ArticlePLoS computational biology2026
An age-structured spatially varying coefficient model for high-resolution mapping of vaccination coverage.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
High-resolution maps of vaccination coverage are valuable for uncovering heterogeneities in coverage to inform vaccine delivery strategies. Coverage maps stratified by age can reveal additional heterogeneities in the timeliness of vaccination and critical immunity gaps among birth cohorts. Here, we propose a spatially varying coefficient model relying on a Bayesian approach for age-structured mapping of vaccination coverage using geolocated individual level household survey and geospatial covariate data. Our flexible modelling framework includes parameterizations capturing spatial (non-)stationarity in differences in coverage between age groups, as well as a modification to allow coverage mapping for single age points through the inclusion of a smoother over age. The proposed models are fitted using the INLA-SPDE approach implemented in the inlabru package in R. We choose between competing model parameterizations by examining their out-of-sample predictive performance via cross-validation and using Bayesian model choice criteria. The methodology is applied to age-structured mapping of measles vaccination coverage in Cote d'Ivoire using the 2021 Demographic and Health Survey. Our results reveal a significant delay in measles vaccination in the first year of life and substantial spatial differences in coverage by age, highlighting the need for targeted interventions to achieve equity and attain vaccine-derived immunity goals.
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