Live Animal movements - Understand trade Partners Choices to predict chains of contact

Hepatitis E virus (HEV) infects pigs and humans. Its spread among pigs occurs within-farm through faecal–oral route including direct and environmental transmission. Live animal movements are known as the major driver of between farms spread. Strongly structured in space and time, the swine production chain is made of various interconnected farms, forming a complex network. We aimed to simulate realistic between-farm movements to feed a multilevel epidemiological model. Swine movements from 2017 to 2019 provided by the National Swine Identification database (BDporc) were analysed using complex network analysis  methods. Exponential random graph models (ERGM) were then used to identify the key drivers of trade partner choices and the associated probabilities of contact between farms. In the 36-month period, 2.512.174 loading and unloading events were recorded involving 16.377 premises. Because of the large size of the dataset, analysis was limited to specific subsets of the data, using semestrial data and type of transported animals (breeding sows, piglets and growing pigs). For each data subset, an ERGM was selected using a stepwise forward approach based on AICs comparison and analyzing goodness-of-fit. In all cases, the company, type of farm, free-ranging characteristics, size and batch rearing systems were identified as explanatory variables of the network structure. Network statistics and levels included were subnetwork-specific. ERGM outcomes allow simulating pig trade networks with characteristics similar to the observed one, to prospect on the global impact of the network structure on pathogen transmission. Simulated movements fed a multi-level model developed with SimInf package. This work was done in the frame of the BIOPIGEE OHEJP project.