Adding Bayesian disease mapping and co-factor analysis to the PAZ project in the Lake Victoria Crest, Kenya
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Dopfer, D., Amene, E., Doble, L., Glanville, W. de and Fèvre, E.M. 2012. Adding Bayesian disease mapping and co-factor analysis to the PAZ project in the Lake Victoria Crest, Kenya. Paper presented at the 13th conference of the International Society for Veterinary Epidemiology and Economics, Maastricht, the Netherlands, 20-24 August 2012. Durban, South Africa: International Symposia for Veterinary Epidemiology and Economics.
Permanent link to this item: http://hdl.handle.net/10568/27766
Internet URL: http://www.sciquest.org.nz/node/81010
The PAZ Project (People, Animals and their Zoonosis lead by E. Fevre, http://www.zoonotic-diseases.org/ home/Research/paz, funded by the Wellcome Trust) and its consecutive projects utilize innovative crossdisciplinary data analysis derived from Numerical Ecology to map, prioritize and deliver interventions against infectious zoonotic diseases in people and their domestic animals. The interventions will be tailored to the risk-driven needs of spatio-temporal clusters. The PAZ Project is undertaking a community based cross sectional survey of humans and their livestock in a mixed crop-livestock farming system in the Lake Victoria Crescent in East Africa. Comprehensive economic, social and disease – including HIV, Malaria, Bovine Tuberculosis, blood and GI parasites – data are collected at the household and individual animal and human level together with prevalence data of six neglected zoonoses. Diseases do not exist in isolation. Ignoring interactions between multiple diseases, co-factors and reservoir species, results in the misinterpretation of infection pressures and miscalculation of effects of interventions. Therefore, designing intervention packages that are specifically targeted to clusters of disease and their co-factors, ensures that the interventions are relevant, targeted and cost-effective. The data from the PAZ Project will be analyzed using (1) Principal Component Analysis (PCA); (2) Cluster Analysis (CA); and finally (3) Bayesian Disease Mapping (BDM) methods. The proposed oral presentation will describe the project and strategic plans for and the preliminary outcomes of the data analysis.