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\begin{document}

\title{A Bayesian geospatial modeling framework for the synthesis of point
prevalence and health facility catchment data}



\author[1]{Andre Python}%
\author[1]{Ewan Cameron}%
\author[1]{Katherine Battle}%
\author[2]{Peter Gething}%
\author[1]{Justin Millar}%
\author[1]{Tasmin Symons}%
\affil[1]{University of Oxford}%
\affil[2]{Curtin University}%


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  \date{May 30, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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