Deep reinforcement learning for large-scale epidemic control
- URL: http://arxiv.org/abs/2003.13676v1
- Date: Mon, 30 Mar 2020 17:57:09 GMT
- Title: Deep reinforcement learning for large-scale epidemic control
- Authors: Pieter Libin, Arno Moonens, Timothy Verstraeten, Fabian
Perez-Sanjines, Niel Hens, Philippe Lemey, Ann Now\'e
- Abstract summary: We investigate a deep reinforcement learning approach to automatically learn prevention strategies in the context of pandemic influenza.
Our model balances complexity and computational efficiency such that the use of reinforcement learning techniques becomes attainable.
This experiment shows that deep reinforcement learning can be used to learn mitigation policies in complex epidemiological models with a large state space.
- Score: 0.3694429692322631
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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