Disease transmission in animal transfer networks

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

“…Introduction into a farm due to the sole transit of a possibly contaminated truck loading pigs in the farm for an outgoing shipment is therefore extremely unlikely. Unlike Sintayehu et al (2017) regarding bovine tuberculosis, our statistical model did not show any significant effect of a herd's betweenness on within-herd HEV seroprevalence. Production units with high betweenness centrality play a key role in the spread of disease throughout the network since they can build so-called bridges between distinct network components.…”

“…Farm centrality in the network therefore appears to be a factor in vulnerability to HEV. This is consistent with the findings of previously published papers (Lee et al, 2017;Sintayehu et al, 2017). As Lee et al (2017) demonstrated for PRRSV, we found that the odds of having higher within-herd HEV seroprevalence was increased more by ingoing closeness than by in-degree, meaning that the level of connectivity with all other holdings in the network is a better predictor of HEV infection than the number of directly connected farms.…”

“…Our study found that the farms' in-degree was positively associated with high within-farm HEV seroprevalence. This is consistent with several studies conducted in livestock production sectors showing that farms having a high in-degree were more likely to be infected with a pathogen (Martin et al, 2011;Frössling et al, 2012;Lee et al, 2017;Sintayehu et al, 2017). Since repeated animal shipments to a farm from the same supplier were aggregated into a single link, the association between HEV seroprevalence and in-degree not only indicates that the HEV seroprevalence of farms increases with the number of incoming shipments, but it also proves that buying animals from several suppliers is linked to higher HEV seroprevalence.…”

“…In the recent literature, several farm connectivity indicators were identified as risk factors for disease occurrence and spread (Martin et al, 2011;Frössling et al, 2012;Lee et al, 2017;Sintayehu et al, 2017). Our study found that the farms' in-degree was positively associated with high within-farm HEV seroprevalence.…”

“…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.…”