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A model of the spread of the bovine viral-diarrhoea virus within a dairy herd.

by: Anne-France F. Viet, Christine Fourichon, Henri Seegers, Christine Jacob, Chantal Guihenneuc-Jouyaux
Preventive veterinary medicine, Vol. 63, No. 3-4. (14 may 2004), pp. 211-236, doi:10.1016/j.prevetmed.2004.01.015  Key: citeulike:11489244

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Abstract

Wet BVDSim (a stochastic simulation model) was developed to study the dynamics of the spread of the bovine viral-diarrhoea virus (BVDV) within a dairy herd. This model took into account herd-management factors (common in several countries), which influence BVDV spread. BVDSim was designed as a discrete-entity and discrete-event simulation model. It relied on two processes defined at the individual-animal level, with interactions. The first process was a semi-Markov process and modelled the herd structure and dynamics (demography, herd management). The second process was a Markov process and modelled horizontal and vertical virus transmission. Because the horizontal transmission occurs by contacts (nose-to-nose) and indirectly, transmission varied with the separation of animals into subgroups. Vertical transmission resulted in birth of persistently infected (PI) calves. Other possible consequences of a BVDV infection during the pregnancy period were considered (pregnancy loss, immunity of calves). The outcomes of infection were modelled according to the stage of pregnancy at time of infection. BVDV pregnancy loss was followed either by culling or by a new artificial insemination depending on the modelled farmer’s decision. Consistency of the herd dynamics in the absence of any BVDV infection was verified. To explore the model behaviour, the virus spread was simulated over 10 years after the introduction of a near-calving PI heifer into a susceptible 38 cow herd. Different dynamics of the virus spread were simulated, from early clearance to persistence of the virus 10 years after its introduction. Sensitivity of the model to the uncertainty on transmission coefficient was analysed. Qualitative validation consisted in comparing the bulk-milk ELISA results over time in a sample of herds detected with a new infection with the ones derived from simulations.


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