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Journal article
Predictions, role of interventions and effects of a historic national lockdown in India’s response to the COVID-19 pandemic: data science call to arms
Harvard data science review, v 2020(Suppl 1)
01 Jan 2020
PMID: 32607504
Abstract
With only 536 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25. The lockdown was first extended to May 3 soon after the analysis of this paper was completed, and then 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 non-pharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. 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 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 by reduction in the number of cases). 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 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 software products are available at
covind19.org
.
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Details
- Title
- Predictions, role of interventions and effects of a historic national lockdown in India’s response to the COVID-19 pandemic: data science call to arms
- Creators
- Debashree Ray - Johns Hopkins UniversityMaxwell Salvatore - Johns Hopkins UniversityRupam Bhattacharyya - Johns Hopkins UniversityLili Wang - Johns Hopkins UniversityJiacong Du - Johns Hopkins UniversityShariq Mohammed - Johns Hopkins UniversitySoumik Purkayastha - Johns Hopkins UniversityAritra Halder - Johns Hopkins UniversityAlexander Rix - Johns Hopkins UniversityDaniel Barker - Johns Hopkins UniversityMichael Kleinsasser - Johns Hopkins UniversityYiwang Zhou - Johns Hopkins UniversityDebraj Bose - Johns Hopkins UniversityPeter Song - Johns Hopkins UniversityMousumi Banerjee - Johns Hopkins UniversityVeerabhadran Baladandayuthapani - Johns Hopkins UniversityParikshit Ghosh - Johns Hopkins UniversityBhramar Mukherjee - Johns Hopkins University
- Publication Details
- Harvard data science review, v 2020(Suppl 1)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Other Identifier
- 991021448169704721