Modelling schistosomiasis in South Africa.

Abstract

Temperature and rainfall vary spatially within South Africa and they in turn affect the parasites and intermediate host snails involved in schistosomiasis transmission. The primary goal of this study was to investigate the relationship between these two abiotic variables and schistosomiasis in South Africa using a Geographic Information System (GlS) as a spatial analytical tool. The secondary goal was to estimate the population exposure to schistosomiasis. Prevalence data for Schistosoma haematobium and S. mansoni obtained from a national hardcopy atlas and two long-term, retrospective, high resolution climate datasets were used to produce two models (temperature-suitability and regression analysis) based on different GIS methodologies. The temperature-suitability model defined areas that are suitable and unsuitable for disease transmission by relating documented temperature regimes to the schistosomes' larval biology. The map outputs show that temperature minima corresponded better with the disease data than temperature maxima. Based on different climate and population data permutations, between approximately 3 903 734 and 4 379 079 school-aged children live in these temperature-suitable zones. The regression model tested the hypothesis that temperatures, especially during spring and summer favoured schistosomiasis transmission more than those of autumn and winter. Positive associations were expected with the rainfall variables. A logistic equation was used to predict, as accurately as possible within the model's limitations, the probability of schistosomiasis occurring in a given area. Increasing annual rainfall, as well as spring and autumn temperature maxima and minima predicted an increase in S. haematobium prevalence rates. Schistosoma haematobium prevalence rates of 11-25% and 26-50% were predicted in the north-eastern and eastern coastal regions. A prevalence rate of 71 to 100% was predicted from Limpopo to KwaZulu-Natal. Increasing the average monthly rainfall, spring temperature maxima and autumn temperature minima, increased the likelihood of S. mansoni transmission. Schistosoma mansoni prevalence rates of 26-50% and 71 to 100% were predicted in Limpopo, Mpumalanga, KwaZulu- Natal and Eastern Cape. This is the first time GIS has been used to correlate climate variables and schistosomiasis occurrence in South Africa. The regression model requires further refinement and it is not as applicable as the temperature-suitability model for practical purposes.