Effect of lockdown interventions to control the COVID-19 epidemic in India


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Received date: 1st October 2020

The pandemic caused by the novel Coronavirus SARS-CoV2 has been responsible for life-threatening health complications and extreme pressure on healthcare systems. While preventive and definite curative medical interventions are yet to arrive, Non-Pharmaceutical Interventions (NPIs) like physical isolation, quarantine, and drastic social measures imposed by governing agencies are useful in arresting the spread of infections in a population. In densely populated countries like India, lockdown interventions are partially effective due to social and administrative complexities. Using detailed demographic data, we present an agent-based model to imitate the population's behavior and mobility features, even under intervention. We demonstrate the effectiveness of contact tracing policies and how our model efficiently relates to empirical findings on testing efficiency. We also present various lockdown intervention strategies for mitigation – using the bare number of infections, the effective reproduction rate, and using reinforcement learning. Our analysis can help assess socio-economic consequences of such interventions and provide useful ideas and insights to policymakers for better decision-making.

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This is an abstract of a preprint hosted on a preprint server, which is currently undergoing peer review at Scientific Reports. The findings have yet to be thoroughly evaluated, nor has a decision on ultimate publication been made. Therefore, the results reported should not be considered conclusive, and these findings should not be used to inform clinical practice, or public health policy, or be promoted as verified information.

Scientific Reports

Nature Research, Springer Nature