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Title: Developing spatio-temporal models of schistosomiasis transmission with climate change
Author: Mangal, Tara Danielle
ISNI:       0000 0004 2697 5252
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2009
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Schistosomiasis is one of the most prevalent diseases in the world and a major cause of morbidity in Africa. Accurate determination of the geographical distribution of schistosomiasis in Africa along with the number of people affected is difficult, since reliable prevalence data are often not available for most of the African continent. Effective schistosomiasis control programmes rely on accurate statistics regarding the geographical distribution of disease, the population at risk, and the intensity of disease transmission. These estimates can be obtained using a number of statistical methods which relate prevalence and intensity of disease to risk factors, measured at the individual level and at the population level. Schistosoma mansoni is largely a climatedriven parasite, which relies on the availability of a suitable snail host. The survival of parasitic infection depends on climatic variables, such as temperature, rainfall and vegetation. Statistical models which incorporate spatial or individual heterogeneity are highly complex and require large numbers of parameters. Until recently, the most common approach was to use regression modelling to identify risk factors for disease transmission. However, this method has a number of limitations. In particular, it gives no information on the dynamics of transmission, e. g. will the disease reach an endemic state under a certain set of conditions or be subject to a periodic cycle? The aim of this thesis was to a) develop mechanistic transmission models to study how schistosomiasis disease dynamics change with water temperature change and to parameterise these models to provide better estimates for a specific host-parasite combination; b) explore how the efficacy of control programmes changes with changing water temperature; c) produce continent-wide maps of schistosomiasis prevalence in Africa, using a combination of geospatial models and environmental data; d) to quantify the impact of climate change over the next 50 years on the prevalence and intensity of disease. A mechanistic model describing the transmission dynamics of schistosomiasis at a range of water temperatures was developed and showed that as the long-term mean temperature increases up to 29°C, the mean worm burden increases. At 34°C, the mean worm burden starts to taper, as the thermal limits of both the snail and the parasite are reached. Adding complexity to the models, such as snail density-dependence and adult parasite density-dependence, had no significant impact on the overall transmission patterns. However, a sensitivity analysis revealed subtle changes in the relative importance of certain parameters. The most detailed model showed that the parameters describing the transmission of schistosomes from snail to man were the most sensitive to change and therefore, provided a useful target point for control strategies. The effects of various control programmes were modelled using discrete time series models and manipulation of the individual parameters. The most effective control programme was repeated mass chemotherapy, although reducing contact with contaminated water also proved highly effective. Producing maps of geo-referenced point prevalence data highlighted the areas in which no data currently exist. This provides an invaluable tool for determining which regions need further study. Four separate geospatial models were developed to predict the distribution of schistosomiasis over Africa, and each was validated using existing data. The ordinary kriging model provided the best estimates for prevalence data and the indicator kriging model provided the best estimates for the probability of infection within a population. These models are useful for determining high-risk populations and locating areas in which control efforts should be focussed. Two types of regression models were used to investigate associations between climatic variables and prevalence of disease. Monthly rainfall and mean annual temperature were shown to have important roles in defining the limits of schistosomiasis transmission. Using these data, it is possible to define a threshold, outside which schistosomiasis transmission is unlikely to occur. These models were used to predict how the distribution of schistosomiasis would change with climate change. It was shown that over the next 50 years, there will be an increase in the number of areas able to support the intermediate vector. Without socio-economic development or intervention strategies, this will almost certainly be followed by an increase in disease transmission. The use of mathematical and geospatial models can greatly enhance our understanding of schistosome epidemiology and are an essential tool in the planning stages of any intervention strategy.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available