. The researchers say this finding was unexpected and needs further investigation. In the meantime, they say, having a psychiatric disorder should be added to the list of risk factors for COVID-19.

Sufferers of “long COVID” – who are still experiencing symptoms months after testing positive – also appear to experience mental health issues, which can be triggered by a range of factors including post-traumatic stress disorder (PTSD), lethargy, fatigue and olfactory disorders such as a loss of their sense of smell.

Even for those who have not tested positive, lockdown measures have increased feelings of loneliness, isolation, restlessness and anxiety, as millions of people have been quickly forced to adapt to new realities and make drastic lifestyle changes. According to the Royal College of Psychiatrists, which represents 18,000 psychiatrists in the UK, more people are seeking crisis mental health services as a result of COVID-19.

Pandemics have had a detrimental impact on the mental health of affected populations throughout history. For example, The Lancet reported that outbreaks of the Ebola virus caused “widespread panic and anxiety, depression resulting from the sudden deaths of friends, relatives, and colleagues, and stigmatization and social exclusion of survivors.” And the Spanish Flu pandemic of 1918-1919 had a long-standing impact on the mental health of survivors as a result of the massive and sudden loss of life which plunged many into a chronic state of helplessness and anxiety.

Even though the link between pandemics and mental health is well-documented and researched, there is still more research needed to fully understand the causes and solutions. Scientists are still examining how COVID-19 affects the brain and the central nervous system to determine how much that plays a role in receiving a diagnosis vs the trauma of suffering with symptoms, and in some cases narrowly avoiding death.

3.6 Are there any positive impact as well?

Only 15% more – not much Appreciation and desire for a simpler uncomplicated life

Rapid medical advance and technology advancement. Young children may learn new skills by having to use online learning tools.

1. Protection of wild animals and punishment of crimes of eating wild animals. This will undoubtedly be beneficial to the harmony between man and nature in the future.
2. Exposed some social problems, need to establish, and improve a set of disaster prevention and control system.
3. The epidemic situation in China has been basically controlled and the national confidence has been improved.
4. China takes the lead in controlling the epidemic in the world, aiding foreign countries, deepening international cooperation and enhancing international status.

4. Impact on education at the school level: long-term effects.

4.1 What is the nature of the possible long-term effects

Besides the short-term effects noted above, it is easy to imagine the lingering or the long-term effects of COVID_19 pandemic. This section is going to note several of these long-term effects.

4.2 Long Term Impact on Employment Prospects Employment issues

Employment issues

In China, campus recruitment is major means for students to find employment. Campus recruitment is divided into autumn recruitment and spring recruitment. The autumn recruitment is generally aimed at students who have no plans to go for higher degrees, and want to work directly after graduation. Spring recruitment is aimed at those students who are otherwise accomplished, but performed poorly in postgraduate entrance examinations and hence need to find employment. For prospective graduates in 2020, the adverse impact of the epidemic on employment prospects is a matter of great concern. For students who participated in the autumn recruitment, whether they can graduate as scheduled becomes an important factor in deciding whether they can successfully enter the job market. Meanwhile, domestic and large enterprises have been adversely affected by the epidemic and whether demand for students as employees will accordingly shrink is causing great uncertainty.

For students waiting for the spring recruitment, this period is also very difficult. The spring recruitment was canceled due to Covid-19. Whether it will be reorganized after the full reopening of schools depends on too many unknown factors. Most of the students waiting for the spring recruitment either participate in the postgraduate entrance examination or take the public examination. Nevertheless, due to the epidemic, the timing for these tests has also become uncertain. It is feared that China may experience the largest spurt in rates of graduate unemployment in the last twenty years.

Employment

In China, campus recruitment is the main means of College Students’ employment. Campus recruitment is divided into autumn recruitment and spring recruitment. The target of autumn recruitment is generally the students who want to work directly after graduation without further education plan. Spring recruitment is targeted at students who have other achievements but do not perform well in the postgraduate entrance examination, so they need employment. For the fresh graduates in 2020, the adverse impact of the epidemic on employment prospects is a very noteworthy issue. For the students who participate in the autumn recruitment, whether they can graduate on time becomes an important factor to decide whether they can enter the job market smoothly. At the same time, large domestic enterprises have also been adversely affected by the epidemic. Whether the demand of students as employees will shrink correspondingly also brings great uncertainty.

It’s also a tough time for students waiting to be recruited in the spring. Spring recruitment was cancelled because of covid-19. Whether the school will be reorganized after the full reopening depends on too many unknown factors. Most of the students waiting for spring enrollment either take the postgraduate entrance examination or take the public examination. However, due to the epidemic, the timing of these tests has also become uncertain. There are concerns that China may experience the biggest surge in graduate unemployment in nearly 20 years.

4.3 Long term Impact on Learning Opportunities/education

Impact on studying abroad

According to news from the Ministry of Education, there are about 1.6 million overseas Chinese students, and currently there are still about 1.4 million abroad 5. It had seemed earlier that the interest in studying abroad among Chinese students was on the rise with 413,900 Chinese studying abroad in 2013 and 662,100 in 2018 6. The countries to which students go for higher education has diversified over the last few years. 7. The impact of the epidemic on the overseas study programs remains uncertain because much will depend on the decisions of universities in other countries to invest in higher education as well as their policies on migration. The global outbreak of the epidemic may lead to the postponement of enrollment of foreign students in many universities posing a dilemma for those students who have already been admitted. Government-sponsored overseas education is also greatly affected, including joint training of students and visiting scholars. Since the trajectory of the coronavirus remains uncertain many programs of training that were premised on global partnerships might need to be suspended.

In summary, the delay of graduation and rise of unemployment is inevitable. Overseas students who have already found a job, similar to domestic students, also face the risk of delayed entry into the work force or the cancellation of their positions. The long-term effects of such delays and uncertainties on the students who were accustomed to thinking that higher education will give them a secure future will need to be carefully studied in the coming years and plans for mitigation of these hardships will be an important project for the government and the universities alike.

The impact on postgraduate education inland

Despite the increasing interest in studying abroad, the poor management of the Covid-19 pandemic in many countries has made some families look at the prospect of overseas studies for their children more critically now. Accompanied by the pressures for adequate jobs mentioned above, the prospective graduates might prefer to take postgraduate admission examination, increasing pressure on domestic graduate admissions. The number of participants in the postgraduate entrance has exceeded 3.41 million in 2020 8. The epidemic will undoubtedly make the situation worse next year.

Apart from the students, the Ministry of Education and the colleges are under great pressure as they plan for the organization of examinations for the postgraduates, the written tests that have to be administered to such large numbers of students, and little experience with online testing methods. The biggest challenge is going to be how to alleviate the unemployment problem for the educated youth and restore faith in the Chinese economy.

As we all know, students have diversified to receive higher education. They will go to various countries for further study. The impact of COVID-19 on overseas study projects is still uncertain because each country’s policies are different. The global explosion of the new crown may cause many universities to delay their applications, and the students who have already been admitted are in a dilemma because they can only take online courses after paying the same tuition fees. As the trajectory of coronavirus transmission is still uncertain, some global activities may be forced to suspend.

Graduation will be delayed and unemployment will rise because of the epidemic. It can be said that graduates at home and abroad are facing the same problem. The long-term impact of these delays and uncertainties on students who are used to thinking that higher education will bring them a safe future needs to be carefully studied in the next few years. The plan to alleviate these difficulties will be an important project of the government and universities.

Despite people’s enthusiasm for studying abroad, the poor management of covid-19 in many countries makes some families prefer boring exam-oriented education to international education. If it goes on like this, it may be worse.

In addition to students, the Ministry of education and universities are also facing great pressure when planning to organize postgraduate examinations. However, there is little experience in online examination methods for large-scale examinations in China. The TOEFL or AP test for international students can be implemented online. It is clear that the university entrance exams in China will never be online, because many would cheat if in that case. The biggest challenge will be how to alleviate the unemployment problem of educated youth and restore confidence in China’s economy.

5. Concluding Remarks and Future Research

In this paper, I have examined and analyzed the nature of the impact of COVID-19 on school learning in China, a topic not only of great interest to me personally, but also of great significance for the world at large especially China.

The sudden outbreak of COVID-19 has brought many disadvantages to countries around the world including China. Focusing on China, its health, well-being, economy, transportation, and schooling along with many other sectors have been adversely affected in the first year and a half since this outbreak. The focus of this paper is the impact on school learning both in the short as well as the long run.

After an introduction in the first section, I present the history of the pandemic to provide the proper perspective to the issue at hand. The following two sections constitute the heart of this research study where I explore the short and the long-term impacts of COVID-19 on school learning China.

In terms of the short term, this paper finds that major impacts have been on the health, psychology, economics, and education part. People’s health conditions are weakened by this pandemic period, and they became afraid of this. The rate of unemployment rose for may shops closed because of the epidemic. Education was quite terrible because students could not go to school as usual. They stayed at home for months until the situation got better. While having online classes, they met a lot of problems.

On the other hand, in terms of the long-term, this paper finds that major impacts have been in terms of good effects on China, like, cities in China tried to work together to overcome this difficulty. Chinese medical power became stronger because many people were waiting to be cured, etc.

Future research.

Of course, it also promotes the rapid development of science and technology in human society. In the short term, the situation of students in class has changed greatly, from face-to-face teaching to online teaching. Although this is the best way during the epidemic period, it still has disadvantages. Moreover, COVID-19 has also aroused many students’ thinking. They organize related speeches, research, and other activities. In the long run, the employment of students is facing great difficulties. The life trajectory of many graduates may be changed because of this epidemic. The postponement of recruitment and examination, the postponement of graduation, and the change of teaching methods are unprecedented. Many families are also hesitant about whether they should study abroad or stay at home.

To study these topics, I investigated a lot of materials, read a lot of literature, and found many useful articles to help my research. Some lovely pictures and scientific charts have also become a great help. However, there are still some deficiencies in the study, such as the limited information, I did not conduct a questionnaire survey of the surrounding population, nor did I conduct further data analysis on the charts.

In future studies, I may not be confined to domestic surveys. I will look at the whole world and study the different effects of COVID-19 on different countries and different policies.

References

1. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/asthma.html
2. https://www.kqed.org/mindshift/55608/14-tips-for-helping-students-with-limited-internet- have-distance-learning
3. http://www.oecd.org/coronavirus/policy-responses/strengthening-online-learning-when- schools-are-closed-the-role-of-families-and-teachers-in-supporting-students-during-the- covid-19-crisis-c4ecba6c/
4. https://www.researchcghe.org/perch/resources/publications/wp54to-publish.pdf
5. https://americanethnologist.org/features/collections/covid-19-and-student-focused- concerns-threats-and-possibilities/analysis-on-the-influence-of-epidemic-on-education-in- china
6. https://www.tandfonline.com/doi/full/10.1080/1540496X.2020.1791074?scroll=top&need Access=true&
7. https://www.gavi.org/vaccineswork/anxiety-depression-and-insomnia-impact-covid-19- mental-health?gclid=EAIaIQobChMI-Jitqu2H8AIV4DytBh3FNwJ2EAAYASAAEgIUoPD_BwE

About the author

Helen Zhang

The post The Impacts of COVID-19 on School Learning in China appeared first on Exploratio Journal.

]]> https://exploratiojournal.com/sars-data-analysis/?utm_source=rss&utm_medium=rss&utm_campaign=sars-data-analysis Mon, 30 Aug 2021 03:57:20 +0000 https://www.exploratiojournal.com/?p=1134 Seoyeon Cho

The post SARS Data Analysis appeared first on Exploratio Journal.

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Author: Seoyeon Cho
February 9, 2021

1. Introduction

Since the beginning of 2020, the economies and social activities of global markets ground to a halt due to the rapid spread of the novel contagious disease caused by Coronavirus from Wuhan, China. Scientists named this virus as ”SARS CoV 2” and the disease it causes as ”Coronavirus Disease 2019” (COVID-19). SARS, also known as Severe Acute Respiratory Syndrome, is a disease that can be easily spread through surfaces and saliva. It was first identified in February 2003, and has reemerged since then. The World Health Organization (WHO) first declared the new outbreak as a public health emergency of international concern in January, 2020 but soon declared the crisis a pandemic as the virus spread exponentially in March, 2020. COVID 19 has now spread to more than 200 countries in the world and has caused 1.9 million deaths.

2: R script

This is an R chunk with no plots:

>sarsdataset <- read_csv("sars_2003_complete_dataset_clean.csv", + col_types = cols(Date = col_date(format = "%Y-%m-%d")))

>#View(sarsdataset)

>names(sarsdataset

[1]	"Date"	"Country"
[3]	"Cumulative number of case(s)" "Number of deaths"	
library(readr)
[5] "Number recovered"

names(sarsdataset)[3]<-"CumNumber"

names(sarsdataset)[4]<-"Deaths"

names(sarsdataset)[5]<-"Recovered"

#install.packages("tidyverse")

library(tidyverse)

#install.packages("ggplot2")

library(ggplot2)

str(sarsdataset)
tibble [2,538		5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ Date	:	Date[1:2538], format: "2003-03-17" "2003-03-17" ...
$ Country	:	chr [1:2538] "Germany" "Canada" "Singapore" "Hong Kong SAR, China" ...
$ CumNumber:		num [1:2538] 1 8 20 95 2 1 40 2 8 0 ...
$ Deaths	:	num [1:2538] 0 2 0 1 0 0 1 0 2 0 ...
$ Recovered:		num [1:2538] 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "spec")=

.. cols(

..   Date = col_date(format = "%Y-%m-%d"),

..   Country = col_character(),

..Cumulative number of case(s)  = col_double(),

..Number of deaths  = col_double(),

..Number recovered  = col_double()

.. )

>sarsdataset$Country <- as.factor(sarsdataset$Country)

# Explore data for individual country

>

>list_Country<-levels(sarsdataset$Country)

>sarsdataset$Country <- as.factor(sarsdataset$Country)

>rev(sort(table(sarsdataset$Country)))[1:5]
Thailand	Singapore Hong Kong SAR, China	
96	96	96
Germany	China	
96	96	
>Country0<-list_Country[11]
>countryindexes<-c(6,11,27)
>countryindexes<-c(1:length(list_Country))
>list_Country[countryindexes]
[1]	"Australia"	"Belgium"	"Brazil"
[4]	"Bulgaria"	"Canada"	"China"
[7]	"Colombia"	"Finland"	"France"
[10]	"Germany"	"Hong Kong SAR, China" "India"	
[13]	"Indonesia"	"Italy"	"Japan"
[16]	"Kuwait"	"Macao SAR, China"	"Malaysia"
[19]	"Mongolia"	"New Zealand"	"Philippines"
[22]	"Poland"	"Republic of Ireland"	"Republic of Korea"
[25]	"Romania"	"Russian Federation"	"Singapore"
[28]	"Slovenia"	"South Africa"	"Spain"
[31]	"Sweden"	"Switzerland"	"Taiwan, China"
[34]	"Thailand"	"United Kingdom"	"United States"
[37]	"Viet Nam"		

With Sweave files, there is only one figure per chunk

countryindexes<- c(6)

for (j in countryindexes){

+   Country0<- list_Country[j]

+ sarsdataset0<- sarsdataset[ sarsdataset$Country== Country0,] + #print(sarsdataset0)

+ #ggplot(data=sarsdataset0, aes(x=Date, y=CumNumber)+ geom_line()

+

+ par(mfcol=c(2,2))

+ plot(sarsdataset0$Date, sarsdataset0$CumNumber, type="l",

main=Country0,

xlab="Date",

ylab="Cumulative Number of Cases")


plot(sarsdataset0$Date, sarsdataset0$Deaths, type="l",

main=Country0,

xlab="Date",

ylab="Deaths")

+

plot(sarsdataset0$Date, sarsdataset0$Recovered, type="l",

main=Country0,

xlab="Date",

ylab="Recovered")

+

plot(sarsdataset0$Date, sarsdataset0$Deaths/sarsdataset0$CumNumber,type="l",

main=Country0,

xlab="Date",
ylab="Death Rate")

}

3. From Google Docs

Background information

The purpose of this research paper is to critically understand the past situation of the SARS outbreak in 2003 with the objective of projecting the near future conditions of the COVID 19 crisis. This effort com-pares and contrasts quantitative information available from the two crises. It now has been almost a year since the outbreak started with significant impacts on human beings all over the world but to date, there have been no significant improvements or comprehensive solutions in countries of the world. The critical, analytical understanding of the SARS outbreak presented in this paper, improves the potential to better predict possible outcomes of the COVID 19.

4. Research Topic

4.1 Issues

Contrasting each country and analyze the SARS impact * examining the progression of disease in each country Interesting points that may arise: * Death rates may be higher in lower income countries * World Health Organization classifies countries by geography and by income level; perhaps there are patterns in expo-sure levels and death rates * When comparing progression of disease by country, accounting for different population sizes of the countries (focus on percentage increases/changes) and possibly residential density of countries. * Degree of government control on quarantines and isolation of active cases.

4.2 Connection between the covid and SARS

• Countries that experienced SARS were possibly better prepared to deal with COVID-19 (e.g., testing protocols and other .)

SARS – data, analysis (organized discussion with logical flow) (list the actual number on the data)

5. Plots from GGPlot

6. Analysis

The similarities between the countries that had most death rates.

This graph indicates the death rates of different countries along the time due to the impact of SARS outbreak back in 2003. One can easily notice the highest deaths has been recorded by China, second by Hong Kong, third by Taiwan, and fourth by Canada.

One can also notice that these four countries can be grouped into two parts , where one has recorded relatively significant number of deaths and another with less significant death rate. China and Hong Kong can be grouped as ”most death rate” group whereas Taiwan and Canada can be grouped as ”non-significant death rate” group. This separation could have been done easily because of the distinct difference in the number of deaths.

The very distinct similarities that ”most death rate” group shared was the exponential growth of number of death from April to late May. The green and blue graphs showed almost an identical exponential growth during that time span and slowly stopped increasing and remained almost constant from June to July with constant death rate of 380 and 300 for China and Hong Kong respectively.

Other than the information from given graph, one can also assume that the ”most death rate” group were similar in a sense that they were both sharing / touching the border of China. This may be very obvious and does not seem like an important fact but can’t be ignored as both SARS and COVID-19 were emerged in China and spread to other countries rapidly. (Add another graph with percentage deaths (death rate) as a percent of cases to compare countries)

The similarities between the countries that did not have many cases From the figure 1, one can also notice the similarities between the countries that did not have such high death cases; Taiwan and Canada. It is that compared to ”most death rate” group, the increase of the death case of these two countries was not very exponential but quite steady. Canada showed very linear increase of death cases along time and Taiwan with small exponential growth during May but not as significant as that of China or Hong Kong.

The another distinct similarity that one can know is that these two countries, Canada and Taiwan, are not touching China’s national border directly. There may be a lot of Chinese immigrants in Taiwan and Canada but they both are not directly touching China in terms of country’s borer line.

7. Worst Experienced Country

In order to pin down which country experienced the worst outbreak, one has to define ”worst experience” first. This is important because one could think the country with most death cases had the worst experience whereas another can argue that the worst experience should be based on the percentage of number of death per national population. In this report, the ”worst experience” will be based on the total number of SARS case. This is because the absolute amount of people who suffered from this disease matters more than the numbers of death rate or the number of deaths. According to figure 2, it is clear that China had the highest cumulated number of SARS case among 4 countries with overwhelming 4800 cases, almost the double of Hong Kong’s SARS case

8. Worst Impacted Country Among Top Rated Countries

Just like how ”worst experienced” was defined in the past paragraph, ”impact” will be defined as how much of a shock SARS brought to a country. In other words, ”impact” will be measured based on the percentage of death per the nation’s population.

Taiwan: 24 mil Canada: 38 mil Hong Kong: 7.5 mil China: 1.4 bil

Even without the detail calculation, one can see that the death / nation pop-ulation is the highest for Hong Kong as it has the smallest number of population but almost the same number of death cases according to figure 1. Therefore, one can argue that Hong Kong had the worst impact among the four given countries.

9. Seriousness of SARS Crisis

Unlike other diseases like cancer or heart disease, the problem with SARS was the fact that this disease was so contagious. This is important because person with cancer or heart disease can live their normal life even after recognizing they have those diseases. However, in case of SARS, the other people who do not carry the disease cannot function properly because of the fear that this unseeable virus might be traveling as an aerosol in mid air and infect them. This fear cannot be sorely explained or quantified with graphs and numbers on figures. Which also explains why the numbers and the figures can not properly explain the seriousness of SARS. SARS did not only kill people but killed the economics of not only these four countries but the many other countries who were closely related to China. Obviously, the death of many people is a deep pain that can not be forgotten but the economic damage that SARS caused was very significant.

From figure 3, one can distinguish the differences and similarities between four countries.

First of all, Canada showed a very distinctive characteristic that other 3 countries never shown. During the late February, the deaths/cumnumber of Canada spiked all the way up to 0.3 but it soon died to 0.1 and slowly increased and remained constant at about 0.15. This kind of instant spike, decrease and remaining of the rate has not been shown in any other countries. The other two countries, Hong Kong, and Taiwan, never shown an instant spike like Canada did but rather slower exponential increases. However China has shown a very different rate graph as well. China was the only country that had linear increase among 4 countries in figure 3.

For the death rates why does death rates stop dramatical increases during June?

From the figure 1, one can see that the exponential growth of death rate for China and Hong Kong has been remained until early June but the growth has significantly died from June. A lot of reasons could be the answer to this dra-matic ease of SARS such as the change of weather, countless efforts of scientists, researchers, and doctors to stop the disease. As all four countries’ death rate has decreased dramatically as time went by, one could assume that the obvious change of weather (from winter to summer) along time has helped the human population to stop the contagious disease, SARS. Another reason for a sudden ease of exponential growth of SARS would be the fact that all four countries’ governments were well aware of the situation with the help of WHO and well managed to stop the spread of virus.

There could be multiple reasons for a stop of dramatical increase during June. One is the change of weather from winter to summer and the increased temperature. Another is the cumulated effort of human beings to stop the virus with research for cures and governments’ desperate effort to stop people from going out.

The country that had most severe crisis and what happened to their country Among these four countries, Hong Kong had the most severe crisis with the highest percentage of both death and cumulated case number per nation’s pop-ulation. The death rate of SARS case in Hong Kong were usually around 20% but it went up close to 55% among elders.

Because other normal respiratory diseases’ death rate were about 1% back then, the SARS did not seem to be a significant issue at the beginning of the outbreak. However, the death rate was significantly high specifically in Hong Kong since they were most vulnerable to the disease as they had the highest portion of elders among the four given countries in the data. This was a very significant fact that changed the SARS’ outcome back in 2003. Furthermore, Hong Kong was not very well prepared for these kind of outbreaks as they have one of the highest population density in the world which meant Hong Kong was the best possible option for SARS outbreak to exponentially damage the country and the economics of it.

Due to SARS outbreak in 2003, Hong Kong’s tourism industry had to face the worst period so far. As you could imagine, with this outbreak ongoing in Hong Kong, no one in other countries would have voluntarily wanted to visit and explore Hong Kong because of the SARS. According to Paul Chan Mo Po, finance minister of Hong Kong in 2003, the number of foreign visitors decreased to 40% compared to that of 2002. This kind of economic impact did not only hurt the tourism industry but the local and international economics of Hong Kong as well. This damage was more severe for Hong Kong since they had such a dense country compared to others like Canada or China in terms of both landscape and population density. It completely frozen the economics and the country and took years of effort for Hong Kong and their people to recover from SARS.

10. Prediction

Compare the conclusion of sars and the current cove pandemic SARS could have been completely controlled within only 6 months of time with international effort back in 2003 but the current ongoing COVID 19 pandemic already has surpassed 12 months of time and does not seem to end in the near future. This could be explained with the fact that back in 2003, the world was not as connective as it is now. For instance, traveling was treated as a huge privilege back in 2003 and was not a very common. However, as the world started to globalize exponentially, traveling was not a big privilege for mange people and became common for everyone in 2021. One can believe that the significant reason why SARS could have ended much faster, only in 6 months, compared to COVID was because it was not as globalized back in 2003 as it is now in 2021.

SARS ended only in 6 months and only caused 774 deaths but the current COVID already has killed more than 1.9 million people internationally all over the world. It is very distinct that the SARS was only spread to few countries whereas COVID has been spread to more than 200 countries. This may be the reason why COVID is not concluding like how SARS has concluded, the exponent globalization of the world.

11. Comparison Between SARS and COVID 19

SARS and COVID 19 share the very same virus called coronavirus which is very contagious and causes respiratory diseases. These two also are very similar in a sense that these diseases are much more dangerous for elders or one with existing diseases like obesity, cancer or respiratory disease than others.

It is also similar how the outbreak originated from China and spread to other close countries. As Hong Kong is literally next to China and easy to travel, the virus was able to spread in Hong Kong rapidly just like how COVID19 had spread in Hon Kong as one of the earliest countries in the first several months of the outbreak.

12. Conclusion

In conclusion, the SARS and COVID 19 share the very similar characteristic in a sense that both of them are caused by coronavirus, are very contagious, and are dramatically more dangerous to elders and people with existing diseases. Back in 2003, people were able to successfully stop the SARS as the world was not as globalized as it is now with efforts of scientists, researchers, doctors, and the governments as the virus only spread to few countries. However, the COVID19 now has been spread to more than 200 countries and caused almost 2 million deaths. From the close observation of SARS case, one can conclude that it has surpassed singularity point but the time, effort from researchers and governments will stop this COVID19 in foreseeable future.

13. References

Seladi-Schulman, J. S. S. (2020, April 2). COVID-19 vs. SARS: How Do They Differ? Healthline. https://www.healthline.com/health/coronavirus-vs-sars

Severe Acute Respiratory Syndrome (SARS). (2019, November 1). World Health Organisation. https://www.who.int/health-topics/severe-acute-respiratory-syndrome

E.P.M.K.U.G.D.H.H.N.P.F.C. (2020, July 3). Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics. The Lancet. https://www.thelancet.com/journals/laninf/article/PIIS147 3099(21)00054-2/fulltext

About the author

Seoyeon Cho

The post SARS Data Analysis appeared first on Exploratio Journal.

1 Introduction

1.1 Overview of COVID-19 

COVID-19 was first found in Wuhan, China. COVID-19 was first thought to be pneumonia, but was later discovered to be a new strain of coronavirus. The disease was a respiratory virus that could be spread through droplets in the air. As a result many countries created guidelines to protect people from spreading the virus. One of the most impactful guidelines put into place were shelter in place orders. Businesses closed down and economies suffered greatly. At one point, oil had negative value due to lack of consumption. 

The virus spread from China to Europe and other countries such as the US. Mainly due to the high amount of travel between other countries and China. In February 2020, the first COVID-19 case was detected in the US. Some countries, like Vietnam handled the virus better than others by imposing strict quarantine and stay at home orders. On the contrary, some countries such as Italy suffered greatly due to lack of preparation and other factors. The world surpassed 1 million COVID-19 deaths in late September 2020 and the cases continue to climb into 2021. 

COVID-19 is a disease that has had an unprecedented effect on the world. The most similar pandemic happened over a century ago with the Spanish Flu. The disease has taken its toll on every single person with people losing their loved ones, others suffering financially, and many lifestyles changed to reduce the spread of the virus. 

Unfortunately COVID-19 continues to infect more people and there seems to be no peak for the cases yet. New mutations of the virus are being discovered which threaten more people and more lives. At the same time, governments are sponsoring vaccine development and leading healthcare experts are battling against the virus to develop and distribute a vaccine, and possibly a cure. 

1.2 Goals of the paper 

In this research paper, data of cases over time for separate countries will be analyzed. The goal of the analysis is to discover similarities between countries and general trends of the spread of the disease. From these trends, people can then understand what caused the different rates of spread and how governments can take better action and build on the successes of other governments to mitigate the spread of future viruses. 

2 Methodology 

2.1 Data 

The data used in this study was taken online from data pulled from the John Hopkins University Center for Systems Science and Engineering. The dataset is about the 2019 Coronavirus pandemic, also known as COVID-19. The dataset contains data dating back to the beginning of the pandemic in late January 2020 and continues to be updated nearly daily at the time of this research paper’s publication, mid February 2021. Data from 191 countries is collected as well as some that include specific provinces within the countries. The data is grouped using date and country and has the total number of confirmed cases, deaths, and recoveries for each date. 

Changes to the data were needed in order to effectively work on the data and fully understand what the data presented. In order to accomplish this, several new datasets were created using the data collected. Firstly, the data was organized so that the number of confirmed cases, deaths, and recoveries could all be listed in one row, or observation. This presented data that had simple the cases that occurred on a certain date. This created the dataset ”dfdaily”.  

In addition, cumulative case counts were also needed to analyze the data. Contrary to the ”dfdaily” dataset, the ”dfcum” dataset has observations that represent the total case count that the country had on the specific date.  

Moreover, a dataset representing the total global cases by date is also helpful in the analysis. This dataset would require the countries to be dropped and would have the confirmed cases, deaths, and recoveries for the whole world by date, as well as the cumulative counts of each of them. This dataset would be named ”dfglobal”. 

Finally, a dataset that represented the total cases was created to understand what the countries’ confirmed cases, deaths, and recoveries were in total. This dataset, ”dfrate”, essentially is the observations in ”dfcum” that have the latest date value. Also, a death rate was calculated for each country based on the case count.  

3 Data Analysis 

3.1 Global Analysis 

To begin, take the data for all the countries collectively and see the world’s cumulative data. Some of these would be cumulative confirmed cases, deaths, recoveries over time. Instead we can also look at the cases from each day or the cumulative death rate for each day.  

The graph above shows the total cumulative cases over time. The red line represents the deaths, the orange line, active cases, the green line, recoveries, and the blue line, total confirmed cases. 

It appears that the confirmed cases, deaths, and recoveries all seem to grow relative to each other. There appears to be a small growth that gradually grows to become exponential in all the graphs. Around April 2020 seems to be when the cases begin to grow rapidly which is around 4-5 months after the discovery of the virus. In addition, the active cases first begin to be larger than the recoveries in the time period following April. However this seems expected since the average time of recovery from the virus is around 2-6 weeks depending on the severity of the case. 

Something that is concerning is that there still seems to be no peak of the curve. This is worrying because there always have been reminders about ”flattening the curve”, yet the peak has not arrived yet. The data shows that the cases will continue to grow, possibly even more exponentially into the future, with no real sign of it flattening out and beginning to decline going into 2021. 

The virus was predicted to get better during the summer months in the US, however the data seems to contradict that statement. Around July, the confirmed cases seem to take a bend upwards and grow more rapidly than before. The cause might be due to people wanting to enjoy a summer vacation despite the conditions of the virus. 

Next, looking at the global death rate alongside global deaths over time might reveal some more information about the virus.  

The sudden rise of death rate around April seems unexpected. However the total confirmed cases also began to grow rapidly around April as seen in the last graph. The reason for the spike in death rate is most likely due to the overflow of COVID-19 patients in hospitals. Without enough medical supplies or facilities, there would have been many people with the disease not getting adequate care. Because of this, the amount of deaths compared to the number of cases would have gone up, which increases the death rate. 

The rise in death rate is also seen in the cumulative deaths over time. The deaths begin to grow more rapidly after April which mirrors the behavior of the death rate graph to a certain extent. After May the death rate seems to hit its peak around 7% and begins to slowly drop down to the predicted 2-3% death rate. This is probably due to the increased political activity of some nations to further understand the virus and develop better treatment patterns. In addition, many more medical facilities were created which would have helped with the overcrowding in the hospitals. 

Even though the death rate has dropped to reach the 2-3% predicted death rate, the deaths continue to grow and have begun to grow at a larger rate around November 2020. This is due to the increasing number of confirmed case. Even if the death rate sits at something as low at 2%, the exponentially increasing number of confirmed cases will still continue to have an impact on the number of deaths. 

As seen before, there have been changes in the rate that the cases grow. These are seen in the graphs where the slope of the line seems to change. For example, the confirmed cases seems to have a change in rate beginning in April, a change before June, and one later in October. As stated before, the change in rate in April was most likely due to the rapid spread of the virus from the small number of people that were not contained. Later in June, the change in rate is likely due to people wanting to enjoy a summer vacation. One change in rate that has not been touched on is the change in October. 

The confirmed case graph changes to become a much steeper line than before and the active cases seem to mimic its activity, but with a more noticeable increase in rate. Back during the early months of the virus, news suggested that the virus would tone down and spread would be smaller in the summer. Ironically, this seems to be wrong as the rate increases when heading into the summer months. However, the main point that the news stressed was that the virus could rebound during the winter due to colder temperature. This seems to be the explanation for the increase in rate for confirmed COVID-19 cases. 

Moving on, the daily case count can also be analyzed to have a deeper understand of the data. We begin by looking at the new confirmed cases for each date.  

The graph for this looks wild with the confirmed cases jumping up and down over and over again. However this is due to some countries such as Botswana, only reporting cases every couple of days. Other countries also have widely varying values over a couple of days. This might be due to the speed of communication between certain parts of the country or other outside factors preventing every medical center from reporting data on a daily basis. 

What is worrying, is the massive spike before January that does not fit the trend whatsoever. More specifically, this is on December 10, 2020. Upon closer inspection, it appears to be an observation of Turkey that records over 800,000 confirmed cases on that day. This completely contrasts the other data in adjacent dates that hover around 30,000. Furthermore, there appears to be another observation from Turkey a little later that reports over 1,000,000 recoveries in one day. This is absurd, albeit good, if the data is correct. However ignoring data points like this will help make our graph clearer.  

In this graph, the line seems to be taking ”steps” where there are steeper portions and flatter portions. For example, the graph is flat up until a little before April. Before April, the graph takes a ”step” as the number of daily confirmed cases increases a lot. However afterwards, the graph become relatively flat. Then before July, the graph once again takes a step. This time, the step appears to be more gradual and plateaus around 250,000 cases daily. Then there appears to be a massive step after October which brings the average confirmed cases daily to over 500,000. Notice that the steps occur at nearly the same time frame as the increases in rates for cumulative cases. This suggests that the steps in the daily confirmed cases cause the change in rate for the cumulative cases.

Furthermore, it appears that the oscillation in the graph appears very small when starting out, but grows very large towards the beginning of 2021 where it has a change in over 125,000 every time it jumps from low to high. This is most likely just a result of the increasing number of cases. Since cases increase, there will be a larger number of cases that graph changes by when adding the data that is not updated daily.

3.2 Grouping countries based on similar factors 

Grouping countries together can uncover some trends that cannot be seen when looking at all the data cumulatively. First grouping the top 5 countries with the highest case count.  

There appears to be a very large difference between the country with the highest cases and the other 4 countries. The gap is so large, that it seems like the total cases of all the other 4 countries could sum to the amount of cases the top country has. Unfortunately, it is the US that has the greatest number of confirmed COVID-19 cases. The graph suggests that it is greater than 25 million as of February 2021. 

The lines themselves also tell a story. For example, the line representing the US is the steepest and the steepness seems to begin after October. This lines up with the global data. However it is hard to tell if the US is following the global trend, or if it is creating the global trend. 

Taking a look at the second highest case country, India, we find a much more mellow graph. The growth of cases in India around August 2020 seem to have approached a growth similar to the US’s in late 2020. However, the growth seems to have reduced and a peak of the curve seems to be emerging. The rest of the countries seem to still be growing in case numbers without having reached their peaks.

Another fact that is not promising for the US is the ratio of cases to total population compared to other countries. The population of the US is about 330 million and the total cases is above 25 million. This means that around 13% of the total population has been infected by the virus. Compare this to India which is considered to be worse off medically and economically in some parts than the US. India’s population is over 1 billion people, yet their total confirmed cases is just above 10 million. This is about 1% of their population. 

The US, with nearly 3 times the land mass of India has over 10 times the percentage of their population infected with COVID-19. India being a very crowded and even poor country, seems to have done much better than the US when facing this pandemic. The question become why this is the case. 

Perhaps the data is rather inaccurate compared to the US because of lack of testing centers in the India compared to the US. Instead, we might look towards a more ”trustworthy” nation such as the UK and compare it to the US. The population of the UK is around 67 million and the total confirmed cases is about 4 million. This means about 17% of the population has been infected by COVID-19. 

The idea that the US has been doing terrible with COVID seems to be rather weak. Other countries such as the UK seem to be doing about the same, or even a little worse. 

Looking at the current active cases can reveal some more information. 

It appears that the US has nearly 20 million active cases as of February 2021. This translates to about 4/5 of the total cases being active cases. Similarly, the UK has around 3.5 million active cases. Comparing this to the total cases the UK has results in around 7/8 of the total cases being active. This is by no means good, but it stands out very much compared to other countries such as India, who seem to have hit their peak cases before October 2020. 

The difference in Active case count suggest that the results of the other countries could possibly be inaccurate from reality due to logistical problems or other factors tying into the collection of the data. 

3.3 Comparing cases between continents 

When dividing the data into groups that share similar factors, the idea of global region comes to mind. A general grouping based on the latitude and longitude values in the data help group the countries by continent. 

To get a general idea of the data, we can try plotting the total confirmed cases over time for a specific continent, in this case, Europe.  

Starting around April 2020, a split appears in the graph. A few countries begin to experience a sizable increase in total cases, while the rest of the countries seem to stay on a slower rate of increase. One could infer that the countries that have a greater increase were more connected with the rest of the world and therefore the cases increased as the whole world began to get infected. 

The graph seems to stay rather constant until October 2020 which suggests that the governments were able to contain the virus and quarantine those who were infected. Once Autumn hits, the cases begin to grow rapidly for those who felt a larger initial increase, but the more ”dormant” countries still experience some change in the growth rate.  

The average cases over time in Europe seem to reflect the graph of all the countries rather well. The increase in cases around April 2020 seems to have the same curve and the increase in October matches the previous graph rather well. 

Taking a look at Asia might reveal some similarities between the continents. 

First of all, the line that skews most of the graph is India. In order to have a better understanding of the Asian countries, filtering out India is a must. 

The country that has cases way before April 2020 is China, the country where the virus originated. The cases seem to grow very rapidly at the start, but they seem to have stabilized the condition very quickly. This may be due to the power of the Chinese government that keeps people quarantined and safe even if people try to resist. In other places such as the US, the government has been more lenient with the COVID-19 policies which have cause people to actively spread the virus on their own. 

Interestingly, the cases in Asia seem to begin a month or two after April 2020. This is contrasting the growth in cases that happened around early April 2020 in Europe. The cases also do not seem to grow as drastically when transitioning into October 2020 in Asia as they had in Europe. 

Including the country India almost quadruples the average for the continent so removing India helps in more accurately representing the rest of the countries. The most noticeable part of the graph is how linear is compared to the average for Europe and even the average with India. This suggests steady growth over time for the cases in Asia. Perhaps not enough government action has been taken, but the population, or the population density, or some other factor is keeping the rate of increase low. However if the graph was linear like this and very steep such as the graph towards the end of 2020 for Europe, a problem would appear. This begins to shine light on how such factors such as government support or population density can affect how the virus spread.

If we isolate the countries with the most cases, we get this graph, once again removing India. It appears that Indonesia and Iran have the most confirmed cases. The rest of the countries seem to be clustered around two points as of January 2021. One group of countries sits around 500,000 cases, where the other group seems to be around 250,000 cases.  

Moving onto Africa, the graph seems very similar to the one of Asia with a couple outliers. 

The graph’s trends seem to align with what has been found before, but the timing of the growth seems to be shifted later. The first initial growth seems to begin around mid-May 2020 compared to early April 2020 in more ”connected” countries. For most of the countries, the growth seems to be very slow and gradual, but for some, the growth changes or grows at a very rapid rate. 

Once isolating the countries with the most cases, one country that stands out. South Africa appears to have the ”steps” pattern in its graph where all the other countries are not as similar to the ”steps” pattern. In addition, it has around double the cases of the other countries. This seems strange since South Africa seems to be similar to the other countries regarding economy and other factors. The most probable reason for South Africa’s case count would be its business and people going to South Africa for business compared to other countries whose economy runs on other factors. In addition, there is always the possibility of inaccurate data collection in African countries and South Africa might be the country that has collected COVID-19 data the best. Therefore South Africa would have higher total case counts. 

4 Statement of Limitations 

4.1 Alternatives 

Other ways to approach the data analysis would be to focus on specific countries individually then compare them after each country was analyzed. This would provide a much more thorough analysis of the data and help understand how each country went through the pandemic. 

In addition, this study focused more on the total confirmed cases over time and how the values for that changed. The data used also allowed more analysis into total deaths, recoveries, or death rates that were not focused on in this paper. When analyzing death rates and deaths, one could understand more about the health care systems and compare how well COVID-19 was treated, compared to how it spread. 

4.2 Weaknesses 

A large weakness came with the data itself. Governments might not be able to collect all the data accurately. Even though the data was collected from a trusted source, John Hopkins University, the data that the countries released may have been flawed. One instance of this could be seen in the mistake in the data for Turkey with over 1 million recoveries in one day. Governments either have falsified data or simply cannot measure data regularly enough. This could be seen in the daily cases graph which became jagged. 

The thorough analysis of the data going by every country would be very weak to the inconsistent data. Many countries have inconsistent data and thoroughly analyzing them all would be inefficient and ineffective. Perhaps a blend of thorough analysis on specific countries and a global approach would have been the best way to work with the data. A next step would be the analysis of death rates and deaths for countries would bring very valuable insight to the study. However the death rates and deaths more shine light on the treatment and effectiveness of the treatment of COVID-19 rather than the spread itself which was the main focus of this research paper. 

4.3 Challenges 

Working with the dataset was rather challenging. The data itself came with some flaws such as incomplete data, or outliers that skewed the data. This was difficult to work through and questioned the validity of the rest of the data. Some outliers could exists for individual countries, but would not be seen without checking the graph of each country independently. 

On a more personal scale, fully understanding and working with the data was new and posed many challenges of getting the code itself to run. 

5 Conclusion 

5.1 Discoveries 

There seems to be very distinct trends in the graph across the data. For example the trend in the early growth of cases in April. Some countries had this portion of the graph shifted earlier or later. It is interesting to understand that different countries get infected at different times due to the kinds of connections the country has with the world. Even though the growth started at different times, the general trend of sharp growth followed by a plateau in the cases shows that the virus always seems to take the same course when spreading. 

In addition, the second ”wave” of COVID-19 also appeared dominant in countries that were considered first-world or better off than others. Surprisingly, they were the ones that experienced the second ”wave” and the third world countries did not experience this second ”wave” of cases. 

The difference between cases for countries and general trends seems to stay consistent throughout the data. This suggests that the virus still has similar impacts regardless of how developed a country is. Perhaps it does not matter how developed or wealthy the country is, but rather the effectiveness of the government and people to combat the virus together. 

Another thing that stands out is how some more authoritarian governments handled the virus better than more democratic governments. Sometimes governments have to take action to do what is better for the people, even if the people themselves do not want to. The freedom that comes with democratic governments has its downsides that allow the people to do what they wish, sometimes at the expense of others. Governments sometimes need to be able to enforce their laws to protect the lives of their people, but it also needs to come with balance to prevent the exploitation of the people. 

5.2 Significance 

Many trends have popped up when analyzing the data, some of the most prominent ones are the growth starting around April 2020, and the increase in rate around October 2020 when moving into Autumn. The increase in growth in April was most likely due to the spread of the virus to all the other countries through business and travel. Even though the virus was discovered in late December 2019, the growth still hit many countries almost 6 months later. 

The question then becomes when is a good time to quarantine society and prevent the spread of the virus? Because many countries would not want to shut down their economy and let other countries get an upper hand in trade and other things. Perhaps it is the selfish nature of humans to keep airlines open and allow the virus to spread freely, only taking action when people lose their lives. The increase in rate around April is most likely due to the lack of government action to prevent the spread of the virus. To prevent this in the future, governments should take action quickly and effectively. 

The growth starting later in October can be explained with the colder weathers which create a better environment for the virus to thrive. Some of the increase in cases could also be attributed to people relaxing their stay-at-home procedures or others becoming desperate to support themselves financially. 

Addressing the first point, people will become bored and look to find relief from the dull quarantine. This however, should be prevented because it seems to be the least meaningful way to cause the spread of the virus. Not out of necessity, but out of boredom or other feelings, people will risk their lives and the lives of others to help themselves. Government action may have a role in this. Where you see more powerful governments, people are forced to stay at home or be arrested. Whether or not this goes against human right, it certainly is effective. In countries such as China, measures were put into place to limit the amount of people going outside and such. It seems to be effective because, even though China was the origin of the virus, they have limited the spread and have almost returned to normal life by 2021. 

The issue of financial aid and other factors that force people to go outside and spread the disease can also be looked at. In the US, every person wants to get their money from others to survive, otherwise they themselves cannot afford to quarantine at home. These actions can be seen in landlords evicting people because they cannot pay their rent. The evictions benefit no one and only further the spread of the virus. Government action and financial aid can help, but that is beyond the scope of the research paper. The economics behind the situation cannot be analyzed in this paper. 

5.3 Contributions 

Thank you to Dr. Peter Kempthorne for being my mentor for this project. This project was actually my first real project in Statistics and using the Language R. I came into this project with some beginner knowledge in other coding languages, but none of them I had used in the way I used R. However after some perseverance, I learned how to generally work with R functions and create data that is useful.

Outside of R, I also learned a lot about statistical thinking in general. While working on this project under a mentor, I was able to understand more of the thought process of how to represent data well, and even new ways to use the data to predict things in the future. Unfortunately, I was not able to incorporate those aspects of my learning into this research paper.

5.4 Citations

Soetewey, Antoine. “Top 5 R Resources on COVID-19 Coronavirus.” Medium, Towards Data Science, 10 Jan. 2021, towardsdatascience.com/top-5-r-resources-on-covid-19-coronavirus-1d4c8df6d85f.

Krispin, Rami. “RamiKrispin/coronavirus_dashboard.” GitHub, 2020, github.com/RamiKrispin/coronavirus_dashboard. 

About the author

Leison Gao

Leison is a sophomore at the Los Gatos High School.

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