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About the Authors:
Tom Lindström
Affiliations Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden, School of Biological Sciences, University of Sydney, Sydney, New South Wales, Australia
Daniel A. Grear
Affiliation: Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
Michael Buhnerkempe
Affiliation: Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
Colleen T. Webb
Affiliation: Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
Ryan S. Miller
Affiliation: United States Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America
Katie Portacci
Affiliation: United States Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America
Uno Wennergren
* E-mail: [email protected]
Affiliation: Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
Introduction
Network analysis is an important technique for extracting epidemiologically relevant information from complex systems. For livestock diseases, animal movement networks have received particular attention because they may serve as a proxy for contact networks for disease spread [1]–[5]). While different diseases have different pathways of transmission, the movement of infected animals between livestock premises is a major risk factor for the introduction of diseases to uninfected herds. Long distance movements are particularly important because they can transmit pathogens great distances from the index herd speeding spread and increasing epidemic size [6]. The use of detailed animal movement data in response to the 2001 Foot and Mouth disease outbreak in the United Kingdom (UK) has spurred considerable advances in the use of contact networks to characterize and predict livestock disease outbreaks in the UK [7], [8], [4]. However, while network models are powerful tools for informing disease spread prediction, data collection may be cumbersome and a complete representation of the network is often impossible to obtain. In situations where the complete network is of interest (e.g. disease spread modeling), some method of scaling up a partially observed network is required. While we focus here on livestock networks, similar problems exist in characterizing wildlife and human contact networks [9]–[10].
In this study we focus on the network of cattle movements in the United States. While considered an...