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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.

More information

Type
Journal Article
Author
Giorgi E
Fronterrè C
Macharia PM
Alegana VA
Snow RW
Diggle PJ
Year of Publication
2021
Journal
Journal of The Royal Society Interface
Volume
18
Issue
179
Number of Pages
20210104
Language
eng
ISSN Number
1742-5662
DOI
10.1098/rsif.2021.0104
Publication Language
eng