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Predictions, Role of Interventions and Effects of a Historic National Lockdown in India's Response to the the COVID-19 Pandemic: Data Science Call to Arms

Published onJun 09, 2020
Predictions, Role of Interventions and Effects of a Historic National Lockdown in India's Response to the the COVID-19 Pandemic: Data Science Call to Arms
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This version of the Pub was released on May 14, 2020. The latest version is from Jun 10, 2020.

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Abstract

India has taken strong and early public health measures for arresting the spread of the COVID-19 epidemic. With only 536 COVID-19 cases and 11 fatalities, India—a democracy of 1.34 billion people—took the historic decision of a 21-day national lockdown on March 25. The lockdown was further extended to May 3rd, soon after the analysis of this paper was completed. The lockdown was again extended to May 18 while this paper was being revised.

In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe nonpharmaceutical interventions. We compare effects of hypothetical durations of the lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, has a high chance of reducing the total number of COVID-19 infected cases in the short-term, and buy India invaluable time to prepare its healthcare and disease monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured in terms of reducing the number of active cases). From an epidemiological perspective, a longer lockdown between 42-56 days is preferable to substantially “flatten the curve” when compared to 21-28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and thus, inform policymakers about one aspect of this multi-faceted decision-making problem. We recognize that the collateral damage of a lockdown from social and economic perspective could be massive.

We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods and tools that can enable data-driven policymaking during a pandemic. Our contribution to data science includes an interactive and dynamic RShiny app with short- and long-term projections updated daily that can help inform policy and practice related to COVID-19 in India. We make our prediction code freely available for reproducible science and for other researchers to use these tools for their own prediction and data visualization work.

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