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Geostatistical methods for disease mapping and visualisation using data from spatio‐temporally referenced prevalence surveys

Abstract:

In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram‐based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood‐based and Bayesian methods of inference, showing...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1111/insr.12268

Authors


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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM; Tropical Medicine
Role:
Author
ORCID:
0000-0003-3725-6088
More from this funder
Funding agency for:
Snow, RW
Publisher:
Wiley Publisher's website
Journal:
International Statistical Review Journal website
Volume:
86
Issue:
3
Pages:
571-597
Publication date:
2018-04-25
Acceptance date:
2018-02-17
DOI:
EISSN:
1751-5823
ISSN:
0306-7734
Pubs id:
pubs:858819
URN:
uri:e4caf390-8caf-40e2-a451-5b5f98837016
UUID:
uuid:e4caf390-8caf-40e2-a451-5b5f98837016
Local pid:
pubs:858819

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