An age-structured spatially varying coefficient model for high-resolution mapping of vaccination coverage

Area of Science:

• Epidemiology
• Biostatistics
• Geographic Information Systems (GIS)

Background:

• High-resolution vaccination coverage maps are essential for identifying disparities and optimizing vaccine delivery strategies.
• Age-stratified maps further illuminate vaccination timeliness and immunity gaps within birth cohorts.

Purpose of the Study:

• To develop and apply a Bayesian spatially varying coefficient model for age-structured mapping of vaccination coverage.
• To analyze spatial and age-related heterogeneities in measles vaccination coverage in Cote d'Ivoire.

Main Methods:

• Utilized a Bayesian approach with a spatially varying coefficient model incorporating geolocated survey and covariate data.
• Employed the Integrated Nested Laplace Approximation-Stochastic Partial Differential Equation (INLA-SPDE) approach within the inlabru R package.
• Assessed model performance using cross-validation and Bayesian model choice criteria.

Main Results:

• The study identified a significant delay in measles vaccination during the first year of life.
• Substantial spatial variations in vaccination coverage across different age groups were observed.
• The model effectively captured age-specific and spatial heterogeneities in vaccine coverage.

Conclusions:

• Age-structured high-resolution maps are critical for understanding vaccination coverage dynamics.
• Findings highlight the need for targeted interventions to address vaccination delays and spatial inequities.
• Achieving equitable vaccine-derived immunity requires addressing identified coverage gaps.