ABSTRACT
With more than 140 million people infected globally and 3 million deaths, the COVID 19 pandemic has left a lasting impact. A modern response to a pandemic of such proportions needs to focus on exploiting all available data to inform the response in real-time and allow evidence-based decision-making. The intermittent lockdowns in the last 13 months have created economic adversity to prevent anticipated large-scale mortality and relax the lockdowns have been an attempt at recovering and balancing economic needs and public health realities. This article is a comprehensive case study of the outbreak in the city limits of Pune, Maharashtra, India, to understand the evolution of the disease and transmission dynamics starting from the first case on March 9, 2020. A unique collaborative effort between the Pune Municipal Corporation (PMC), a government entity, and the Pune knowledge Cluster (PKC) allowed us to layout a context for outbreak response and intervention. We report here how access to granular data for a metropolitan city with pockets of very high-density populations will help analyze, in real-time, the dynamics of the pandemic and forecasts for better management and control of SARS-CoV-2. Outbreak data analytics resulted in a real-time data visualization dashboard for accurate information dissemination for public access on the epidemics progress. As government agencies craft testing and vaccination policies and implement intervention strategies to mitigate a second wave, our case study underscores the criticality of data quality and analytics to decode community transmission of COVID-19.
ABSTRACT
BackgroundReal-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 Punes 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 Indias nationwide lockdown and 22% (IRR 0.78, 95% CI 0.73 - 0.84) during Punes 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.
