Chronic wasting disease is a transmissible spongiform encephalopathy that affects cervids with a clinical picture of muscle wasting in infected animals. The objective of this study was to quantify movement patterns of farmed cervids in the state of Minnesota as a model for identifying potential disease mitigation points. Time aggregated network analysis was performed on data consisting of 1221 intra-state cervid movements from farms located within Minnesota (n = 432 farms). Intra-state movements accounted for 48.2 % of all documented movements (2578) in Minnesota from 2013 to 2018; the remaining movements were inter-state. Annual networks were sparse in nature with low graph densities (6.9 × 10-4 - 1.4 × 10-3) and transitivity (0.06-0.12). Frequency of movements increased significantly (p < 0.05) in the months of September and October before decreasing in November, which coincided with the breeding and hunting seasons. Some of these contacts were as far as 500 km apart. The median length of infection chains for CWD positive farms was estimated to be 5.0 and 6.0 farms in-and out-going infection chains, respectively. A k-test analysis demonstrated that the observed median number of infected farms directly connected to other infected farms was 2.0, which was significantly higher than a fortuitous event (p = 0.002). Movements of cervids between farms were largely unpredictable with very low edge overlap (mean 0.02 %) from year to year, suggesting that persistent commercial relationships among farms were rare. In conclusion, long distance trade movements present a risk for spread of chronic wasting disease in Minnesota. The sparse networks and unpredictable farm contacts could be because cervid production is not as vertically integrated as other species-differentiated and established industries, such as swine or poultry. Our analytical approach can be used to understand chronic wasting disease in other states in the U.S. and North America in general.
Keywords: Disease transmission; Farmed cervids; North America; Social network analysis.
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