Thesis Dutta Bhagat Lal

Bhagat Lal Dutta

Spatio-temporal modelling of pathogen spread in a bovine metapopulation: application to Bovine Viral Diarrhoea Virus (BVDV)

Abstract :

Bovine Viral Diarrhoea Virus (BVDV) infection poses serious socio-economic concerns to cattle farms. The objective of this thesis was to understand, by a modelling approach, the propagation of BVDV between farms at a regional scale, interacting via animal movements and neighbouring relationships, thus paving the way for the evaluation of control strategies. In the first part, the network of cattle movements in France (2005-2009) was analysed to evaluate its temporal evolution influencing the underlying capacity of spreading pathogens via animal exchanges between farms and to explore the importance of its regional and breed-related specificities. Topological properties of this network were found to be quite stable over time. The relative vulnerability of beef and dairy sub-networks depended on if aggregated or dynamical views of these networks were considered. In the second part, a multilevel stochastic metapopulation model of BVDV spread in dairy herds was developed and efficiently implemented, coupling local herd population and pathogen spreading dynamics and possible inter-herd transmission of the pathogen through animal exchanges and neighbouring relationships. Animal movement data between farms and geo-location based neighbourhoods were used to simulate BVDV transmission in Finistère, a dominantly dairy department of Western France. The simulations spanning over 10 years showed that cattle movements played the main role in the transmission of BVDV at a regional scale. In agreement with field observations, it was also found that, on average, 6 to 11% of farms were infected. This modelling approach will be further used to test the control programs in-silico.

Key words :

BVDV, metapopulation model, spatio-temporal modelling, model coupling, network analysis, dynamic networks, neighbourhood, intensive simulations


Modification date : 11 September 2023 | Publication date : 29 June 2017 | Redactor : ML