SVBR is a new hierarchical Bayesian method that treats buffer radii as unknown spatially varying parameters, improves parameter recovery in simulations, and reveals spatial heterogeneity in healthcare access effects on antenatal care in Madagascar.
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A two-step framework combines stacked hurdle random forest models for local severity prediction with semi-parametric spatio-temporal modeling to reconstruct large-scale disease dynamics from imperfect indicators, demonstrated on sugar beet yellows in France.
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Modeling Spatial Heterogeneity in Exposure Buffers and Risk: A Hierarchical Bayesian Approach
SVBR is a new hierarchical Bayesian method that treats buffer radii as unknown spatially varying parameters, improves parameter recovery in simulations, and reveals spatial heterogeneity in healthcare access effects on antenatal care in Madagascar.
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Predicting disease severity and large-scale spread from coupled severity measurements and imperfect indicators: Application to beet yellows
A two-step framework combines stacked hurdle random forest models for local severity prediction with semi-parametric spatio-temporal modeling to reconstruct large-scale disease dynamics from imperfect indicators, demonstrated on sugar beet yellows in France.