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Quantifying aggregated uncertainty in Plasmodim falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation

Abstract:

Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or admi...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1371/journal.pcbi.1000724

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Institution:
University of Oxford
Research group:
Spatial Ecology and Epidemiology Group
Department:
Mathematical,Physical & Life Sciences Division - Zoology
Role:
Author
More by this author
Institution:
University of Oxford
Research group:
Spatial Ecology and Epidemiology Group
Department:
Mathematical,Physical & Life Sciences Division - Zoology
Role:
Author
More by this author
Institution:
University of Oxford
Research group:
Spatial Ecology and Epidemiology Group
Department:
Mathematical,Physical & Life Sciences Division - Zoology
Role:
Author
More from this funder
Funding agency for:
Anand P. Patil
More from this funder
Funding agency for:
Simon I. Hay
Publisher:
Public Library of Science Publisher's website
Journal:
PLoS Computational Biology Journal website
Volume:
6
Issue:
4
Pages:
Article: e1000724
Publication date:
2010-04-05
DOI:
EISSN:
1553-7358
ISSN:
1553-734X
URN:
uuid:ac39c604-ec93-4252-8120-2f1986bba455
Local pid:
ora:3656

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