Prediction of Pig Trade Movements in Different European Production Systems Using Exponential Random Graph Models - Relun, Anne; Grosbois, Vladimir; Alexandrov, Tsviatko; Sánchez-Vizcaíno, Jose M.; Waret-Szkuta, Agnes; Molia, Sophie; Etter, Eric Marcel Charles; Martínez-López, Beatriz (Frontiers in Veterinary Science, 2017)
In most European countries, movements of live animals are routinely collected and can greatly aid predictive epidemic modelling. However, the use of complete movements’ dataset to conduct policy-relevant predictions has been so far limited by the massive amount of data that have to be processed or the availability of timely and updated records on animal movements, particularly in areas where small-scale or extensive production is predominant. The aim of this study was to use of exponential random graph models (ERGMs) to reproduce, understand and predict pig trade networks in different European production systems. Three trade networks were built by aggregating movements of pig batches among premises (farms and trade operators) over 2011 in Bulgaria, Extremadura (Spain) and Côtes-d’Armor (France), where small-scale, extensive and intensive pig production are predominant, respectively. Three ERGMs were fitted to each network with various demographic and geographic attributes of the nodes as well as six internal network configurations. Several statistical and graphical diagnostic methods were applied to assess the goodness of fit of the models. For all systems, both exogenous (attribute-based) and endogenous (network-based) processes appeared to govern the structure of pig trade network, and neither alone were capable of capturing all aspects of the network structure. Geographic mixing patterns strongly structured pig trade organization in the small-scale production system, whereas belonging to the same company, or keeping pigs in the same housing system appeared to be key drivers of pig trade, in intensive and extensive production systems, respectively. Heterogeneous mixing between types of production also explained a part of network structure, whichever production system considered. Limited information is thus needed to capture most of the global structure of pig trade networks. Such findings will be useful to simplify trade networks analysis and better inform European policy makers on more cost-effective prevention and control against swine diseases such as African swine fever, classical swine fever or Porcine Reproductive and Respiratory Syndrome.