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Modelling the first wave of COVID-19 in India

Hazra, DK and Pujari, BS and Shekatkar, SM and Mozaffer, F and Sinha, S and Guttal, V and Chaudhuri, P and Menon, GI (2022) Modelling the first wave of COVID-19 in India. In: PLoS computational biology, 18 (10). e1010632.

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Official URL: https://doi.org/10.1371/journal.pcbi.1010632


Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. Here, we use a 9-component, age-stratified, contact-structured epidemiological compartmental model, which we call the INDSCI-SIM model, to analyse the first wave of COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to daily reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions (NPIs), an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05-0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 35 of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

Item Type: Journal Article
Publication: PLoS computational biology
Publisher: NLM (Medline)
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Bayes theorem; communicable disease control; epidemiology; human; India; pandemic, Bayes Theorem; Communicable Disease Control; COVID-19; Humans; India; Pandemics
Department/Centre: Division of Biological Sciences > Centre for Ecological Sciences
Date Deposited: 04 Jan 2023 04:33
Last Modified: 04 Jan 2023 04:33
URI: https://eprints.iisc.ac.in/id/eprint/78673

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