EpidRLearn: Learning Intervention Strategies for Epidemics with Reinforcement Learning
20th International Conference on Artificial Intelligence in Medicine, AIME 2022
; 13263 LNAI:189-199, 2022.
Article
in English
| Scopus | ID: covidwho-1971533
ABSTRACT
Epidemics of infectious diseases can pose a serious threat to public health and the global economy. Despite scientific advances, containment and mitigation of infectious diseases remain a challenging task. In this paper, we investigate the potential of reinforcement learning as a decision making tool for epidemic control by constructing a deep Reinforcement Learning simulator, called EpidRLearn, composed of a contact-based, age-structured extension of the SEIR compartmental model, referred to as C-SEIR. We evaluate EpidRLearn by comparing the learned policies to two deterministic policy baselines. We further assess our reward function by integrating an alternative reward into our deep RL model. The experimental evaluation indicates that deep reinforcement learning has the potential of learning useful policies under complex epidemiological models and large state spaces for the mitigation of infectious diseases, with a focus on COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
COVID-19; Mitigation policies; Reinforcement learning; Decision making; Deep learning; Disease control; Health risks; Learning systems; Compartmental modelling; Decision making tool; Epidemic control; Global economies; Infectious disease; Intervention strategy; Learning simulators; Reinforcement learnings; Scientific advances
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
20th International Conference on Artificial Intelligence in Medicine, AIME 2022
Year:
2022
Document Type:
Article
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