In this talk, I will first make some general comments about the role of statistical modelling in scientific research, distinguishing between empirical and mechanistic models. I will then describe in detail how statistical modelling based on Gaussian stochasatic processes has been used in a multi-national control programme for onchocerciasis river blindness) in equatorial Africa.
Finally, I will suggest that statistical thinking should be considered as an essential part of scientific method, and that this should guide our teaching of statistics to (natural and social) science students.
Diggle, P.J. and Chetwynd, A.G. (2011). Statistics and Scientific Method: an Introduction for Students and Researchers. Oxford University Press.
Diggle, P.J., Thomson, M.C., Christensen, O.F., Rowlingson, B., Obsomer, V., Gardon, J., Wanji, S., Takougang, I., Enyong, P., Kamgno, J., Remme, H., Boussinesq, M. and Molyneux, D.H. (2007). Spatial modelling and prediction of Loa loa risk: decision making under uncertainty. Annals of Tropical Medicine and Parasitology, 101, 499-509.
Schlueter, D., Ndeffo-Mbah, M.L., Takougang, I, Ukety, T., Wanji, S., Galvani, A.P. and Diggle, P.J. (2015). Using community-level prevalence of Loa loa infection to predict the proportion of highly-infected individuals: statistical modelling to support lymphatic filariasis elimination programs. PLoS NTD
Zoure, H.G.M., Noma, M., Tekle, A.H., Amazigo, U.V., Diggle, P.J., Giorgi, E. and Remme, J.H.F. (2014). The geographic distribution of onchocerciasis in the 20 participating countries of the African Programme for Onchocerciasis Control: 2. Pre-control endemicity Levels and estimated number infected. Parasites and Vectors, 7, 326 (doi:10.1186/1756-3305-7-326)
All are welcome. Tea/coffee from 3.45.
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