Search

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.

This article introduces new methods for inference with count data registered on a set of aggregation units. Such data are omnipresent in epidemiology due to confidentiality issues: it is much more common to know the county in which an individual resides, say, than know their exact location in space. Inference for aggregated data has traditionally made use of models for discrete spatial variation, for example conditional autoregressive models (CAR). We argue that such discrete models can be improved from both a scientific and inferential perspective by using spatiotemporally continuous models to directly model the aggregated counts. We introduce methods for delivering (limiting) continuous inference with spatitemporal aggregated count data in which the aggregation units might change over time and are subject to uncertainty. We illustrate our methods using two examples: from epidemiology, spatial prediction malaria incidence in Namibia; and from politics, forecasting voting under the proposed changes to parlimentary boundaries in the United Kingdom., Comment: 31 pages, 11 Figures. Note: figure quality substantially reduced in order to meet arXiv requirements