Malaria Risk Mapping Using Routine Health System Incidence Data in Zambia

TL;DR

This paper introduces a novel statistical framework for high-resolution malaria risk mapping in Zambia, integrating health system data and treatment-seeking behavior to estimate community-level incidence more accurately.

Contribution

It develops a formal down-scaling method using an aggregated log-Gaussian Cox process to improve spatial resolution of malaria risk maps from routine health data.

Findings

Estimated community malaria cases to be nearly double reported cases.

Produced high-resolution risk maps at 5km$^2$ scale across Zambia.

Provided a scalable approach for real-time malaria burden estimation.

Abstract

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.