Robust Lock-Down Optimization for COVID-19 Policy Guidance

Ankit Bhardwaj, Han-Ching Ou, Haipeng Chen, Shahin Jabbari, Milind Tambe, Rahul Panicker, Alpan Raval

Abstract:

As the COVID-19 outbreak continues to pose a serious worldwide threat, numerous governments choose to establish lockdowns in order to reduce disease transmission. However, imposing the strictest possible lock-down at all times has dire
economic consequences, especially in areas with widespread
poverty. In fact, many countries and regions have started
charting paths to ease lock-down measures. Thus, planning
efficient ways to tighten and relax lock-downs is a crucial and
urgent problem. We develop a reinforcement learning based
approach that is (1) robust to a range of parameter settings,
and (2) optimizes multiple objectives related to different aspects of public health and economy, such as hospital capacity
and delay of the disease. The absence of a vaccine or a cure
for COVID to date implies that the infected population cannot
be reduced through pharmaceutical interventions. However,
non-pharmaceutical interventions (lock-downs) can slow disease spread and keep it manageable. This work focuses on
how to manage the disease spread without severe economic
consequences.