The number of confirmed cases worldwide stands at a staggering 1,930,780 (as of 9:20 a.m. Keywords: basic reproduction number, coronavirus, credible interval, India, intervention forecasting, SIR modelįour months since the first case of COVID-19 in Wuhan, China, the SARS-CoV-2 virus has engulfed the world and has been declared a global pandemic (World Health Organization, 2020b). 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 models focus solely on projecting the number of COVID-19 infections and thus inform policymakers about one aspect of this multifaceted decision-making problem. A longer lockdown from 42–56 days is preferable to substantially ‘flatten the curve’ when compared to 21–28 days of lockdown. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases).
We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its health care and disease-monitoring system.
We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. In this article, 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. The lockdown was first extended to May 3 soon after the analysis of this article was completed, and then to May 18 while this article was being revised. With only 536 COVID-19 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25, 2020.
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