We present a Bayesian geospatial modeling framework developed for the synthesis of point prevalence and health facility catchment data of mixed types: Plasmodium parasite prevalence and malaria febrile incidence. Since the clinical case definition for health facility record keeping is less strict than that used in cohort studies used to construct previous parasite prevalence to clinical incidence relationships (the latter usually incorporating a parasite density threshold to remove background fevers accompanied by coincidence asymptomatic parasite infections) our model learns a smooth prevalence-to-incidence conversion during posterior sampling. Also jointly fitted are a catchment model based on the relative travel times between each pixel location and its nearby health facilities, as well as a distribution regression-based covariance structure for explaining the residual errors at health facility level based on the similarity between the ‘bags’ of covariate values in their respective catchments.