  Improvements to Zambia's malaria surveillance system allow better monitoring
of incidence and targetting of responses at refined spatial scales. As
transmission decreases, understanding heterogeneity in risk at fine spatial
scales becomes increasingly important. However, there are challenges in using
health system data for high-resolution risk mapping: health facilities have
undefined and overlapping catchment areas, and report on an inconsistent basis.
We propose a novel inferential framework for risk mapping of malaria incidence
data based on formal down-scaling of confirmed case data reported through the
health system in Zambia. We combine data from large community intervention
trials in 2011-2016 and model health facility catchments based upon
treatment-seeking behaviours; our model for monthly incidence is an aggregated
log-Gaussian Cox process, which allows us to predict incidence at fine scale.
We predicted monthly malaria incidence at 5km$^2$ resolution nationally:
whereas 4.8 million malaria cases were reported through the health system in
2016, we estimated that the number of cases occurring at the community level
was closer to 10 million. As Zambia continues to scale up community-based
reporting of malaria incidence, these outputs provide realistic estimates of
community-level malaria burden as well as high resolution risk maps for
targeting interventions at the sub-catchment level.
