Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania

Abstract

An increasing number of studies have attempted to predict disease risk geographically on the basis of satellite data, although few have been applied to large-scale control programmes. The aim of our study was to predict the spatial distributions of Schistosoma haematobium and Schistosoma mansoni infections to assist planning the implementation of mass distribution of praziquantel as part of an on-going national control programme in Tanzania. Bayesian geostatistical models were developed using parasitological data from 143 schools. In the S. haematobium models, whilst land surface temperature and rainfall were significant predictors of prevalence, they became non-significant when spatial correlation was taken into account. For S. mansoni, distance to water bodies and annual minimum temperature were significant predictors, even in models adjusting for spatial correlation.