Predictive modelling of climate change impacts on disease dynamics in Tanzania

• Main Author: Reynolds, Rachael Amy
• Published: Manchester Metropolitan University 2018
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756922

Climate and the environment are key determinants impacting various aspects of disease transmission, including lifecycle, survivability and prevalence. Recent changes in both the long-term climatology, and short term El Niño events are impacting the spatial distribution of disease, increasing the number of people being at higher risk of contracting fatal diseases. These changes are particularly detrimental in developing countries, where socioeconomic conditions hinder access to disease prevention and treatment. This thesis explores climate, environment and disease interactions using multiple epidemiological modelling methodologies to develop an informative framework within which disease risk can be assessed, to aid decision-making. Statistical analysis of the impact of extreme events indicate that El Niño has a significant impact on the Tanzanian climate, which differs by location. Spatial modelling results demonstrate that by 2050 under RCP 8.5 mean malaria risk will initially reduce by 4.7%, which then reverses to an increase of 8.9% in 2070. Overall, analysis indicates increases in mean malaria risk. Biological modelling indicates that the predicted increases in malaria risk are likely a result of the reduction in time taken to complete the sporogonic and gonotrophic cycles due to increasingly optimum environmental conditions. The novel approach applied here contributes the development of a new model in environmental epidemiology. This thesis concludes that epidemiological modelling results could be beneficial in aiding decision makers to prepare for the impact of climate and environmental change, with a recommendation to continue research in this area with a particular focus on understudied and developing countries.