An integrated epidemio-economic modelling framework of the complex interplay between pathogen spread and disease management: control of BVD within beef herds as a case study

Résumé fourni par la source

The dynamics of animal diseases is usually modelled thanks to an epidemiological modelling framework where on-farm health practices are omitted. Optimal control decisions are mainly drawn by modelling the effect of mandatory practices, which is of public interest for the control and eradication of major epidemic diseases. However, most endemic infectious animal diseases do not rely on public health control, although generating significant adverse economic consequences in livestock. Pathogen spread and the magnitude of associated economic consequences are directly linked to efforts undertaken by farmers themselves.Our objective was to propose a mathematical modelling framework integrating individual health decisions into dynamic stochastic epidemiological models, in order to highlight the feedback loops occurring between the modification over time of the epidemiological situation and farmers’ decision process. These feedback loops exist for a single herd, but also more widely for a group of herds interacting at a regional level. This work is applied to the control of Bovine Viral Diarrhea at the scale of a beef cattle herd, using an existing epidemiological model [1] and considering vaccination as control measure. We represented decision at each time step (here of one year) as a balance between expected farm income when performing vs. not performing vaccination. Since the epidemiological model is stochastic, a methodology was needed to compare model outcomes in both cases. We simulated a sufficient number of repetitions for each scenario to compare predictions using Wilcoxon-Mann-Whitney statistical test, which allows comparing mean values without normality assumptions on the tested variable. To account for the limited information available to farmer about the subsequent actual realization, one of the simulated repetitions was assumed to truly occur. Therefore, predicted farm health decisions are not necessarily optimal. The vaccination choice performed on time t influences epidemiological dynamics between t and t+1, and thus the next decision to be made on time t+1.The proposed approach makes it possible to characterize the dynamics in terms of vaccination decisions (existence of vaccination cycle), as well as disease dynamics at the farm level. Simulation results reveal that BVD epidemiological dynamics at farm level highly depends on farmers’ health choices over time, as well as on the randomness of biological processes.

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