Date Published: May 31, 2019
Publisher: Public Library of Science
Author(s): Martin Sterchi, Céline Faverjon, Cristina Sarasua, Maria Elena Vargas, John Berezowski, Abraham Bernstein, Rolf Grütter, Heiko Nathues, Esteban Hernandez-Vargas.
The topology of animal transport networks contributes substantially to how fast and to what extent a disease can transmit between animal holdings. Therefore, public authorities in many countries mandate livestock holdings to report all movements of animals. However, the reported data often does not contain information about the exact sequence of transports, making it impossible to assess the effect of truck sharing and truck contamination on disease transmission. The aim of this study was to analyze the topology of the Swiss pig transport network by means of social network analysis and to assess the implications for disease transmission between animal holdings. In particular, we studied how additional information about transport sequences changes the topology of the contact network. The study is based on the official animal movement database in Switzerland and a sample of transport data from one transport company. The results show that the Swiss pig transport network is highly fragmented, which mitigates the risk of a large-scale disease outbreak. By considering the time sequence of transports, we found that even in the worst case, only 0.34% of all farm-pairs were connected within one month. However, both network connectivity and individual connectedness of farms increased if truck sharing and especially truck contamination were considered. Therefore, the extent to which a disease may be transmitted between animal holdings may be underestimated if we only consider data from the official animal movement database. Our results highlight the need for a comprehensive analysis of contacts between farms that includes indirect contacts due to truck sharing and contamination. As the nature of animal transport networks is inherently temporal, we strongly suggest the use of temporal network measures in order to evaluate individual and overall risk of disease transmission through animal transportation.
Animal transports are widely seen as one of the main factors contributing to the transmission of infectious diseases in animal populations . This has led public authorities in many countries and supranational bodies such as the EU to collect animal movement data. In the last decade, there has been a growing interest in analyzing such data using social network analysis in order to better understand interrelationships between animal holdings (i.e., farms, slaughterhouses, etc.) and assess their impact on disease transmission . The first attempts at modelling animal transport networks with social network analysis were static network models [3,4]. The importance of dynamic (or temporal) network analysis has only recently been established [5–7]. Animal transport networks are inherently temporal, as a link between two holdings only exists at the time of the transport. For the analysis of the transmission of a disease, this consideration is crucial, as links in temporal networks are not necessarily transitive. In other words, a pathogen may only be passed from farm A to farm C via farm B if the corresponding transports between those three farms are sequential in time.
The main objective of our study was to assess the effect of the topology of the Swiss pig transport network on the potential for disease transmission between animal holdings. The analysis was based on (i) the official animal movement database in Switzerland, and (ii) a sample of transport data from one animal transport company in Switzerland. The sample covered approximately 13% of the transports reported in AMD and the data comprised transports in all major regions of Switzerland. The present study was designed primarily to compare the topology of networks based on (i) to networks based on (ii). We examined the effect of the structure of the Swiss pig industry, which differs from other pig industries in Europe because of the high number of small holdings, the frequent trade of small batches of pigs and the use of vehicles that are not always properly cleaned and disinfected. For both data sources, we restricted our analysis to monthly networks. As explained earlier, movements to slaughterhouses were not considered in the network analysis.