Journal article
Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict
- Abstract:
- This paper provides statistical guidance on the development and application of model-based geostatistical methods for disease prevalence mapping. We illustrate the different stages of the analysis, from exploratory analysis to spatial prediction of prevalence, through a case study on malaria mapping in Tanzania. Throughout the paper, we distinguish between predictive modelling, whose main focus is on maximizing the predictive accuracy of the model, and explanatory modelling, where greater emphasis is placed on understanding the relationships between the health outcome and risk factors. We demonstrate that these two paradigms can result in different modelling choices. We also propose a simple approach for detecting over-fitting based on inspection of the correlation matrix of the estimators of the regression coefficients. To enhance the interpretability of geostatistical models, we introduce the concept of domain effects in order to assist variable selection and model validation. The statistical ideas and principles illustrated here in the specific context of disease prevalence mapping are more widely applicable to any regression model for the analysis of epidemiological outcomes but are particularly relevant to geostatistical models, for which the separation between fixed and random effects can be ambiguous.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.7MB, Terms of use)
-
- Publisher copy:
- 10.1098/rsif.2021.0104
Authors
- Publisher:
- Royal Society
- Journal:
- Journal of the Royal Society, Interface More from this journal
- Volume:
- 18
- Issue:
- 179
- Article number:
- 20210104
- Publication date:
- 2021-06-02
- Acceptance date:
- 2021-05-11
- DOI:
- EISSN:
-
1742-5662
- ISSN:
-
1742-5689
- Pmid:
-
34062104
- Language:
-
English
- Keywords:
- Pubs id:
-
1181215
- Local pid:
-
pubs:1181215
- Deposit date:
-
2021-07-16
- ARK identifier:
Terms of use
- Copyright holder:
- Giorgi et al.
- Copyright date:
- 2021
- Rights statement:
- © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
- Licence:
- CC Attribution (CC BY)
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