Analysis of COVID-19 in India

1 Introduction to the topic

The COVID-19 pandemic has had a significant impact on India, with the country (like others) recording a large number of cases and deaths due to the virus. A vast amount of data has been generated, which may not necessarily be related to the virus itself but rather to aspects of daily life, such as data on air quality, education, or the economy. This data has enabled researchers, analysts, and data scientists to gain insight into and understand the virus’s impact on society. This project will present the datasets and their subsequent analysis.

2 Data Analysis and Hypotheses

These datasets are public and easily accessible online, and contain information on confirmed cases, deaths, testing, hospitalizations, vaccinations, and other relevant variables at various geographic levels, such as global and regional. Analyzing these datasets allows us to track the spread of the virus, identify trends, and make informed decisions based on these facts. The collected data covers the period from January 2020 to May 2021, so we can also verify whether our observations of the pandemic’s progression were accurate or not.

Country

3 Demographics

3.0.1 Uttar Pradesh

Uttar Pradesh is the most populous state, with a population of 200 million people. Since it is a region with high-quality soil and economically favorable conditions for its residents, it also has the highest population density of all Indian states, which means that urban areas are more susceptible to the spread and transmission of the virus. ¹ We can see that the number of vaccinated people is also the highest, but in the table of infected people, it ranks sixth, with 1.7 million people infected, which equals 7% of the state’s total population. ²

¹ Kopf, Dan; Varathan, Preeti (11 October 2017). “If Uttar Pradesh were a country”. Quartz India. Archived from the original on 22 June 2019. Retrieved 20 May 2019.

² “Agriculture” (PDF). www.niti.gov.in. NITI Aayog. Retrieved 19 October 2021.

The country received the largest number of vaccine doses—approximately 300 million—which, relative to its population, amounts to 130%; in other words, the majority of the population was vaccinated, and as a result, the virus did not spread in this country as had been anticipated.

3.0.2 Maharashtra

On the other hand, the state with the highest number of confirmed cases is Maharashtra, which, like Uttar Pradesh, has a high population density; the case rate is similarly high, at around 125%. Mumbai, the capital of Maharashtra, is India’s most populous city, with a population of 20 million. The city is also home to India’s largest and busiest airport, Chhatrapati Shivaji Maharaj, with an annual passenger volume of 48 million, meaning the potential for virus transmission is very high. Furthermore, the country’s population was not familiar with or did not adhere to current anti-pandemic measures, as a large portion of the population consists of migrant workers who come from other parts of India in search of better conditions.³

³ Bertaud, A. (2011). Mumbai’s ill conceived Malthusian approach to development.

Although the number of people vaccinated was relatively high, the total number of confirmed cases reached 6.4 million, accounting for 4% of Maharashtra’s population.

4 Statistical Trends

By the end of the data analyzed, the number of confirmed cases had risen to 32 million, with 31.5 million people successfully recovered and approximately 500,000 deaths. As we can see in the graphs on the right, during the first wave—that is, from early 2020 through March 2021—the number of cases was relatively low. Nevertheless, hospitals in India were full and struggled to cope with the mounting pressure from rising case numbers and to treat as many patients as possible.

The Indian government implemented several measures to increase hospital capacity, including setting up temporary COVID-19 care centers and converting hotels and stadiums into isolation facilities. Hospitals have also implemented strict infection control measures to prevent the spread of the virus, such as isolating COVID-19 patients, using personal protective equipment (PPE), and increasing the frequency of cleaning and disinfection. The sharp rise in COVID-19 cases in India has led to a shortage of medical oxygen, which is essential for treating severe cases of the disease. Hospitals are struggling to secure enough oxygen to meet demand, which has led to a public health crisis; therefore, the Indian government has taken steps to address the oxygen shortage, such as increasing production and importing oxygen from other countries, but in some parts of the country, the situation remained critical.⁴

⁴ Kapoor, Suraj. “COVID-19 pandemic response by India and future directions.” Journal of Public Health and Primary Care 3 (2022): 56 - 62., Available from: http://www.jphpc.org/text.asp?2022/3/3/56/354819

⁵ Anand, Abhishek et al. “Three New Estimates of India’s All-Cause Excess Mortality during the COVID-19 Pandemic.” (2021).

During the second wave, from March 2021 to May 2021, the number of cases and deaths rose sharply. The surge in cases overwhelmed the healthcare system, with hospitals running out of beds, oxygen, and other essential supplies. The Indian government imposed lockdowns and other restrictions in an attempt to control the spread of the virus, but the measures were criticized for being too little, too late. The rise in cases was also attributed to factors such as large gatherings, political rallies, and religious events, which were allowed to take place despite the risk of spreading the virus.⁵

5 Analysis of Vaccination and the Effect of Gender on Mortality

As can be seen from the graphs, the gender gap is as high as 7 percent, which seems natural at first glance. However, the fact is that during this period, the male-to-female ratio was 1000:1020, meaning there were more women than men. This suggests that there may have been a bias in the vaccination process, with men being prioritized for vaccination ⁶.

⁶ https://www.bbc.com/news/world-asia-india-59428011

⁷ Massimi, A., Rosso, A., Marzuillo, C., Prencipe, G., Soccio, P.D., Adamo, G., Sturabotti, G., Vacchio, M.R., Vito, C.D., & Villari, P. (2017). Childhood vaccinations. Validation of a tool for measuring knowledge, attitudes and vaccine hesitancy in pregnant women. Epidemiology, Biostatistics, and Public Health.

Women often hesitated to get vaccinated for various reasons: limited knowledge about vaccination, insufficient attention from healthcare providers, obtaining information from unreliable sources, and misconceptions about vaccine side effects. Concerns about vaccine safety and efficacy, including fears of unknown long-term effects, pregnancy-related concerns, etc.⁷

The study found that biological sex also influences the outcomes of infection, with men having higher rates of comorbidities and higher mortality than women, who instead experienced more severe illness and longer survival. Another study found that male sex was a major predictor of admission to the intensive care unit (ICU) due to infection, along with the presence of obesity, chronic kidney disease, and hypertension.

6 Probability of developing the disease

Studies have shown that the accuracy of COVID-19 tests can vary depending on the test’s sensitivity and specificity, as well as the prevalence of the virus in the tested population. False-positive test results may occur, leading to unnecessary quarantine and treatment, while false-negative results may lead to the spread of the virus.

In reviewing studies on test effectiveness, we found that different studies report varying percentages. For example, the IgM/IgG test had values ranging from 73.9% to 100% across eight different studies, with an average of approximately 94.5% for a single test. To avoid false positives, if a person is tested using two or more tests, the accuracy of the test increases. ⁸

⁸ Xiao, X., Zhou, Q., Zhu, J., Sun, L., Zhang, H., Sun, Y., Zhao, J., & Cui, L. (2021). False-positive colloidal gold-based immunochromatographic strip assay reactions for antibodies to SARS-CoV-2 in patients with autoimmune diseases. Annals of translational medicine, 9(7), 534. https://doi.org/10.21037/atm-20-6509

For the calculation, we can use, for example, Bayes’ theorem. Bayes’ theorem is a mathematical formula that describes the probability of an event based on prior knowledge of conditions that may be related to the event. It can be used to calculate the probability that a person will test positive:

\[ {\color{Orange} {\color{bayesorange} P (\text{Illness} \mid \text{Positivity})}} = \frac {{\color{brown} {\color{bayesred} P (\text{Illness})}} \times {\color{brown} {\color{bayesblue} P (\text{Positive} \mid \text{Illness})}}} {{\color{grey} {{\color{grey} P(\text{Positive})}}}} \]

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\[ {\color{Orange} {\color{bayesorange} P (\text{Illness} \mid \text{Positivity})}} \]

represents the probability that I have the disease and have tested positive.

\[ \frac {{\color{brown} {\color{bayesred} P (\text{Illness})}} \times {\color{brown} {\color{bayesblue} P (\text{Positive} \mid \text{Illness})}}} {{\color{grey} {{\color{grey} P(\text{Positive})}}}} \]

represents the probability of having the disease, multiplied by the probability of a false positive (the chance that I test positive when I actually have the disease), divided by the test’s sensitivity. This can be further expressed as the probability of a positive test result plus the probability of a false negative test result. \[ {{\color{grey} {{\color{grey} P(\text{Positive})}}}} = {{\color{brown} {\color{bayesred} P (\text{Illness})}}} \times {\color{brown} {\color{bayesblue} P (\text{Positive} \mid \text{Illness})}} \ + \ {{\color{brown} {\color{bayesred} P (\text{-Illness})}}} \times {\color{brown} {\color{bayesblue} P (\text{Positive}\mid \text{-Illness})}} \] So, in our case, assuming that the test has a 95% sensitivity and the prevalence of the disease in the population is 1 in 181—which corresponds to a probability of 0.005—we get:

\[ 0.08=\frac{0.005 \times 0.95}{0.005 \times 0.95 \ + \ 0.995 \times 0.05} \] In other words, if we were to get tested now and receive a positive result, there is only an 8% chance that we are actually positive. However, if we were to repeat the test, the probability would increase significantly, to approximately 62%.

7 Summary

The World Health Organization (WHO) declared a pandemic in March 2020, and since then it has affected millions of people worldwide. The pandemic has had a significant impact on public health, the economy, and society as a whole, and has highlighted the importance of preparedness and response to infectious diseases. The pandemic has affected different countries in different ways.

Using data, we were able to visualize and demonstrate what had a significant impact on the sharp rise in, for example, cases, deaths, or vaccinations.