Deep Reinforcement Learning for Large-Scale Epidemic Control

Research output: Chapter in Book/Report/Conference proceedingConference paperResearch

Abstract

Epidemics of infectious diseases are an important threat to public health and global economies.Yet, the development of prevention strategies remains a challenging process, as epidemics are non-linear and complex processes.For this reason, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in the context of pandemic influenza.Firstly, we construct a new epidemiological meta-population model, with 379 patches (one for each administrative district in Great Britain), that adequately captures the infection process of pandemic influenza.Our model balances complexity and computational efficiency such that the use of reinforcement learning techniques becomes attainable.Secondly, we set up a ground truth such that we can evaluate the performance of the "Proximal Policy Optimization" algorithm to learn in a single district of this epidemiological model.Finally, we consider a large-scale problem, by conducting an experiment where we aim to learn a joint policy to control the districts in a community of 11 tightly coupled districts, for which no ground truth can be established.This experiment shows that deep reinforcement learning can be used to learn mitigation policies in complex epidemiological models with a large state space.Moreover, through this experiment, we demonstrate that there can be an advantage to consider collaboration between districts when designing prevention strategies.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track
Subtitle of host publicationEuropean Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V
EditorsYuxiao Dong, Georgiana Ifrim, Dunja Mladenić, Craig Saunders, Sofie Van Hoecke
Place of PublicationGhent, Belgium
PublisherSpringer
Pages155-170
Number of pages16
Volume5
ISBN (Electronic)978-3-030-67670-4
ISBN (Print)978-3-030-67669-8
DOIs
Publication statusPublished - 2021
EventThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Ghent, Belgium
Duration: 14 Sep 202018 Sep 2020
https://ecmlpkdd2020.net/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
Volume12461
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated titleECMLPKDD2020
CountryBelgium
CityGhent
Period14/09/2018/09/20
Internet address

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