Open AccessModel building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict
Author(s) -
Emanuele Giorgi,
Claudio Fronterrè,
Peter M. Macharia,
Victor A. Alegana,
Robert W. Snow,
Peter J. Diggle
Publication year - 2021
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 139
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2021.0104
Subject(s) - interpretability , covariate , computer science , estimator , context (archaeology) , predictive modelling , regression analysis , model selection , econometrics , feature selection , machine learning , statistical model , linear model , data mining , statistics , mathematics , geography , archaeology
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.