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IDD

Seminar

Veterinary epidemiology: where mathematical modellers , biologists, animal scientists, and veterinarians (should) meet

Klinkenberg, D; de Jong, M (Universiteit Utrecht/Wageningen University)
Monday 19 August 2013, 17:00-18:00

Seminar Room 1, Newton Institute

Abstract

Infectious disease problems in farmed animals are studied by mathematical modellers and veterinary epidemiologists, often without optimal use of each otherís proficiency. Quality of mathematical models and acceptance by non-modelling epidemiologists and veterinarians should profit from using all knowledge that is present in biological and veterinary communities. More than in other areas of infectious disease epidemiology, veterinary epidemiology allows for detailed observational studies with repeated measurements and for experimental approaches. We will discuss developments in veterinary epidemiological modelling, focussing on heterogeneity and data analysis, identified in the 1993 Newton Institute meeting on Epidemic models (Mollison, 1995). Heterogeneity appeared important in modelling: (i) Bovine Spongiform Encephalopathy (BSE), with large variation in incubation times and initially very uncertain predictions of incidence; (ii) Foot-and-Mouth Disease (FMD), with variation in susceptibility, infectivity, and clinical outcome in different animal species; (iii) Bluetongue, with spatial heterogeneity in vector abundance, to be extracted from vector trapping data and remote sensing; (iv) Avian Influenza, with interactions between ecology of migratory birds, contact patterns of poultry, evolution of strains, and the risk of a human pandemic. Experimental data have been used to quantify BSE incubation times and transmission heterogeneity between animal species in FMD, to address the scaling of contact rates between different settings (as in De Jong et al, 1995), and to study environmental transmission. For the future, we foresee the use of more genomic data to address heterogeneities, both in pathogens and hosts. We advocate use of mechanistically based decision rules to complement predictions by detailed simulations or complex mathematical models. These rules will facilitate the dialog with non-modellers if they have a biological interpretation and can be substantiated by data.

Presentation

[pptx]

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