  In the context of the ongoing Covid-19 pandemic, several reports and studies
have attempted to model and predict the spread of the disease. There is also
intense debate about policies for limiting the damage, both to health and to
the economy. On the one hand, the health and safety of the population is the
principal consideration for most countries. On the other hand, we cannot ignore
the potential for long-term economic damage caused by strict nation-wide
lockdowns. In this working paper, we present a quantitative way to compute
lockdown decisions for individual cities or regions, while balancing health and
economic considerations. Furthermore, these policies are learnt automatically
by the proposed algorithm, as a function of disease parameters (infectiousness,
gestation period, duration of symptoms, probability of death) and population
characteristics (density, movement propensity). We account for realistic
considerations such as imperfect lockdowns, and show that the policy obtained
using reinforcement learning is a viable quantitative approach towards
lockdowns.

Physics and Society;Artificial Intelligence;Machine Learning;Populations and Evolution;Machine Learning