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In the context of the ongoing Covid-19 pandemic, several reports and studieshave attempted to model and predict the spread of the disease. There is alsointense debate about policies for limiting the damage, both to health and tothe economy. On the one hand, the health and safety of the population is theprincipal consideration for most countries. On the other hand, we cannot ignorethe potential for long-term economic damage caused by strict nation-widelockdowns. In this working paper, we present a quantitative way to computelockdown decisions for individual cities or regions, while balancing health andeconomic considerations. Furthermore, these policies are learnt automaticallyby the proposed algorithm, as a function of disease parameters (infectiousness,gestation period, duration of symptoms, probability of death) and populationcharacteristics (density, movement propensity). We account for realisticconsiderations such as imperfect lockdowns, and show that the policy obtainedusing reinforcement learning is a viable quantitative approach towardslockdowns.

Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning