  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.
