Open Access: Mapping malaria by sharing spatial information between incidence and prevalence data sets

Each week, we select a recently published Open Access article to feature. This week’s article comes from the Journal of the Royal Statistical Society Series C (Applied Statistics) and compares approaches to determining the risk of malaria in low-prevalence areas. 

The article’s abstract is given below, with the full article available to read here.

Lucas, T.C.D., Nandi, A.K., Chestnutt, E.G., Twohig, K.A., Keddie, S.H., Collins, E.L., Howes, R.E., Nguyen, M., Rumisha, S.F., Python, A., Arambepola, R., Bertozzi‐Villa, A., Hancock, P., Amratia, P., Battle, K.E., Cameron, E., Gething, P.W. and Weiss, D.J. (2021), Mapping malaria by sharing spatial information between incidence and prevalence data sets. J R Stat Soc Series C.
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.
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