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The use of mixture density networks in the emulation of complex epidemiological individual-based models

Abstract:

Complex, highly-computational, individual-based models are abundant in epidemiology. For epidemics such as macro-parasitic diseases, detailed modelling of human behaviour and pathogen life-cycle are required in order to produce accurate results. This can often lead to models that are computationally-expensive to analyse and perform model fitting, and often require many simulation runs in order to build up sufficient statistics. Emulation can provide a more computationally-efficient output of ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Role:
Author
ORCID:
0000-0003-2665-0127
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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Big Data Institute
Role:
Author
ORCID:
0000-0001-5962-4238
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Role:
Author
ORCID:
0000-0002-0132-5005
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Role:
Author
ORCID:
0000-0003-4785-8998
Publisher:
Public Library of Science (PLoS)
Journal:
PLoS Computational Biology More from this journal
Volume:
16
Issue:
3
Article number:
e1006869
Place of publication:
United States
Publication date:
2020-03-16
Acceptance date:
2020-02-20
DOI:
EISSN:
1553-7358
ISSN:
1553-734X
Pmid:
32176687
Language:
English
Keywords:
Pubs id:
1094582
Local pid:
pubs:1094582
Deposit date:
2020-04-28

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