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Mapping disease risk by sharing spatial information between areal-incidence and point-prevalence data
Tim Lucas (University of Oxford)
Disaggregation regression models have been developed to estimate disease risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons. However, in areas with both routine surveillance data and prevalence surveys, models that make use of the spatial information from prevalence point-surveys have great potential.
However, the development of these models is difficult because the areal and point data are on different scales (incidence is a rate while prevalence is a proportion). Using case studies of malaria incidence in Indonesia, Senegal and Madagascar, we compare two methods for incorporating point-level, spatial information into geospatial disaggregation regression models. The first simply fits a spatial Gaussian random field to prevalence point-surveys to create a new covariate. The second is a multi-likelihood model that is fitted jointly to prevalence point-surveys and polygon incidence data.
We find that the simple model generally performs better than a baseline disaggregation model while the performance of the joint model was mixed. More generally, our results demonstrate that combining these types of data improves estimates of disease incidence.