Modelling Control Strategies Against Classical Swine Fever: Influence of Traders and Markets Using Static and Temporal Networks in Ecuador : [Preprint]

Pig farming in Ecuador represents an important economic and cultural sector. Recently, the National Veterinary Service has implemented individual identification of pigs, stricter movement control and mandatory vaccination against classical swine fever (CSF), which has been prevalent since 1940. The information registered since 2017 seems valuable for applying modelling techniques assessing the risk of dissemination of animal diseases and test control strategies. Social network analysis (SNA) was used to characterise the premises into risk categories, looking for better targeting surveillance activities and identifying higher risk targets. Network modelling was used to test the disease spread and control strategies over the network, using a Susceptible, Infected, Recovered, Susceptible SIRS model to compare a target selection of high-risk nodes against the current traditional surveillance. Finally, temporal network analysis revealed general trends in the network's evolution. The network contained 751,003 movements with 6 million pigs, excluding movements to slaughterhouses. A total of 165,593 premises were involved in network movements (144,118 farms, 138 industrials, 21,337 traders and 51 markets). On an annual average, 75% of the premises (124,976) received or sent up to one movement with an annual average of 1.5 pigs. In contrast, 0.01% of the premises (166) made up to 1,372 movements, with an annual average of 11,607 pigs. Simulations on the network resulted in the dissemination of CSF, reaching a mean prevalence of 29.93% without a control strategy. The random selection strategy reduced the prevalence to 24.4%, while target selection by risk reduced to 3.3%. When comparing the static representation of the network with the temporal counterpart, causal fidelity (c = 0.62) showed an overestimation of 38% in the number of transmission paths, even though it took an average of 4.39 steps to cross the network, these steps took approximately 233 days. The characterisation of the premises acting on the Ecuadorian network, based on risk emphasis, and the application of network modelling techniques can support the redesign of the surveillance system. Minor changes could enable the implementation of risk-based surveillance, targeting specifically the nodes at higher risk and considering special attention to markets and traders.

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