A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping.

ABSTRACT: The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of 'leaving no one behind' has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and 'coldspots' of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the

SUBMITTER: Utazi CE 

PROVIDER: S-EPMC6745613 | biostudies-literature | 2019 Oct-Nov

REPOSITORIES: biostudies-literature