Show simple item record

AuthorPadmanabhan, Regina
AuthorMeskin, Nader
AuthorKhattab, Tamer
AuthorShraim, Mujahed
AuthorAl-Hitmi, Mohammed
Available date2021-05-24T09:43:28Z
Publication Date2021-07-01
Publication NameBiomedical Signal Processing and Control
Identifierhttp://dx.doi.org/10.1016/j.bspc.2021.102676
CitationPadmanabhan, Regina, Nader Meskin, Tamer Khattab, Mujahed Shraim, and Mohammed Al-Hitmi. "Reinforcement Learning-based Decision Support System for COVID-19." Biomedical Signal Processing and Control (2021): 102676.
ISSN17468094
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105497577&origin=inward
URIhttp://hdl.handle.net/10576/18450
AbstractGlobally, 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.
Languageen
PublisherElsevier
SubjectActive intervention
COVID-19
Differential disease severity
Optimal control
Reinforcement learning
TitleReinforcement learning-based decision support system for COVID-19
TypeArticle
Volume Number68
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record