Age-structured impact of social distancing on the COVID-19 epidemic in India

TL;DR

This study uses an age-structured model with social contact data to evaluate COVID-19 social distancing measures in India, highlighting the importance of sustained lockdowns and age-specific impacts.

Contribution

It introduces an age-structured SIR model with survey-based social contact matrices to assess COVID-19 mitigation strategies in India.

Findings

A three-week lockdown is insufficient to prevent resurgence.

Sustained lockdowns with periodic relaxation are more effective.

Social contact structures significantly influence epidemic outcomes.

Abstract

The outbreak of the novel coronavirus, COVID-19, has been declared a pandemic by the WHO. The structures of social contact critically determine the spread of the infection and, in the absence of vaccines, the control of these structures through large-scale social distancing measures appears to be the most effective means of mitigation. Here we use an age-structured SIR model with social contact matrices obtained from surveys and Bayesian imputation to study the progress of the COVID-19 epidemic in India. The basic reproductive ratio R0 and its time-dependent generalization are computed based on case data, age distribution and social contact structure. The impact of social distancing measures - workplace non-attendance, school closure, lockdown - and their efficacy with durations are then investigated. A three-week lockdown is found insufficient to prevent a resurgence and, instead, protocols of sustained lockdown with periodic relaxation are suggested. Forecasts are provided for the reduction in age-structured morbidity and mortality as a result of these measures. Our study underlines the importance of age and social contact structures in assessing the country-specific impact of mitigatory social distancing.