Abstract
Abstract As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low-prevalence areas are increasingly needed. For low-burden areas, disaggregation regression models have been developed to estimate 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 might make more accurate predictions. Using case studies in Indonesia, Senegal and Madagascar, we compare the out-of-sample mean absolute error for two methods for incorporating point-level, spatial information into disaggregation regression models. The first simply fits a binomial-likelihood, logit-link, 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 in most cases there is no difference in mean absolute error between models. In only one case, did the new models perform the best. More generally, our results demonstrate that combining these types of data has the potential to reduce absolute error in estimates of malaria incidence but that simpler baseline models should always be fitted as a benchmark.
Highlights
Global malaria incidence has decreased dramatically over the last 20 years (Battle et al, 2019; Bhatt et al, 2015; Weiss et al, 2019)
When the study area contains both low-and medium/high-b urden areas, or when prevalence surveys and routine surveillance provide complementary spatial coverage, models that use both of these data sources have the potential to improve estimates of malaria prevalence and incidence
We considered the ability of the model to predict polygon incidence to be our main objective and our performance metric for this was mean absolute error (MAE)
Summary
Global malaria incidence has decreased dramatically over the last 20 years (Battle et al, 2019; Bhatt et al, 2015; Weiss et al, 2019). Two important data sources for malaria mapping are cluster-level surveys of prevalence (Bhatt et al, 2015; Bhatt et al, 2017; Gething et al, 2011; Gething et al, 2012) and routine surveillance data, typically aggregated by administrative unit polygons (Cibulskis et al, 2011; Ohrt et al, 2015; Sturrock et al, 2016) These data sources have different strengths and different spatial coverage. When the study area contains both low-and medium/high-b urden areas, or when prevalence surveys and routine surveillance provide complementary spatial coverage, models that use both of these data sources have the potential to improve estimates of malaria prevalence and incidence
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