Description of empirical movement data from Canadian swine herds with an application to a disease spread simulation model

On-farm characteristics and biosecurity protocols for small-scale swine producers in eastern Australia

Pigs are considered high risk for the introduction and spread of foot and mouth disease (FMD) in Australia. Facilities where animals from different origins are commingled, such as saleyards, pose a high risk for disease spread. Sound on-farm management practices and biosecurity protocols are the first line of defence against a potential on-farm disease outbreak. This study evaluated the practices of 104 producers (vendors who sold pigs and purchasers of live pigs for grow-out) who traded pigs at 6 peri-urban and rural saleyards in eastern Australia. Specifically, management and on-farm biosecurity practices were assessed using an in-depth questionnaire. Univariable and multivariable logistic regression analyses were used to investigate (1) producer associations: producer type, State, motivation to keep pigs, farm type, gender, years having owned pigs, and the acquisition of formal livestock qualifications; and (2) pig associations: herd size, housing, management (husbandry and feeding) practices and biosecurity (including pig movement) practices. Backyard operations (<20 sows) were undertaken by 60.6% of participants, followed by small-scale pig operations (28.8%; 21-100 sows). Few producers (16.3%) reported residing in close proximity (<5 km) to commercial operations; however, less rural producers had neighbouring hobby pig operations within 5 km of their property (P=0.033). Motivation for keeping pigs was significantly associated with a number of biosecurity practices. Producers who kept pigs for primary income were more likely to provide footwear precautions (P=0.007) and ask visitors about prior pig contacts (P=0.004). Approximately 40% of backyard and small-scale producers reported not having any quarantine practices in place for incoming pigs, compared to only 9.1% among larger producers. The main reasons cited for not adopting on-farm biosecurity practices in this study included having no need on their property (43.1%) and a lack of information and support (by the industry and/or authorities; 18.5%). Up to three-quarters of all producers maintained an open breeding herd, regularly introducing new pigs to the main herd. Saleyards are an important source of income for backyard and small-scale producers as well as an important risk factor for the introduction and dissemination of endemic and emerging animal diseases. Differing management and biosecurity practices as well as the motivations of these producers keeping pigs in small numbers and trading pigs at saleyards need to be taken into account in the development of successful biosecurity extension programmes for this sector of the Australian pork industry.

One-Health Simulation Modelling: Assessment of Control Strategies Against the Spread of Influenza between Swine and Human Populations Using NAADSM

Simulation models implemented using a range of parameters offer a useful approach to identifying effective disease intervention strategies. The objective of this study was to investigate the effects of key control strategies to mitigate the simultaneous spread of influenza among and between swine and human populations. We used the pandemic H1N1 2009 virus as a case study. The study population included swine herds (488 herds) and households-of-people (29,707 households) within a county in Ontario, Canada. Households were categorized as: (i) rural households with swine workers, (ii) rural households without swine workers and (iii) urban households without swine workers. Seventy-two scenarios were investigated based on a combination of the parameters of speed of detection and control strategies, such as quarantine strategy, effectiveness of movement restriction and ring vaccination strategy, all assessed at three levels of transmissibility of the virus at the swine-human interface. Results showed that the speed of detection of the infected units combined with the quarantine strategy had the largest impact on the duration and size of outbreaks. A combination of fast to moderate speed of the detection (where infected units were detected within 5-10 days since first infection) and quarantine of the detected units alone contained the outbreak within the swine population in most of the simulated outbreaks. Ring vaccination had no added beneficial effect. In conclusion, our study suggests that the early detection (and therefore effective surveillance) and effective quarantine had the largest impact in the control of the influenza spread, consistent with earlier studies. To our knowledge, no study had previously assessed the impact of the combination of different intervention strategies involving the simultaneous spread of influenza between swine and human populations.

Analysis of Swine Movement in Four Canadian Regions: Network Structure and Implications for Disease Spread

Direct and indirect contacts among animal holdings are important in the spread of infectious diseases. The objectives of this study were to describe networks of pig movements and the sharing of trucks used for those movements between swine farms in four Canadian regions using network analysis tools and to obtain contact parameters for infectious disease spread simulation models. Four months of swine movement data from a pilot pig traceability programme were used. Two types of networks were created using three time scales (weekly, monthly and the full study period): one-mode networks of farm-to-farm direct contact representing animal shipments and two-mode networks representing the sharing of trucks between farms. Contact patterns among farms were described by estimating a range of relevant network measures. The overall network neglecting the four regions consisted of 145 farms, which were connected by 261 distinct links. A total of 184 trucks were used to transport 2043 shipments of pigs during the study period. The median in- and out-degree for the overall one-mode network was 1 and ranged from 0 to 26 and 0 to 10, respectively. The overall one-mode network had heterogeneous degree distribution, a high clustering coefficient and shorter average path length than would be expected for randomly generated networks of similar size. On average one truck was shared by four farms in the overall network, or by three farms when considered the monthly and weekly networks. Degree distribution of the two-mode overall network demonstrated characteristics of power-law distribution. For more than 50% of shipments on any given day, the same truck was used for at least one other shipment. Findings from this study are in agreement with previous work, which suggested that swine movement networks exhibit small-world and scale-free topologies. Furthermore, trucks used for the shipment of pigs can play an important role in connecting otherwise unconnected farms and may increase the spread of disease.

Estimating animal movement contacts between holdings of different production types

Animal movement poses a great risk for disease transmission between holdings. Heterogeneous contact patterns are known to influence the dynamics of disease transmission and should be included in modeling. Using pig movement data from Sweden as an example, we present a method for quantification of between holding contact probabilities based on different production types. The data contained seven production types: Sow pool center, Sow pool satellite, Farrow-to-finish, Nucleus herd, Piglet producer, Multiplying herd and Fattening herd. The method also estimates how much different production types will determine the contact pattern of holdings that have more than one type. The method is based on Bayesian analysis and uses data from central databases of animal movement. Holdings with different production types are estimated to vary in the frequency of contacts as well as in what type of holding they have contact with, and the direction of the contacts. Movements from Multiplying herds to Sow pool centers, Nucleus herds to other Nucleus herds, Sow pool centers to Sow pool satellites, Sow pool satellites to Sow pool centers and Nucleus herds to Multiplying herds were estimated to be most common relative to the abundance of the production types. We show with a simulation study that these contact patterns may also be expected to result in substantial differences in disease transmission via animal movements, depending on the index holding. Simulating transmission for a 1 year period showed that the median number of infected holdings was 1 (i.e. only the index holding infected) if the infection started at a Fattening herd and 2161 if the infection started on a Nucleus herd. We conclude that it is valuable to include production types in models of disease transmission and the method presented in this paper may be used for such models when appropriate data is available. We also argue that keeping records of production types is of great value since it may be helpful in risk assessments.

A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development

Livestock movements are important in spreading infectious diseases and many countries have developed regulations that require farmers to report livestock movements to authorities. This has led to the availability of large amounts of data for analysis and inclusion in computer simulation models developed to support policy formulation. Social network analysis has become increasingly popular to study and characterize the networks resulting from the movement of livestock from farm-to-farm and through other types of livestock operations. Network analysis is a powerful tool that allows one to study the relationships created among these operations, providing information on the role that they play in acquiring and spreading infectious diseases, information that is not readily available from more traditional livestock movement studies. Recent advances in the study of real-world complex networks are now being applied to veterinary epidemiology and infectious disease modelling and control. A review of the principles of network analysis and of the relevance of various complex network theories to infectious disease modelling and control is presented in this paper.