title: Infectious disease transmission and contact networks in wildlife and livestock.
abstract: The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.
authors: Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN 55108, USA craft@umn.edu.
authors: Meggan E
authors: http://orcid.org/0000-0001-5333-8513
authors: ME
authors: Craft
mesh_terms: D000818:Animals; D000835:Animals, Wild; D001522:Behavior, Animal; D003141:Communicable Diseases / Q000635:transmission / Q000662:veterinary*; D016358:Contact Tracing / Q000662:veterinary; D058507:Host Specificity; D054884:Host-Pathogen Interactions / Q000235:genetics; D058751:Livestock; D008954:Models, Biological; D012919:Social Behavior
keywords: contact analysis; contact network epidemiology; dynamic network model; infectious disease; network modelling; transmission dynamics