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Modelling the first wave of COVID-19 in India
Dhiraj Kumar Hazra1,2,3, Bhalchandra S Pujari4, Snehal M Shekatkar4
1The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, INDIA.
Plos Computational Biology
|October 24, 2022
Summary
Estimating COVID-19
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- Accurate estimation of COVID-19 burden in India is challenging due to undercounted cases and deaths.
- The first wave of COVID-19 in India (Jan 30, 2020 - Feb 15, 2021) requires robust analytical models.
Purpose of the Study:
- To estimate the true burden of COVID-19 during its first wave in India.
- To analyze the impact of non-pharmaceutical interventions (NPIs) and testing on disease spread.
- To determine age-specific infection-fatality ratios (IFR) and overall infection prevalence.
Main Methods:
- Utilized the INDSCI-SIM model, a 9-component, age-stratified, contact-structured epidemiological model.
- Employed Bayesian methods for optimal fitting to daily reported cases and deaths.
- Incorporated data from serological surveys and accounted for lockdowns, testing increases, and undercounting.
Main Results:
- Deaths were undercounted by a factor of 2-5 (average 2.2), with higher undercounting in urban areas.
- Cases were undercounted by a factor of 20-25 by the end of the first wave.
- Estimated overall infection prevalence at approximately 35% by the end of the first wave, with an IFR of 0.05-0.15.
Conclusions:
- The study provides crucial insights into the underestimation of COVID-19's first wave impact in India.
- Findings highlight the importance of accounting for undercounting in epidemiological assessments.
- Results contribute to understanding the long-term trajectory of COVID-19 in India and inform public health strategies.
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