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Mapping malaria by sharing spatial information between incidence and prevalence data sets

As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low-prevalence areas are increasingly needed. For low-burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons.

Citation:
Lucas TCD, Nandi AK, Chestnutt EG, ...... Rumisha SF,  Python A, Arambepola R, ...... Amratia P, Battle KE, Cameron E, Gething PW, Weiss DJ. How et al. Mapping malaria by sharing spatial information between incidence and prevalence data sets. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2021;70(3):733-49.

Keywords:
Disaggregation regression; disease mapping; geostatistics; joint modelling; spatial statistics

Abstract:
As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low-prevalence areas are increasingly needed. For low-burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons.