- 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 ...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
-
(pdf, 2.0MB)
-
- Publisher copy:
- 10.1098/rsif.2017.0520
-
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- Publisher:
- Royal Society Publisher's website
- Journal:
- Interface Journal website
- Volume:
- 14
- Issue:
- 134
- Pages:
- Article: 20170520
- Publication date:
- 2017-09-20
- Acceptance date:
- 2017-08-30
- DOI:
- EISSN:
-
1742-5662
- ISSN:
-
1742-5689
- Pubs id:
-
pubs:730703
- URN:
-
uri:5b351ce0-780a-4a65-aa76-b082bb9edb2a
- UUID:
-
uuid:5b351ce0-780a-4a65-aa76-b082bb9edb2a
- Local pid:
- pubs:730703
- Paper number:
- 134
- Language:
- English
- Keywords:
- 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
Journal article
Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization
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