Impact of National and Regional Lockdowns on Growth of COVID-19 Cases in COVID-Hotspot City of Pune in Western India: A Real-World Data Analysis

Abstract

AbstractBackgroundReal-world data assessing the impact of lockdowns on COVID-19 cases remain limited from resource-limited settings. We examined growth of incident confirmed COVID-19 cases before, during and after lockdowns in Pune, a city in western India with 3.1 million population that reported the largest COVID-19 burden at the peak of the pandemic.MethodsUsing anonymized individual-level data captured by Pune’s public health surveillance program between February 1st and September 15th 2020, we assessed weekly incident COVID-19 cases, infection rates, and epidemic curves by lockdown status (overall and by sex, age, and population density) and modelled the natural epidemic using the 9-compartmental model INDSCI-SIM. Effect of lockdown on incident cases was assessed using multilevel Poisson regression. We used geospatial mapping to characterize regional spread.FindingsOf 241,629 persons tested for SARS-CoV-2, the COVID-19 disease rate was 267.0 (95% CI 265.3 – 268.8) per 1000 persons. Epidemic curves and geospatial mapping showed delayed peak of the cases by approximately 8 weeks during the lockdowns as compared to modelled natural epidemic. Compared to a subsequent unlocking period, incident COVID-19 cases 43% lower (IRR 0.57, 95% CI 0.53 – 0.62) during India’s nationwide lockdown and 22% (IRR 0.78, 95% CI 0.73 – 0.84) during Pune’s regional lockdown and was uniform across age groups and population densities.ConclusionLockdowns slowed the growth of COVID-19 cases in population dense, urban region in India. Additional analysis from rural and semi-rural regions of India and other resource-limited settings are needed.