Abstract
Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: (1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, (2) develop a network based on those estimated probabilities, and (3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum information entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of the small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.
Highlights
Livestock are often moved between facilities to reduce costs and improve productivity
We have three objectives: [1] we compute optimal estimates swine movement probabilities among counties from the aggregated data of United State Department of Agriculture (USDA)-NASS, [2] we develop a realization of the network from the estimated probabilities, and [3] we analyze the developed network with different network analysis metrics
Animal movement has been one of the major causes of diseases spread among farms for several outbreaks in the United States (US) swine industry
Summary
Livestock are often moved between facilities to reduce costs and improve productivity. Another research group predicted movement networks of the swine industry for some counties of Minnesota using a machine learning approach6 They used confidential survey data from two counties to train their model. We propose a novel algorithm to develop a farm level swine movement network using the estimated swine movement probabilities. To understand the generated swine movement network, we use network centrality measures. They have been used often in the literature to understand the livestock movement patterns. The network centrality measures can assist in detection of the important farms, which can control the movement flows in the network This information can be useful to plan effective mitigation strategies to reduce an epidemic size. From the analysis of the developed swine movement network, we find a trace of the small world phenomenon and the presence of hubs in the US swine movement network
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