Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning: An AI-Driven Control Approach Compatible with Existing Disease and Network Models.

There has been intense debate about lockdown policies in the context of Covid-19 for limiting damage both to health and to the economy. We present an AI-driven approach for generating optimal lockdown policies that control the spread of the disease while balancing both health and economic costs. Furthermore, the proposed reinforcement learning approach automatically learns those policies, as a function of disease and population parameters. The approach accounts for imperfect lockdowns, can be used to explore a range of policies using tunable parameters, and can be easily extended to fine-grained lockdown strictness. The control approach can be used with any compatible disease and network simulation models.