The pig transport network in Switzerland: Structure, patterns, and implications for the transmission of infectious diseases between animal holdings

• DOI: 10.1371/journal.pone.0217974
• Publication Date: 2019-05-31T17:53:03Z
• Authors: Martin Sterchi; Céline Faverjon; Cristina Sarasua; Maria Elena Vargas; John Berezowski; Abraham Bernstein; Rolf Grütter; Heiko Nathues
• Keywords: Farms; Livestock; 10009 Department of Informatics; Swine; Science; 610 Medicine & health; Transportation; 1100 General Agricultural and Biological Sciences; 000 Computer science, knowledge & systems; Communicable Diseases; Disease Outbreaks; 0403 veterinary science; 1300 General Biochemistry, Genetics and Molecular Biology; Risk Factors; Animals; Humans; Animal Husbandry; Swine Diseases; 2. Zero hunger; 1000 Multidisciplinary; 630 Agriculture; Q; R; 04 agricultural and veterinary sciences; 3. Good health; Medicine; 020 Library & information sciences; Switzerland; Research Article

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.