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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

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Publisher copy:
10.1098/rstb.2018.0277

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Author
ORCID:
0000-0002-3437-759X
More from this funder
Name:
National Institute for Health
Grant:
1 R01 GM105247-01
More from this funder
Name:
National Science Foundation
Grant:
EvolutionofInfectiousDiseaseprogram
Ecology
More from this funder
Name:
Biotechnology and Biological Sciences Research Council
Grant:
BB/K010972/4
Publisher:
Royal Society Publishing
Journal:
Philosophical transactions of the Royal Society of London. Series B, Biological sciences More from this journal
Volume:
374
Issue:
1776
Article number:
20180277
Publication date:
2019-05-20
Acceptance date:
2019-02-26
DOI:
EISSN:
1471-2970
ISSN:
0962-8436
Pmid:
31104604
Language:
English
Keywords:
Pubs id:
pubs:1004311
UUID:
uuid:91276edf-3e13-404b-b677-5db460880d67
Local pid:
pubs:1004311
Source identifiers:
1004311
Deposit date:
2019-07-03

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