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.
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Acknowledgements
This work was supported by the Vinnova grant on “AI-supported design of more effective intervention strategies” and the Digital Futures EXTREMUM project.
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Bampa, M., Fasth, T., Magnusson, S., Papapetrou, P. (2022). EpidRLearn: Learning Intervention Strategies for Epidemics with Reinforcement Learning. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds) Artificial Intelligence in Medicine. AIME 2022. Lecture Notes in Computer Science(), vol 13263. Springer, Cham. https://doi.org/10.1007/978-3-031-09342-5_18
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DOI: https://doi.org/10.1007/978-3-031-09342-5_18
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