Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling

Abstract

Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine surveys on different age-groups and to acquire separate estimates for individuals aged ≤20 years and entire communities. Prevalence estimates were combined with population statistics to obtain country-specific numbers of Schistosoma infections. We estimate that 122 million individuals in eastern Africa are currently infected with either S. mansoni, or S. haematobium, or both species concurrently. Country-specific population-adjusted prevalence estimates range between 12.9% (Uganda) and 34.5% (Mozambique) for S. mansoni and between 11.9% (Djibouti) and 40.9% (Mozambique) for S. haematobium. Our models revealed that infection risk in Burundi, Eritrea, Ethiopia, Kenya, Rwanda, Somalia and Sudan might be considerably higher than previously reported, while in Mozambique and Tanzania, the risk might be lower than current estimates suggest. Our empirical, large-scale, high-resolution infection risk estimates for S. mansoni and S. haematobium in eastern Africa can guide future control interventions and provide a benchmark for subsequent monitoring and evaluation activities.

Keywords: BCI; Bayesian credible interval; Bayesian geostatistics; CI; Confidence interval; EROS; Earth Resources Observation; East Africa; GNTD database; Global Neglected Tropical Disease database; HII; ISRIC; International Soil Reference and Information Center; LST; MAE; MCMC; ME; MODIS; Markov chain Monte Carlo; Markov chain Monte Carlo simulation; Moderate Resolution Imaging Spectroradiometer; NDVI; OR; Risk mapping and prediction; SD; SEDAC; Schistosoma haematobium; Schistosoma mansoni; Schistosomiasis; Socioeconomic Data and Applications Center; human influence index; land surface temperature; mean absolute error; mean error; normalized difference vegetation index; odds ratio; standard deviation.