02142nas a2200277   4500000000100000008004100001260002200042653001800064653001500082653001700097653001900114653001700133653002700150100001300177700001700190700001600207700001500223700001200238700001400250245014500264856007000409300001300479490000700492520135100499022001401850       2021                            d  bThe Royal Society10aBiotechnology10aBiophysics10aBiochemistry10aBioengineering10aBiomaterials10aBiomedical Engineering1 aGiorgi E1 aFronterrè C1 aMacharia PM1 aAlegana VA1 aSnow RW1 aDiggle PJ00aModel building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict  uhttps://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0104  a202101040 v183 a<jats:p>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.</jats:p>  a1742-5662