Reinforcement learning-based decision support system for COVID-19
الملخص
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.
معرّف المصادر الموحد
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105497577&origin=inwardالمجموعات
- أبحاث فيروس كورونا المستجد (كوفيد-19) [811 items ]
- الصحة العامة [411 items ]