  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
effective 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 behavior of the population and
its 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, as well as using reinforcement learning. Our
analysis can help assess the socio-economic consequences of such interventions,
and provide useful ideas and insights to policy makers for better decision
making.
