Spatio-Temporal analysis of the trade network of the cattle industry in California
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1
Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, United States
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2
Animal Health Branch, California Department of Food and Agriculture, United States
Introduction: In California, the dairy industry is ranked as the number 1 commodity with a value of 6.5 billion, and cattle & calves is ranked as the number 4 commodity with a value of 2.6 billion. By January 1, 2017 the total cattle inventory in California was 5.15 million head (California Agricultural Statistics Review, 2017-18). A better understanding of movements between premises in California can help to design surveillance and emergency response programs to better prevent potential introduction and spread of diseases. It can also help to inform the development of disease spread models to test disease scenarios and evaluate most cost-effective interventions in case outbreaks occur.
Network analysis has been widely used to describe the contacts of animals in a trade network (Martínez-López, Perez, & Sánchez-Vizcaíno, 2009). For several diseases it has been recognized that the movements of animals between premises plays a very important role for the transmission (Dubé, Ribble, Kelton, & McNab, 2009). However, there are very few studies characterizing the movements of cattle in California which limits the possibility to design more effective preparedness and emergency plans. The Animal Health Branch of the California Department of Food and Agriculture (CDFA) has a permitting system which records movements of livestock entering the state, and brand inspection data for cattle, which records movements occurring as result of change of ownership, and movements to feedlot or to slaughter plants. The objective of this study was to describe the spatio-temporal patterns and structure of cattle movements within California for the time period between 2015 and 2017. Results will serve to inform modeling efforts and the design of more cost-effective surveillance and prevention programs to protect the cattle industry in California.
Methods: We first geocoded all farms and reconstruct the most likely routes for each of the recorded movements during the period of study using the California Road System with geographical information systems. Then we used this information to generate a dynamic spatial-explicit network in which nodes are the geocoded farms and the edges represent the mostly likely route (shortest road trip) connecting both farms at a movement and time period. We computed the traveled distances and well as the reachability of the farms in the whole study period as well as what we called “epidemic reachability” which is based on the reachability by a chosen time period, i.e. a user-defined high-risk period, which is a short time period at the beginning of an epidemic when high risk movements may still occur before movement restrictions take place. We also computed time-dependent centrality measures to describe the network structure over time and space. All analyses were conducted in R-language.
Results: We were able to identify regions with high activity in the network, and premises that have a high reachability and therefore could be important nodes for dissemination of disease. We also were able to identify the most transited routes for the road network during different time periods.
To the best of our knowledge this is one of the first studies incorporating temporal and spatial structure, at a fine scale, of a cattle network using real-route analysis (and not Euclidean distances). It is also one of the first network analyses of the cattle industry within a state in the US, and the first in California. We plan to use these results to inform and parametrize disease spread models for emergency response planning for diseases affecting cattle such as foot-and-mouth disease.
Acknowledgements
Data was provided by the Animal Health branch from the California Department of Food and Agriculture.
Funding for supporting the student was provided by UC MEXUS.
References
California Agricultural Statistics Review. (2017-18). Retrieved from www.cdfa.ca.gov/statistics
Dubé, C., Ribble, C., Kelton, D., & McNab, B. (2009). A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development. Transboundary and Emerging Diseases, 56(3), 73–85. https://doi.org/10.1111/j.1865-1682.2008.01064.x
Martínez-López, B., Perez, A. M., & Sánchez-Vizcaíno, J. M. (2009). Social network analysis. Review of general concepts and use in preventive veterinary medicine. Transboundary and Emerging Diseases, 56(4), 109–120. https://doi.org/10.1111/j.1865-1682.2009.01073.x
Keywords:
dynamic network analysis,
Spatio-Temporal Analysis,
Livestock industry,
Movements Network,
Geographical informatics system (GIS)
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Student oral presentation
Topic:
Spatial-explicit or spatio-temporal network analysis
Citation:
Gomez-Vazquez
JP,
Quiroz
L,
Javidmehr
A,
Louie
A and
Martínez-López
B
(2019). Spatio-Temporal analysis of the trade network of the cattle industry in California.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00069
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Received:
20 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Mx. Jose P Gomez-Vazquez, Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, Davis, California, CA 95616-5270, United States, jpgo@ucdavis.edu