Reinforcement learning-based decision support system for COVID-19

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

doi:10.1016/j.bspc.2021.102676

Reinforcement learning-based decision support system for COVID-19

Biomed Signal Process Control. 2021 Jul:68:102676. doi: 10.1016/j.bspc.2021.102676. Epub 2021 Apr 27.

Authors

Regina Padmanabhan¹, Nader Meskin¹, Tamer Khattab¹, Mujahed Shraim², Mohammed Al-Hitmi¹

Affiliations

¹ Department of Electrical Engineering, Qatar University, Qatar.
² Department of Public Health, College of Health Sciences, QU Health, Qatar University, Qatar.

Abstract

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.

Keywords: Active intervention; COVID-19; Differential disease severity; Optimal control; Reinforcement learning.