Pig movements in France: Designing network models fitting the transmission route of pathogens

Abstract

Pathogen spread between farms results from interaction between the epidemiological characteristics of infectious agents, such as transmission route, and the contact structure between holdings. The objective of our study was to design network models of pig movements matching with epidemiological features of pathogens. Our first model represents the transmission of infectious diseases between farms only through the introduction of animals to holdings (Animal Introduction Model AIM), whereas the second one also accounts for pathogen spread through intermediate transit of trucks through farms even without any animal unloading (i.e. indirect transmission–Transit Model TM). To take the pyramidal organisation of pig production into consideration, these networks were studied at three different scales: the whole network and two subnetworks containing only breeding or production farms. The two models were applied to pig movement data recorded in France from June 2012 to December 2014. For each type of model, we calculated network descriptive statistics, looked for weakly/strongly connected components (WCCs/SCCs) and communities, and analysed temporal patterns. Whatever the model, the network exhibited scale-free and small-world topologies. Differences in centrality values between the two models showed that nucleus, multiplication and post-weaning farms played a key role in the spread of diseases transmitted exclusively by the introduction of infected animals, whereas farrowing and farrow-to-finish herds appeared more vulnerable to the introduction of infectious diseases through indirect contacts. The second network was less fragmented than the first one, a giant SCC being detected. The topology of network communities also varied with modelling assumptions: in the first approach, a huge geographically dispersed community was found, whereas the second model highlighted several small geographically clustered communities. These results underline the relevance of developing network models corresponding to pathogen features (e.g. their transmission route), and the need to target specific types of holdings/areas for surveillance depending on the epidemiological context.

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs

“…Eight production sites surveyed in 2009 ceased activity before 2013, limiting movement data availability to only 178 out of the 186 farms. This is consistent with the observed overall decrease in the number of pig farms in France (Salines et al, 2017b). On the département scale, the model involving the random sampling of farm-level HEV prevalence from beta distributions − with a weighted confidence interval − made it possible to take into account the low precision of some prevalence figures in quite a few départements where a low number of farms had been sampled.…”

“…Moreover, a thorough cleaning stage was carried out to manage incorrect or incomplete data. The quality of data in terms of accuracy, reliability, and comprehensiveness guaranteed the robustness of our results (Salines et al, 2017b). The random selection process for tested farms and for individual pigs tested from each farm (Rose et al, 2011) ensured reliable estimates for the seroprevalence values used in our study.…”

“…Indeed, one should note that movement and prevalence data were not simultaneously collected. However, the French pig movement network has been found to be stable over time (Salines et al, 2017b), so we can assume that combining the 2009 prevalence data with the 2013 pig movement data is still consistent. Moreover, 70% of the 178 farms included in our study showed a loyalty equal to 1 (i.e.…”

“…Pig movement database. As described by Salines et al (2017b), pig movement data were obtained from the National Swine Identification Database (BDporc), managed by swine industry professionals and recognised by the French Ministry for Agriculture. All pig movements between farms and to slaughterhouses, rendering plants and trade operators are systematically recorded in this database.…”

“…As such, animal movement data have been increasingly studied using social network analysis (SNA) methods, with farms being considered as nodes, and animal movements between farms as links (Wasserman and Faust, 1994;Bigras-Poulin et al, 2006;Bigras-Poulin et al, 2007;Martínez-López et al, 2009;Natale et al, 2009;Ribbens et al, 2009;Nöremark et al, 2011;Lindstrom et al, 2012;Rautureau et al, 2012;Buttner et al, 2013;Dorjee et al, 2013;Guinat et al, 2016;Thakur et al, 2016). Although in most studies network analyses have been motivated by the consequences of animal trade on the epidemiology of animal diseases (Keeling, 2005;Lloyd-Smith et al, 2005;Bigras-Poulin et al, 2007;Martínez-López et al, 2009;Rautureau et al, 2012;Buttner et al, 2013), the specific role of animal shipments in pathogen transmission and/or exposure has only scarcely been documented and rarely quantified, especially in the swine sector (Ortiz-Pelaez et al, 2006;Green et al, 2008;Martin et al, 2011;Porphyre et al, 2011;Frössling et al, 2012;Nicolas et al, 2013;Beaunee et al, 2015;Lee et al, 2017;Salines et al, 2017b;Sintayehu et al, 2017). Analysing contact patterns related to pig trade could provide new insight into infection dynamics, pathogen spread and risk factors, helping to design risk-based surveillance programmes.…”