Spatial and network analysis of U.S. livestock movements based on Interstate Certificates of Veterinary Inspection.

Livestock movements are a common pathway for the spread infectious diseases in a population. An understanding of livestock movement patterns is needed to understand national transmission risks of highly infectious diseases during epidemics. Social Network Analysis (SNA) is an approach that helps to describe the relationships among individuals and the implications of those relationships. We used SNA to describe the contact structure of livestock movements throughout the contiguous U.S. from April 1st, 2015 to March 31st, 2016. We describe 4 network types: beef cattle, dairy cattle, swine, and small ruminant. Livestock movement data were sourced from Interstate Certificates of Veterinary Inspection (ICVI) while county-level farm demographic data were from the National Agricultural Statistics Service (NASS). In the described networks, nodes are represented by counties and arcs by shipments between nodes; the networks were weighted based on the number of shipments between nodes. For the analyses, movement data were aggregated at the county level and on an annual basis. Measures of centrality and cohesiveness were computed and identification of trade-communities in all networks was conducted. During the study period, a total of 219,042 movements were recorded and beef cattle movements accounted for 63 % of all movements. At least 70 % of U.S. counties were present in each of the networks, but the density of arcs was less than 2% in all networks. In the beef cattle network, counties with high out-degree were strongly correlated (0.8) with the number of beef cows per county while for the dairy cattle network a strong correlation (>0.86) was found with the number of dairy cattle per km2 at the county level. All networks were found to have between 4 and 6 large communities (50 counties or more per community), and were geographically clustered except for the communities in the small ruminant network. Outputs reported in these analyses can help to understand the structure of the contact networks for beef cattle, dairy cattle, swine, and small ruminants. They may also be used in conjunction with simulation modeling to evaluate spread of highly infectious disease such as foot-and-mouth disease at the national level and to evaluate the application of intervention strategies.

Using exceedance probabilities to detect anomalies in routinely recorded animal health data, with particular reference to foot-and-mouth disease in Viet Nam.

The widespread availability of computer hardware and software for recording and storing disease event information means that, in theory, we have the necessary information to carry out detailed analyses of factors influencing the spatial distribution of disease in animal populations. However, the reliability of such analyses depends on data quality, with anomalous records having the potential to introduce significant bias and lead to inappropriate decision making. In this paper we promote the use of exceedance probabilities as a tool for detecting anomalies when applying hierarchical spatio-temporal models to animal health data. We illustrate this methodology through a case study data on outbreaks of foot-and-mouth disease (FMD) in Viet Nam for the period 2006-2008. A flexible binomial logistic regression was employed to model the number of FMD infected communes within each province of the country. Standard analyses of the residuals from this model failed to identify problems, but exceedance probabilities identified provinces in which the number of reported FMD outbreaks was unexpectedly low. This finding is interesting given that these provinces are on major cattle movement pathways through Viet Nam.

Patterns of contact within the New Zealand poultry industry.

Members of the Poultry Industry Association and the Egg Producers Federation of New Zealand (n=420) were sent a questionnaire asking them to describe the type and frequency of on- and off-enterprise movements relating to feed, live birds and hatching eggs, table eggs and poultry product, and manure and waste litter. Social network analyses were used to describe patterns of contact among poultry enterprises and their associates for these four movement types. The response rate to the survey was 58% (244 out of 420). Network structures for enterprise-to-enterprise movements of feed, live birds and hatching eggs, and table egg and poultry product were characterised by 'hub and spoke' type structures with small-world characteristics. Small worlds were created by network hubs (e.g. feed suppliers and hatcheries) providing goods and services to larger numbers of client farms. In addition to hubs acting as the predominant source of material moving onto farms we identified enterprises acting as bridges between identified small worlds. The presence of these bridges is a concern, since their presence has the potential to facilitate the spread of hazards (e.g. feed contaminants, infectious agents carried within feed) more readily throughout the population. An ability to predict enterprises with these network characteristics on the basis of factors such as shed capacity, enterprise type, geographic location would be useful for developing risk-based approaches to disease prevention, surveillance, detection, response and control activities.

A cross-sectional study of ownership of backyard poultry in two areas of Palmerston North, New Zealand.

AIMS: To determine the proportion of residential land parcels with backyard poultry in an urban and urban-rural fringe area of provincial New Zealand. To document key husbandry and biosecurity practices of owners of backyard poultry, and to identify factors that might assist animal health authorities in locating backyard poultry flocks in the event of an infectious disease emergency. METHODS: A cross-sectional survey was undertaken, in which residents of 449 land parcels in an urban and urban-rural fringe area within and adjacent to the city of Palmerston North, respectively, were visited between February and November 2006. Residents were asked if backyard poultry were kept on the premises. Details recorded for those that kept poultry included the type and number of birds kept, and details of management and biosecurity practices. The geographical distribution of poultry-positive land parcels was assessed for evidence of spatial clustering. RESULTS: Backyard poultry were kept on 3.5 (95% CI=2.1- 5.0)% of land parcels; 1.6 (95% CI=0.7-3.4)% in the urban area and 18.9 (95% CI=11.6-29.3)% in the urban-rural fringe area. There were no significant clusters of poultry-positive land parcels in either area. On all poultry-positive parcels birds were allowed, for at least a portion of the day, to range freely over the property. Three poultry-positive land parcels were within a distance of 1 km of a commercial poultry enterprise in the urban-rural fringe area. Most owners of backyard poultry used feed prepared commercially. CONCLUSIONS: The prevalence of ownership of backyard poultry in this area of New Zealand was low, and varied according to classification of the land, viz urban, or urban-rural fringe. The close proximity of backyard flocks to the single commercial enterprise in the urban-rural fringe area reiterates the importance of strict biosecurity measures on commercial farms. In the event of an infectious disease emergency, it is proposed that a sampling frame of owners of backyard poultry might be rapidly obtained by contacting suppliers of commercial feed.