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Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies

Abstract:

The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all possible epidemics are also best for each possible epidemic. Reinforcement learning (RL) and Monte Carlo control have been used to develop machine-readable context-dependent solutions for complex problems with many possible realizations ranging f...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's Version

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Publisher copy:
10.1098/rstb.2018.0277

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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM
Subgroup:
BDI-NDM
Role:
Author
ORCID:
0000-0002-3437-759X
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Grant:
Ecology and Evolution of Infectious Disease program
Publisher:
The Royal Society Publishing Publisher's website
Journal:
Philosophical transactions of the Royal Society of London. Series B, Biological sciences Journal website
Volume:
374
Issue:
1776
Pages:
Article: 20180277
Publication date:
2019-05-20
Acceptance date:
2019-02-26
DOI:
EISSN:
1471-2970
ISSN:
0962-8436
Pubs id:
pubs:1004311
URN:
uri:91276edf-3e13-404b-b677-5db460880d67
UUID:
uuid:91276edf-3e13-404b-b677-5db460880d67
Local pid:
pubs:1004311

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