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Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization

Abstract:

Maps of infectious disease-charting spatial variations in the force of infection, degree of endemicity and the burden on human health-provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is Gaussian process regression, a mathematical framework ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1098/rsif.2017.0520

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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Author
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Funding agency for:
Gething, PW
Grant:
OPP1068048, OPP1106023
More from this funder
Funding agency for:
Gething, PW
Grant:
OPP1068048, OPP1106023
More from this funder
Funding agency for:
Gething, PW
Grant:
OPP1068048, OPP1106023
More from this funder
Funding agency for:
Gething, PW
Grant:
OPP1068048, OPP1106023
Publisher:
Royal Society Publisher's website
Journal:
Interface Journal website
Volume:
14
Issue:
134
Article number:
20170520
Publication date:
2017-09-20
Acceptance date:
2017-08-30
DOI:
EISSN:
1742-5662
ISSN:
1742-5689
Pmid:
28931634
Source identifiers:
730703
Language:
English
Keywords:
Pubs id:
pubs:730703
UUID:
uuid:5b351ce0-780a-4a65-aa76-b082bb9edb2a
Local pid:
pubs:730703
Deposit date:
2017-09-29

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