ArticleJournal of the Royal Society, Interface2021
Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Bayesian Hierarchical Modeling Approaches for Combining Information From Multiple Data Sources to Produce Annual Estimates of National Immunization Coverage.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Spatio-temporal analysis and geostatistical modelling of onchocerciasis prevalence in Nigeria to support elimination efforts.PLoS neglected tropical diseases · 2026Article
- Toward a Principled Workflow for Prevalence Mapping Using Household Survey Data.Journal of survey statistics and methodology · 2026Article
- The impact of the termination of Lymphatic Filariasis mass drug administration on Soil-transmitted Helminth prevalence in school children in Malawi.PLoS neglected tropical diseases · 2026Article
- Geospatial Mapping of Pediatric Febrile Illness in Uganda to Inform Precision Public Health Interventions.Open forum infectious diseases · 2026Article
- SEEDNet: Covariate-free multi-country settlement-level epidemiological estimates datasets for network analysis.Scientific data · 2025Article
- Understanding the impact of covariates for trachoma prevalence prediction using geostatistical methods.BMC global and public health · 2025Article
- Spatiotemporal analysis of schistosomiasis and soil-transmitted helminth distribution in three highly endemic provinces in Angola.PLoS neglected tropical diseases · 2025Article
- Model-Based Geostatistical Mapping of the Prevalence of Onchocerca volvulus in Cameroon between 1971 and 2020.PLoS neglected tropical diseases · 2025Article
- Using ESPEN data for evidence-based control of neglected tropical diseases in sub-Saharan Africa: A comprehensive model-based geostatistical analysis of soil-transmitted helminths.PLoS neglected tropical diseases · 2025Article
- Paediatric anaemia in rural Kenya and the role of travel time to emergency care services.Frontiers in epidemiology · 2025Article
- Understanding the impact of covariates on the classification of implementation units for soil-transmitted helminths control: a case study from Kenya.BMC medical research methodology · 2024Article
- Prevalence and determinants of double and triple burden of malnutrition among mother-child pairs in Malawi: a mapping and multilevel modelling study.Public health nutrition · 2024Article
- Identifying childhood malaria hotspots and risk factors in a Nigerian city using geostatistical modelling approach.Scientific reports · 2024Article
- Bayesian spatio-temporal analysis of malaria prevalence in children between 2 and 10 years of age in Gabon.Malaria journal · 2024Article
- Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda.One health (Amsterdam, Netherlands) · 2023Article
- Space-time modelling of monthly malaria incidence for seasonal associated drivers and early epidemic detection in Southern Ethiopia.Malaria journal · 2023Article
- Geostatistical analysis of active human cysticercosis: Results of a large-scale study in 60 villages in Burkina Faso.PLoS neglected tropical diseases · 2023Article
- Using model-based geostatistics for assessing the elimination of trachoma.PLoS neglected tropical diseases · 2023Article
- Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania.Scientific reports · 2023Article
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6 authors.
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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.
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