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Data-driven scenario-based model projections and management of the May 2021 Covid-19 resurgence in India

Bibliographic Data

ID19594797
AuthorsElizabeth Michael (0000-0002-9473-4245, University of South Florida, corresponding author), Ken Newcomb (0000-0001-7468-4079, University of South Florida), Anuj Mubayi (0000-0003-3936-3055, Illinois State University)
EditorsAbram Luther Wagner (0000-0003-4691-7802)
Year2022
Volume2
Issue12
Pagese0001382
Publication date2022-12-13
Open AccessYes
TypeARTICLE
VenuePLOS Global Public Health (JOURNAL)
Journal identifiersISSN: 2767-3375 • E-ISSN: 2767-3375
PublisherPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pgph.0001382
PMID36962906
OpenAlexW4311314692
LanguageEN
References cited25

The resurgence of the May 2021 COVID-19 wave in India not only pointed to the explosive speed with which SARS-CoV-2 can spread in vulnerable populations if unchecked, but also to the gross misreading of the status of the pandemic when decisions to reopen the economy were made in March 2021. In this combined modelling and scenario-based analysis, we isolated the population and policy-related factors underlying the May 2021 viral resurgence by projecting the growth and magnitude of the health impact and demand for hospital care that would have arisen if the spread was not impeded, and by evaluating the intervention options best able to curb the observed rapidly developing contagion. We show that only by immediately re-introducing a moderately high level of social mitigation over a medium-term period alongside a swift ramping up of vaccinations could the country be able to contain and ultimately end the pandemic safely. We also show that delaying the delivery of the 2 nd dose of the Astra Zeneca vaccine, as proposed by the Government of India, would have had only slightly more deleterious impacts, supporting the government’s decision to vaccinate a greater fraction of the population with at least a single dose as rapidly as possible. Our projections of the scale of the virus resurgence based on the observed May 2021 growth in cases and impacts of intervention scenarios to control the wave, along with the diverse range of variable control actions taken by state authorities, also exemplify the importance of shifting from the use of science and knowledge in an ad hoc reactive fashion to a more effective proactive strategy for assessing and managing the risk of fast-changing hazards, like a pandemic. We show that epidemic models parameterized with data can be used in combination with plausible intervention scenarios to enable such policy-making

Development economics · Economic growth · Economics · Environmental health · Geography · Pandemic · Political science · Population · Vaccination · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Law · Medicine · SARS-CoV-2 and COVID-19 Research · Virology

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Highly citedNo

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