Reinforcement learning-based decision support system for COVID-19.

Reinforcement learning-based decision support system for COVID-19.

Regina Padmanabhan, Nader Meskin, Tamer Khattab, Mujahed Shraim, Mohammed Al-Hitmi

Globally, informed decision on the most effective set of restrictions for the containment of COVID-19 has been the subject of intense debates. There is a significant need for a structured dynamic framework to model and evaluate different intervention scenarios and how they perform under different national characteristics and constraints. This work proposes a novel optimal decision support framework capable of incorporating different interventions to minimize the impact of widely spread respiratory infectious pandemics, including the recent COVID-19, by taking into account the pandemic's characteristics, the healthcare system parameters, and the socio-economic aspects of the community. The theoretical framework underpinning this work involves the use of a reinforcement learning-based agent to derive constrained optimal policies for tuning a closed-loop control model of the disease transmission dynamics.

2021

covid-19, Optimal control, reinforcement learning, Active intervention, Differential disease severity

10.1016/j.bspc.2021.102676

Biomedical signal processing and control