1. INTRODUCTION

Although the exact origin of the Covid-19 disease is still unknown, in late December 2019, a previously unidentified Coronavirus, now named as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China and spread globally. Since then, the pandemic moved from Asia to Europe and the US, and as developed countries are gradually vaccinating their way out of the Covid crisis, a deadly second wave has overwhelmed less developed countries of South Asia (see Figure 1). India has witnessed a rapid increase in Covid positive cases since late March 2021 and by early May, officially reported daily cases touched the grim milestone of 400,000. The results of this severe second wave have been devastating: Acute shortage of hospital beds, medication, and medical staff even in big cities like Delhi, Bengaluru, Lucknow, and many more have been widely reported. One of the most pressing concerns have been a shortage of medical oxygen to treat Covid patients, a situation that has caused numerous avoidable fatalities (^{Wire Staff, 2021}). But the problem is not going to be confined to India. Neighboring countries are also witnessing surges that threaten to overrun their health infrastructure (^{UN News, 2021}).

Note: The horizontal dashed line is the population share of South Asia in the world (as 23.9%). Data covers the period from 24^th February 2020 to 15^th May 2021.

Source: Chart created using data from the Our World in Data website: <https://ourworldindata.org/coronavirus>.

Figure 1 Proportion of daily reported COVID-19 cases and deaths in South Asia as a percentage of the world total 

The strategies to mitigate COVID-19 have included both medical interventions (MIs) and non-pharmaceutical interventions (NPIs). While MIs involve long-term preparedness of health infrastructure and immediate treatment of symptoms with approved drugs, hospitalization in severe cases and administration of medical oxygen, NPIs include testing, contact-tracing, quarantining infected persons, mandating the use of masks in public, shelter-in place, or imposing lockdowns. Scholarly and reflective pieces on the pandemic have shown that across countries, the pace and shape of the MIs and NPIs, depend on the robustness of governance and broader political-economic factors (^{Bergquist, Otten, and Sarich, 2020}; ^{Atkinson et al., 2020}; ^{Salvatore et al., 2020}). For instance, in the United States, the choice to mandate NPIs has been a political one: Most Democratic governors implemented NPIs while Republicans were reluctant to do so (^{Cui et al., 2021}). Likewise, research on the ongoing pandemic indicates how the difficulties of persuading government ministers to make decisions, and the ambiguities and lags in communications between policy makers and health care professionals have shaped the COVID-19 policies (^{Atkinson et al., 2020}). Moreover, because of its enormous economic cost, the NPIs, specifically the imposition of shelter-in-place or lockdown measures, have been disfavored by policy makers as well as groups that stand to lose from them -business owners and workers. As this article demonstrates, in South Asian countries, long-term health policies, and immediate economic and political considerations have been crucial in shaping the responses to the pandemic.

The efforts to contain the pandemic also spurred global cooperation for vaccine research, which resulted in the unprecedented development of a vaccine under one year. The approval of a vaccine in late 2020 led to a global rush to manufacture and administer the doses to a significant section of global population. The manufacturing and distribution of vaccines has raised important questions related to Intellectual Property Rights and the pharmaceutical industry’s prioritization of profits. There is an ongoing debate, and deep concerns about, access to vaccines and gaps in roll-out in low-income and middle-income countries compared with economically developed countries. At the same time, the pace and success of vaccination drives also depend on the state of a country’s health care infrastructure and the ability of national governments to formulate and implement effective policies regarding the pandemic.

This study captures the dynamics of the COVID-19 pandemic in South Asia by tracking major indicators, including the rates of infection and fatalities, the extent of testing, the preparedness to mitigate the disease, and also the pace of vaccination rollouts. Specifically, we capture the dynamics of the COVID-19 pandemic in four South Asian countries by tracking five key indicators. The severity of the disease and its lethal impact are captured by the daily reported cases per million persons and the daily reported deaths per million persons, respectively. Reporting of cases relies on widespread testing. Hence, the extent of the pandemic’s spread can only be truly captured if adequate testing is conducted. We look at this aspect of the pandemic by tracking daily tests per thousand persons and the daily test positive rate. The final indicator that we use refers to the most important intervention to deal with the pandemic in a long run sense: Vaccination. We track vaccination rollout by tracking the number of people fully vaccinated per hundred persons.

Historically, South Asian countries have witnessed many outbreaks of infectious diseases -cholera is one of the most glaring examples of this (^{Chakrabarti, 2010}; ^{Mahapatra et al., 2014}). The region includes eight countries -Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. Our study focuses on India and its three neighboring countries -Bangladesh, Pakistan, and Sri Lanka. The three countries of India, Pakistan, and Bangladesh are largest in terms of population. Even though Afghanistan and Nepal have higher populations than Sri Lanka, we chose the latter as the fourth country in our sample because the country has one of the most developed health care systems in the region. Comparing and contrasting these four nations will cover a large section of South Asian population and also allow us to consider variations in health policies and how they have determined the responses to COVID-19.

The rest of this paper is organized as follows: In section 2, we discuss our data sources; in section 3, we discuss trends about the spread of the pandemic; in section 4, we look at health spending and infrastructure; in section 5, we discuss government interventions; finally, we conclude the paper in section 6.

2. DATA SOURCES

We use several data sources for conducting the analysis in this paper. The data on the COVID-19 pandemic are taken from the online resource, Our World in Data (^{Ritchie et al., 2020}). From this online resource, we collect information on reported COVID-19 daily cases per million population, reported COVID-19 daily deaths per million population, daily COVID-19 tests per thousand population, daily positive rate, people vaccinated per hundred and people fully vaccinated per hundred. These are the primary variables we use to track the spread of the pandemic and the response of governments to both estimate the severity of the outbreak and to initiate measures to contain its spread.

We also use data from the World Bank’s World Development Indicators Database to understand health expenditure, health infrastructure and poverty in the four South Asian countries that we study. We supplement this with information on the economic policy response of governments from the International Monetary Fund’s COVID-19 policy tracker. Together, these data give us a picture of the preparedness and the response of different South Asian countries to deal with the pandemic.

3. TRAJECTORY OF THE PANDEMIC

3.2. Four South Asian countries: Cases and deaths

Figure 2 presents the 7-day moving average of the number of daily COVID-19 cases reported per million population in Bangladesh, India, Pakistan and Sri Lanka. From the figure we can see that in each of these countries, the pandemic has progressed in waves.

Note: Data covers the period from 24^th February 2020 to 15^th May 2021.

Source: Chart created using data from the Our World in Data website: <https://ourworldindata.org/coronavirus>.

Figure 2 Daily reported COVID-19 cases per million population (7-day moving average) 

Two waves of the pandemic are most prominently visible in India. The first COVID-19 case was reported in India on 30^th January 2020, but the first wave started in earnest only in April 2020. It peaked in late September 2020, and then went downhill until March 2021. A catastrophic second wave hit India in April 2021, exactly one year since the first. While the number of daily reported cases at the peak of the first wave in India was only about 75 per million population, the second wave has reached almost 300 daily reported cases per million population -a four-fold increase.

Sri Lanka shows a similar two-wave pattern. The first case of COVID-19 was reported in this country on 27^th January 2020. Number of daily reported cases was relatively low in Sri Lanka for the next eight months. The first wave started in early October 2020, peaked in early-March 2021, and declined for a month before a huge second wave hit the country from early April 2021. Daily reported cases at the peak of the first wave was about 50 per million population; in the second wave, daily reported cases have crossed 100 per million population by the first week of May 2021.

The patterns observed in Bangladesh and Pakistan are similar. Both these countries have witnessed three relatively small waves (compared to India and Sri Lanka). At the peak of its latest wave, which started in early March, the daily reported cases in Bangladesh has been just under 50 per million population. In Pakistan, the peak for the latest wave, which also started in early March, was even lower. More importantly, the latest wave in both these countries is declining, in sharp contrast to the situation in India and Sri Lanka, where the wave has not yet peaked.

Figure 3 plots the 7-day moving average of daily reported COVID-19 deaths. In this figure, we see the same wavelike pattern that we observed in Figure 2. At the peak of the first wave in India, daily reported deaths were just under 1 person per million population. It has increased three-fold, to about 3 persons per million population, in the second wave. In Sri Lanka, daily deaths at the peak of the first wave was lower than 0.5 persons per million population, but it has come close to 1 person per million population in the second wave. In both Bangladesh, daily deaths during the peak of the first wave was much lower than 0.5 persons per million population; it has crossed above 0.5 persons per million population during the latest, third, wave. In Pakistan, peak daily COVID-19 deaths have crossed 0.5 persons per million population during both the first and the third wave.

Note: Data covers the period from 24^th February 2020 to 15^th May 2021.

Source: Charts created using data from the Our World in Data website: <https://ourworldindata.org/coronavirus>.

Figure 3 Daily reported COVID-19 deaths per million population (7-day moving average) 

From Figures 2 and 3, we can conclude that the severity and fatality of the outbreak is worst in India, with the second wave being nothing short of catastrophic. The next worst outbreak is in Sri Lanka, which is also witnessing a devastating second wave from April 2021. Both Bangladesh and Pakistan seem to have so far weathered the storm better than India and Sri Lanka.

4. HEALTH INFRASTRUCTURE, EXPENDITURE, AND POVERTY

How prepared were these countries to deal with the pandemic? To address this question, we present, in Table 1, data on some indicators of health infrastructure, health expenditure and poverty for the four countries from the World Development Indicators Database of the World Bank.

Let us start by looking at two measures of health infrastructure: Physicians and hospital beds. Physicians per 1,000 persons is lowest in Bangladesh at 0.5. It is followed by India at 0.8 physicians per 1,000 persons. Pakistan and Sri Lanka both have a higher Figure: 1 physician per 1,000 persons. Hospital beds per 1,000 persons is lowest in Pakistan, at 0.6, followed by India, at 0.7, and then Bangladesh, at 0.8. With 3.6 hospital beds per 1,000 persons, Sri Lanka has more than 5 times the number for the other three countries.

Next, let us look at two measures of health expenditure: As a share of Gross Domestic Product (GDP) and in per capita terms. Current health expenditure as a share of GDP is close among the countries -with Bangladesh having the lowest, at 2.3%, and Sri Lanka having the highest, at 3.8%. But these countries are very different in terms of population and GDP. Hence, a better way to compare healthcare expenditure is to look at per capita figures. Current healthcare expenditure per capita in purchasing power parity (PPP) terms, and measured in current international dollars, varies widely across the four countries. Bangladesh has the lowest health expenditure, at $94.3 per person, followed by Pakistan, at $160.6 per person, and India, at $253.3 per person. With $ 503.6 per person, Sri Lanka is way ahead of the other three countries in health expenditure.

The picture of health expenditure is incomplete unless we also ask the following question: What fraction of current health expenditure comes from the government (or, relatedly, what is the share of out-of-pocket expenditure on health)? The pattern here is largely consistent with per capita healthcare expenditure. Bangladesh, which has the lowest per capita health expenditure, also has the lowest share contributed by the government (16.7%); Sri Lanka, with the highest per capita health expenditure, also has the government contributing the most (43%). India and Pakistan fall between these two ends with government, with 27% and 32% contributed by the government, respectively. The figures for out-of-pocket expenditure on health are the mirror images of the government share of health expenditure: Countries with higher government share have lower out-of-pocket expenditure on health.

The final set of indicators displayed in Table 1 refers to the average income and poverty. Per capita GDP is a measure of the average level income in a country. It provides an indicator of the average material standard of living of the population. Countries with higher per capita GDP are generally better off and can deal with health crises like the COVID-19 pandemic in a better way. From Table 1, we see that per capita GDP is lowest in Bangladesh, and highest in Sri Lanka. Pakistan and India fall in between, with India having a higher average income than Pakistan. Thus, in terms of average standard of living, Sri Lanka was the best and Bangladesh the worst prepared to deal with the pandemic.

Poverty is measured by the head count ratio, which is defined as the proportion of persons who fall below the national poverty line. This is a very rough and imperfect measure of the proportion of people who are most vulnerable in economic terms, and who would be most in need of help to cushion any negative income shock due to the pandemic. In terms of poverty headcount ratios, there is very little difference between Bangladesh (24.3% of population), India (21.9% of population) and Pakistan (24.3% of population) -though India’s number is a little lower than the other two countries. The poverty headcount ratio in Sri Lanka, at 4.1% of the population, is about 5 times lower than the other three countries.

5. GOVERNMENT RESPONSE

Governments responded to the pandemic with interventions in several directions. The first and immediate response was to start testing for COVID-19 cases across different cities, states and regions. This was essential to get an accurate picture of the spread of the pandemic in the country and also isolate infected patients and provide them medical care.

As the pandemic progressed and impacted the population of countries, governments also responded with two sets of policies to contain the spread of the pandemic and to reduce the adverse economic impact on the population. The first related to various non-pharmaceutical interventions like encouraging measures for physical distancing in society, which could reduce the speed of transmission of the virus. The second set of policies related to expenditures that were necessary to deal with the health and economic impacts of the pandemic.

Using information from the Johns Hopkins University coronavirus dashboard and the International Monetary Fund’s COVID-19 country-level policy tracker, we have summarized basic information about government responses in Table 2, and will now discuss each component in detail.

The final piece of government intervention related to COVID-19 vaccination. It is widely understood by public health experts and epidemiologists that the only sustainable way to deal with the pandemic is to vaccinate a large section of each country’s population. This effort picked up from the early part of 2021 and we track the progress of vaccination in the four South Asian countries in this section.

5.1. COVID-19 testing

Part of the difference in the number of cases reported across the four countries can be attributed to differences in the adequacy, or otherwise, of testing. Without adequate number of tests, a country with a severe outbreak would not report the true scale of the epidemic. We look at two indicators of the adequacy of COVId-19 testing across these four countries (see Figure 4).

Note: Data covers the period from 24^th February 2020 to 15^th May 2021.

Source: Charts created using data from the Our World in Data website: <https://ourworldindata.org/coronavirus>.

Figure 4 Daily tests per thousand population (7-day moving average) 

By late-August 2020, India was conducting more than 0.5 daily tests per thousand population; Sri Lanka reached this level of testing by about November 2020. Both India and Sri Lanka witnessed a gradual decline in daily testing rates in the early part of 2021. As the second wave progresses, testing has been ramped up again in both countries.

Bangladesh and Pakistan have been conducting far fewer tests than India and Sri Lanka. In Pakistan, daily tests have slowly increased to reach about 0.25 tests per thousand population by early April 2021. Bangladesh has the lowest level of testing among the four countries, not even reaching 0.25 tests per thousand population.

While tests per thousand population gives a first indication of the level of testing, it does not normalize by the severity of the outbreak. Hence, we look at a second measure, the test positive rate. This is the ratio of positives that are reported per 100 tests conducted. The World Health Organization (WHO) recommends a benchmark figure of 5% test positive rate as an indication of adequate testing. When the test positive rate is higher, it shows that testing is low given the severity of the outbreak.

In Figure 5, we plot the 7-day moving average of the test positive rate in the four countries. From the figure, we get corroboration of the general pattern we saw in Figure 3 but with some important differences. In general, testing has been least adequate in Bangladesh, where the test positive rate has mostly been above 10%, other than a few months in the early part of 2021. In Pakistan, testing was generally inadequate during the early phase of the pandemic. By September 2020, the test positive rate in Pakistan had fallen to below 5%, giving indication of adequate testing once the severity of the outbreak is considered. Since late February 2021, the test positive rate has been rising in Pakistan. In general, India has performed better than Bangladesh and Pakistan, in terms of testing. But the picture is very different for the second wave in India since late March. The test positive rate has increased rapidly since early April and is now over 20%. This provides evidence that testing is inadequate given the severity of the outbreak in the second wave in India. In general, Sri Lanka has performed the best in terms of testing. Even during the current, second wave, the test positive rate in Sri Lanka has stayed below 10%, giving indication of adequate testing.

Note: Data covers the period from 24^th February 2020 to 15^th May 2021.

Source: Charts created using data from the Our World in Data website: <https://ourworldindata.org/coronavirus>.

Figure 5 Daily test positive rate, i.e. positive cases per 100 tests (7-day moving average) 

5.3. COVID-19 vaccination

One of the pillars of the long-term strategy to deal with the pandemic is vaccination. Hence, the final metric we track is the speed of vaccination rollout. Figure 6 plots the number of persons vaccinated per hundred population, and Figure 7 plots the number of persons fully vaccinated per hundred population. Here we see a disappointing picture.

Note: Data covers the period from 24^th February 2020 to 15^th May 2021.

Source: Charts created using data from the Our World in Data website: <https://ourworldindata.org/coronavirus>.

Figure 6 People vaccinated per hundred population 

Note: Data covers the period from 24^th February 2020 to 15^th May 2021.

Source: Charts created using data from the Our World in Data website: <https://ourworldindata.org/coronavirus>.

Figure 7 People fully vaccinated per hundred population 

Pakistan has the slowest COVID-19 vaccination rollout. While vaccination efforts started in mid-February 2021, it has only managed to vaccinate about 1.2% and fully vaccinate almost 0% of its population (Figure 6). Sri Lanka kicked off vaccination in early February 2021 and had managed to vaccinate about 4% of its population by early April. Since then, the pace of vaccination has slowed down significantly (Figure 6). Sri Lanka saw completion of vaccination dosage in a serious way only in late April and by mid-May, it has managed to fully vaccinate only 1% of its population (Figure 6). Bangladesh started its vaccination campaign in mid-February and the pace of vaccination has slowed down significantly since early April (Figure 6). By mid-May, Bangladesh had managed to fully vaccinate a little over 2% of its population. Among these four countries India started COVID-19 vaccination earliest, in mid-January, and by mid-May had vaccinated close to 10% of its population (Figure 6) and had fully vaccinated about 3% of its population (Figure 7).

As noted earlier, the ability of a country to vaccinate its people depend on a range of political, economic, and social factors. The pace of vaccination in South Asia highlights a scarcity of supply as major vaccine producing countries have kept most doses for themselves. The three countries -Bangladesh, Pakistan, and Sri Lanka- lack the necessary know-how, raw material, and the infrastructure to ramp up their production of vaccines (^{Rowland, Rauhala, and Berger, 2021}). Consequently, their vaccination plans rely on supplies from other large producers such as China, Russia, and India. However, after the disastrous second wave, India is struggling to procure vaccines for its own citizens -which has adversely hit supplies to other countries in the world, including Bangladesh and Sri Lanka.

India is different in this respect from other South Asian countries because it has a well-developed vaccine production capacity. In January 2021, the Drug Controller General of India authorized the use two vaccines. The first, called Covishield, was developed by Oxford University and AstraZeneca, and is being produced locally by the Serum Institute of India. The second one is a locally developed vaccine called Covaxin. A leading global manufacturer of vaccines, India was expected to roll out these vaccines not just for its own usage but also for other countries in Asia and Africa. By April 2021, India had exported millions of doses to other countries. However, the government’s inability to estimate vaccine needs and place order in advance has resulted in a severe supply crisis in the country. This shortage of shots has become even more acute after the onset of the deadly second surge in March 2021 in India. The subsequent ban on vaccine exports threatens to delay the global vaccination drive, including in countries such as Bangladesh and Sri Lanka, which are scrambling to find alternate sources of vaccine imports (^{Yasir, Bengali, and Gladstone, 2021}; ^{Burke, Wintour, and Ratcliffe, 2021}).

Although the sluggishness of vaccine procurement and delays in effective distribution are the biggest determinants of vaccine rollouts, misinformation and hesitancy have also had negative effects on vaccination in these countries. For instance, rumors of potential side effects and suspicions about vaccine efficacy have kept away people from taking vaccines in Pakistan (^{Dagia, 2021}). A nationally representative, cross-sectional survey in Bangladesh has revealed that more than 32% of the respondents suspected the effectiveness and safety of the vaccine (^{Ali and Hossain, 2021}). According to reports, in India, the fears of losing a day’s work to get the shot and possible side effects that may force them to take days off work that they simply cannot afford, are keeping daily wagers away from vaccines (^{Cohen, 2021}). Apparently, vaccine hesitancy is low in Sri Lanka. However, a recent survey-based study reveals that most respondents are concerned about the possible side effects, allergies, and protection duration (^{Wijesinghe et al., 2021}). Suspicion of the new vaccines are also rooted in misinformation regarding the seriousness of coronavirus disease itself. For instance, in India, the unscientific approach of public figures to COVID-19 has created rumors and misinformation about the disease and its remedies (^{Express Web Desk, 2021}). Unfortunately, systematic awareness campaigns that will disseminate scientific information and reassure people of side effects are lacking in these countries.

6. CONCLUSION

In this paper, we have studied the progression of and responses to the COVID-19 pandemic in four South Asian countries: Bangladesh, India, Pakistan and Sri Lanka.

Our analysis shows that in terms of preparedness, Sri Lanka was clearly the best placed. It was not only the richest country among the four studied in this article, but also had the largest expenditure on health -with the greatest share contributed by the government. Bangladesh was at the most disadvantaged position -not only because it was the poorest, but also because of the low contribution of the government to an already low total health expenditure. India and Pakistan fell in between these two extremes.

Our study also highlights how policy priorities of countries come to the fore during a health crisis like a pandemic. Countries which have invested, or continue to invest, more in building health infrastructure and capacities, would naturally be better able to shield its citizens from the negative impact of the pandemic. One way to gauge this policy slant is to compare health and military expenditure (last row of Table 1). From the data in Table 1, we see that the policy priorities are most skewed in Pakistan, where military expenditure is twice that of health expenditure. At the other extreme is Sri Lanka, where health expenditure is twice that of military expenditure. Bangladesh and India fall in between, with India having a more favorable expenditure share towards health than Bangladesh.

The impact of the pandemic is of course going to depend on a complex set of factors, which includes but it not limited to health expenditure and health infrastructure. One simple way to quantify the negative impact of the pandemic is to look at the total number of COVID-19 deaths per million population. On May 15^th, 2021, total (cumulative) reported COVID-19 deaths per million population in Bangladesh, India, Pakistan and Sri Lanka were 73.62, 195.86, 88.47 and 43.95, respectively.

Thus, while Sri Lanka has clearly been best able to minimize the adverse impact of the pandemic on lives lost, due in no small measure to its superior health infrastructure (see section 3), India has performed the worst among the four South Asian countries we have studied, despite having better infrastructure than Bangladesh and Pakistan. The fact that India is currently governed at the federal level by a Hindu nationalist, right-wing political party, whose leaders have routinely minimized the role of scientific analysis and evidence in dealing with the pandemic, must surely have contributed to the relative outcomes we see.