Journal article
Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization
- Abstract:
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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
Actions
Access Document
- Files:
-
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(Accepted manuscript, pdf, 2.0MB)
-
- Publisher copy:
- 10.1098/rsif.2017.0520
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Authors
Funding
+ Medical Research Council
More from this funder
Funding agency for:
Gething, PW
Grant:
OPP1068048, OPP1106023
+ Department for International Development
More from this funder
Funding agency for:
Gething, PW
Grant:
OPP1068048, OPP1106023
+ European Union
More from this funder
Funding agency for:
Gething, PW
Grant:
OPP1068048, OPP1106023
+ Bill and Melinda Gates Foundation
More from this funder
Funding agency for:
Gething, PW
Grant:
OPP1068048, OPP1106023
Bibliographic Details
- 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:
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1742-5662
- ISSN:
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1742-5689
- Pmid:
-
28931634
- Source identifiers:
-
730703
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
pubs:730703
- UUID:
-
uuid:5b351ce0-780a-4a65-aa76-b082bb9edb2a
- Local pid:
- pubs:730703
- Deposit date:
- 2017-09-29
Terms of use
- Copyright holder:
- Bhatt et al
- Copyright date:
- 2017
- Notes:
-
Copyright © 2017 The Authors.
Published by the Royal Society. This is the accepted manuscript version of the article. The final version is available online from the Royal Society at: https://doi.org/10.1098/rsif.2017.0520
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