Comparative impact assessment of COVID-19 policy interventions in five South Asian countries using reported and estimated unreported death counts during 2020-2021

Abstract

There has been raging discussion and debate around the quality of COVID death data in South Asia. According to WHO, of the 5.5 million reported COVID-19 deaths from 2020-2021, 0.57 million (10%) were contributed by five low and middle income countries (LMIC) countries in the Global South: India, Pakistan, Bangladesh, Sri Lanka and Nepal. However, a number of excess death estimates show that the actual death toll from COVID-19 is significantly higher than the reported number of deaths. For example, the IHME and WHO both project around 14.9 million total deaths, of which 4.5-5.5 million were attributed to these five countries in 2020-2021. We focus our gaze on the COVID-19 performance of these five countries where 23.5% of the world population lives in 2020 and 2021, via a counterfactual lens and ask, to what extent the mortality of one LMIC would have been affected if it adopted the pandemic policies of another, similar country? We use a Bayesian semi-mechanistic model developed by Mishra et al. (2021) to compare both the reported and estimated total death tolls by permuting the time-varying reproduction number (Rt) across these countries over a similar time period. Our analysis shows that, in the first half of 2021, mortality in India in terms of reported deaths could have been reduced to 96 and 102 deaths per million compared to actual 170 reported deaths per million had it adopted the policies of Nepal and Pakistan respectively. In terms of total deaths, India could have averted 481 and 466 deaths per million had it adopted the policies of Bangladesh and Pakistan. On the other hand, India had a lower number of reported COVID-19 deaths per million (48 deaths per million) and a lower estimated total deaths per million (80 deaths per million) in the second half of 2021, and LMICs other than Pakistan would have lower reported mortality had they followed India's strategy. The gap between the reported and estimated total deaths highlights the varying level and extent of under-reporting of deaths across the subcontinent, and that model estimates are contingent on accuracy of the death data. Our analysis shows the importance of timely public health intervention and vaccines for lowering mortality and the need for better coverage infrastructure for the death registration system in LMICs.

Fig 1. Number of people along with their percentages for different comorbidities.

This includes hypertension (Adults) [17, 18], COPD [–23], asthma [–28], diabetes (Adults), cardiovascular death rate, no of hospital beds, and number of people in different age groups and GDP per capita (2019 and 2020) for each of the five countries. Population in each category reported in millions. The percentages for comorbidities are calculated within population at risk.*ourworldindata.org. **wikipedia.org, ***worldbank.org.

Fig 2. Schematic representation of the different components of the Bayesian semi- mechanistic transmission model.

Each box represents and provides details of the components that define the overall stochastic structure of the model.

Fig 3. Daily reported and total (Reported + Unreported) deaths per million in 2020 (March 15 2020–Dec 31 2020) for each country.

The red and the black lines correspond to the daily reported and total estimated death trajectories respectively.

Fig 4. Reported cumulative deaths per million with the 95% CrI on December 31, 2020 calculated starting from March 15, 2020 in different counterfactual situations.

The rows correspond to the countries which are the donors, while the columns correspond to the recipients. The diagonal cells denote the reported deaths per million in the 5 countries. The off-diagonal cells correspond to the counterfactuals, which consists of 3 quantities: Counterfactual Deaths Per Million, Actual Deaths per million and difference of deaths between the actual and counterfactuals that could have been averted.

Fig 5. Total cumulative deaths (reported and unreported) with the 95% CrI on December 31, 2020 calculated starting from March 15, 2020 in different counterfactual situations.

The rows correspond to the countries which are the donors, while the columns correspond to the recipients. The diagonal cells denote the estimated actual deaths per million in the 4 countries. The off diagonal cells correspond to the counterfactuals, which consists of 3 quantities: Counterfactual Deaths Per Million, Actual Deaths per million, and difference of deaths between the actual and counterfactuals that could have been averted. A cell is coloured red or blue depending upon whether the 95% CrI of the number deaths averted in a counterfactual situation lies entirely in the negative or positive side.

Fig 6. Daily deaths for the different counterfactual situations along with the actual fitted reported deaths for each country in 2020 based on reported deaths analysis.

The blue bars in the plots denote the actual daily death cases for the recipient country, while the red lines denote the counterfactual ones. The time period of analysis is from March 15, 2020 to December 31, 2020.

Fig 7. Daily reported and total (Reported + Unreported) deaths per million in 2021 for each country.

The red and the black lines correspond to the daily observed and total death trajectories respectively.

Fig 8. Daily reported deaths for the different counterfactual situations along with the actual fitted deaths for each country.

The blue bars in the plots denote the actual daily death cases for the recipient country, while the red lines denote the counterfactual ones. The time period of analysis is from January 1, 2021, to June 30, 2021.

Fig 9. Cumulative reported deaths per million with the 95% CrI for the five different countries in each of the counterfactual situations in 2021.

The rows correspond to the countries which are the donors, while the columns correspond to the recipients. The diagonal cells denote the observed deaths per million in the 5 countries. The off diagonal cells correspond to the counterfactuals, which consists of 3 quantities, Counterfactual Deaths Per Million obtained using the Relative Approach, observed Deaths per million, and difference of deaths between the reported and counterfactuals that could have been averted. A cell is coloured red or blue depending upon whether the 95% CrI of the number deaths averted in a counterfactual situation lies entirely in the negative or positive side.

Fig 10. Total cumulative deaths (reported + unreported) per million with the 95% CrI in 2021 for the five different countries in each of the counterfactual situations.

The rows correspond to the countries which are the donors, while the columns correspond to the recipients. The diagonal cells denote the total deaths per million estimated by IHME in the 5 countries. The off diagonal cells correspond to the counterfactuals, which consists of 3 quantities, Estimated counterfactual Deaths Per Million, estimated Deaths per million by IHME, and difference of deaths between the estimated IHME deaths and counterfactuals that could have been averted. A cell is colored red and blue depending upon whether the number of deaths averted in a country is negative or positive respectively.