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Wolfson Research Institute for Health and Wellbeing

Wolfson Fellow

Publication details for Dr Mark Booth

Stensgaard, A., Booth, M., Nikulin, G. & McCreesh, N. (2016). Combining process-based and correlative models improves predictions of climate change effects on Schistosoma mansoni transmission in eastern Africa. Geospatial Health 11(1s): 406.

Author(s) from Durham

Abstract

Currently, two broad types of approach for predicting the impact of climate change on vector-borne diseases can be distinguished: i) empirical-statistical (correlative) approaches that use statistical models of relationships between vector and/or pathogen presence and environmental factors; and ii) process-based (mechanistic) approaches that seek to simulate detailed biological or epidemiological processes that explicitly describe system behavior. Both have advantages and disadvantages, but it is generally acknowledged that both approaches have value in assessing the response of species in general to climate change. Here, we combine a previously developed dynamic, agentbased model of the temperature-sensitive stages of the Schistosoma mansoni and intermediate host snail lifecycles, with a statistical model of snail habitat suitability for eastern Africa. Baseline model output compared to empirical prevalence data suggest that the combined model performs better than a temperature-driven model alone, and highlights the importance of including snail habitat suitability when modeling schistosomiasis risk. There was general agreement among models in predicting changes in risk, with 24-36% of the eastern Africa region predicted to experience an increase in risk of up-to 20% as a result of increasing temperatures over the next 50 years. Vice versa the models predicted a general decrease in risk in 30-37% of the study area. The snail habitat suitability models also suggest that anthropogenically altered habitat play a vital role for the current distribution of the intermediate snail host, and hence we stress the importance of accounting for land use changes in models of future changes in schistosomiasis risk.