Patterns of contact within the New Zealand poultry industry

Unravelling direct and indirect contact patterns between duck farms in France and their association with the 2016-2017 epidemic of Highly Pathogenic Avian Influenza (H5N8)

Live animal movements generate direct contacts (via the exchange of live animals) and indirect contacts (via the transit of transport vehicles) between farms, which can contribute to the spread of pathogens. However, most analyses focus solely on direct contacts and can therefore underestimate the contribution of live animal movements in the spread of infectious diseases. Here, we used French live duck movement data (2016-2018) from one of the largest transport companies to compare direct and indirect contact patterns between duck farms and evaluate how these patterns were associated with the French 2016-2017 epidemic of highly pathogenic avian influenza H5N8. A total number of 614 farms were included in the study, and two directed networks were generated: the animal introduction network (exchange of live ducks) and the transit network (transit of transport vehicles). Following descriptive analyses, these two networks were scrutinized in relation to farm infection status during the epidemic. Results showed that farms were substantially more connected in the transit network than in the animal introduction network and that the transit of transport vehicles generated more opportunities for transmission than the exchange of live animals. We also showed that animal introduction and transit networks' statistics decreased substantially during the epidemic (January-March 2017) compared to non-epidemic periods (January-March 2016 and January-March 2018). We estimated a probability of 33.3 % that a farm exposed to the infection through either of the two live duck movement networks (i.e. that was in direct or indirect contact with a farm that was reported as infected in the following seven days) becomes infected within seven days after the contact. However, we also demonstrated that the level of exposure of farms by these two contact patterns was low, leading only to a handful of transmission events through these routes. As a consequence, we showed that live animal movement patterns are efficient transmission routes for HPAI but have been efficiently reduced to limit the spread during the French 2020-2021 epidemic. These results underpin the relevance of studying indirect contacts resulting from the movement of animals to understand their transmission potential and the importance of accounting for both routes when designing disease control strategies.

Transmission dynamics of an antimicrobial resistant Campylobacter jejuni lineage in New Zealand's commercial poultry network

Understanding the relative contribution of different between-farm transmission pathways is essential in guiding recommendations for mitigating disease spread. This study investigated the association between contact pathways linking poultry farms in New Zealand and the genetic relatedness of antimicrobial resistant Campylobacter jejuni Sequence Type 6964 (ST-6964), with the aim of identifying the most likely contact pathways that contributed to its rapid spread across the industry. Whole-genome sequencing was performed on 167C. jejuni ST-6964 isolates sampled from across 30 New Zealand commercial poultry enterprises. The genetic relatedness between isolates was determined using whole genome multilocus sequence typing (wgMLST). Permutational multivariate analysis of variance and distance-based linear models were used to explore the strength of the relationship between pairwise genetic associations among the C. jejuni isolates and each of several pairwise distance matrices, indicating either the geographical distance between farms or the network distance of transportation vehicles. Overall, a significant association was found between the pairwise genetic relatedness of the C. jejuni isolates and the parent company, the road distance and the network distance of transporting feed vehicles. This result suggests that the transportation of feed within the commercial poultry industry as well as other local contacts between flocks, such as the movements of personnel, may have played a significant role in the spread of C. jejuni. However, further information on the historical contact patterns between farms is needed to fully characterise the risk of these pathways and to understand how they could be targeted to reduce the spread of C. jejuni.

Using multiple data sources to explore disease transmission risk between commercial poultry, backyard poultry, and wild birds in New Zealand

The movements of backyard poultry and wild bird populations are known to pose a disease risk to the commercial poultry industry. However, it is often difficult to estimate this risk due to the lack of accurate data on the numbers, locations, and movement patterns of these populations. The main aim of this study was to evaluate the use of three different data sources when investigating disease transmission risk between poultry populations in New Zealand including (1) cross-sectional survey data looking at the movement of goods and services within the commercial poultry industry, (2) backyard poultry sales data from the online auction site TradeMe®, and (3) citizen science data from the wild bird monitoring project eBird. The cross-sectional survey data and backyard poultry sales data were transformed into network graphs showing the connectivity of commercial and backyard poultry producers across different geographical regions. The backyard poultry network was also used to parameterise a Susceptible-Infectious (SI) simulation model to explore the behaviour of potential disease outbreaks. The citizen science data was used to create an additional map showing the spatial distribution of wild bird observations across New Zealand. To explore the potential for diseases to spread between each population, maps were combined into bivariate choropleth maps showing the overlap between movements within the commercial poultry industry, backyard poultry trades and, wild bird observations. Network analysis revealed that the commercial poultry network was highly connected with geographical clustering around the urban centres of Auckland, New Plymouth and Christchurch. The backyard poultry network was also a highly active trade network and displayed similar geographic clustering to the commercial network. In the disease simulation models, the high connectivity resulted in all suburbs becoming infected in 96.4 % of the SI simulations. Analysis of the eBird data included reports of over 80 species; the majority of which were identified as coastal seabirds or wading birds that showed little overlap with either backyard or commercial poultry. Overall, our study findings highlight how the spatial patterns of trading activity within the commercial poultry industry, alongside the movement of backyard poultry and wild birds, have the potential to contribute significantly to the spread of diseases between these populations. However, it is clear that in order to fully understand this risk landscape, further data integration is needed; including the use of additional datasets that have further information on critical variables such as environmental factors.

Estimating the level of disease risk and biosecurity on commercial poultry farms in New Zealand

Aims: To collect baseline data on the contact risk pathways and biosecurity practices of commercial poultry farms in New Zealand, investigate the relationship between the farm-level disease contact risks and biosecurity practices, and identify important poultry health concerns of producers. Methods: A cross-sectional survey of all registered New Zealand commercial poultry operations was conducted in 2016 collecting information on farm demographics, biosecurity practices, and contact risk pathways. Survey responses were used to generate an unweighted subjective disease risk score based on eight risk criteria and a subjective biosecurity score based on the frequency with which producers reported implementing seven biosecurity measures. Producer opinions towards poultry health issues were also determined. Results: Responses to the survey response were obtained from 120/414 (29.0%) producers, including 57/157 (36.3%) broiler, 33/169 (19.5%) layer, 24/55 (44%) breeder, and 6/32 (19%) other poultry production types. Median disease risk scores differed between production types (p < 0.001) and were lowest for breeder enterprises. The greatest risk for layer and broiler enterprises was from the potential movement of employees between sheds, and for breeder enterprises was the on- and off-farm movement of goods and services. Median biosecurity scores also differed between production types (p < 0.001), and were highest for breeder and broiler enterprises. Across all sectors there was no statistical correlation between biosecurity scores and disease risk scores. Producers showed a high level of concern over effectively managing biosecurity measures. Conclusions: The uptake of biosecurity measures in the commercial poultry farms surveyed was highly variable, with some having very low scores despite significant potential disease contact risks. This may be related to the low prevalence or absence of many important infectious poultry diseases in New Zealand leading farmers to believe there is a limited need to maintain good biosecurity as well as farmer uncertainty around the efficacy of different biosecurity measures. Further research is needed to understand barriers towards biosecurity adoption including evaluating the cost-effectiveness of biosecurity interventions.

Using social network analysis to characterize the collaboration network of backyard poultry trainers in ackCalifornia

In order to better understand collaboration among trainers in the backyard poultry community (i.e. feed store managers, youth development programs (i.e. 4-H), veterinarians, government agencies, extension resources and backyard poultry club leaders), Social Network Analysis (SNA) was used as a tool to better characterize and quantify the current collaboration network structure of backyard poultry trainers in California. Invited trainer attendees of two "Train-the-Trainers" poultry workshops (n = 67) held in Northern and Southern California were given a survey that asked them to list contacts that they collaborated with on backyard poultry (BYP) related work. The collaboration network in this study included a total of 109 trainers, 18 practitioners, and 32 individuals who are both trainers and practitioners for a total of 170 nodes (11 individuals did not have affiliation information available). In order to help identify central actors or collaboration leaders, the surveys were analyzed using Social Network Analysis (SNA), which allows for a quantitative analysis of relationships among various stakeholders. While the SNA showed that the existing collaboration network is disconnected with a clustering coefficient of 0.043 and median total degree centrality of 1 (range 9) and therefore not conducive for collaboration, key insights that could help restructure and improve the network were identified. As an example, among different poultry groups, 4-H was identified as the organization with the second highest median coverage score and fifth highest median centrality score. In addition, 4-H group leaders act as both trainers and practitioners. Consequently, outreach to 4-H group leaders throughout the state would potentially have the greatest impact with respect to overall coverage both inside and outside the 4-H network due to their high centrality and boundary spanning roles. Using SNA to strengthen the collaboration network infrastructure of backyard poultry trainers ultimately offers a more targeted approach toward extension for backyard poultry owners, which could ultimately facilitate communication and knowledge-sharing with BYP owners during a disease outbreak.

Social network analysis for poultry HPAI transmission

In this survey study, the networks among poultry farms and related poultry enterprises in two counties in China (Feixi County in Anhui Province and Beizhen city in Liaoning Province) were analysed and evaluated focusing on the connectivity of contacts, movements, and potential pathogen transmission. The Feixi County poultry production network exhibited greater connectivity, which incorporated approximately 94% of the farms interviewed in a major component (a set of connected farms not linked with each other), mainly due to linkages of backyard farms through local produce stores and individual agents, whilst the Beizhen City network was more fragmented owing to independent in-house operations (from breed, raise, to slaughter and process) of a few large companies, with multiple smaller components. A range of factors influencing the contacts/movements among farms (act as bridges) were identified in this study. Ability to predict the pathway with the network characteristics on the basis of the factors, such as entity type and geographic location, is useful for developing risk-based approaches for disease prevention, surveillance, early detection, and effective controlling.

A large-scale study of a poultry trading network in Bangladesh: implications for control and surveillance of avian influenza viruses

BACKGROUND

Since its first report in 2007, avian influenza (AI) has been endemic in Bangladesh. While live poultry marketing is widespread throughout the country and known to influence AI dissemination and persistence, trading patterns have not been described. The aim of this study is to assess poultry trading practices and features of the poultry trading networks which could promote AI spread, and their potential implications for disease control and surveillance. Data on poultry trading practices was collected from 849 poultry traders during a cross-sectional survey in 138 live bird markets (LBMs) across 17 different districts of Bangladesh. The quantity and origins of traded poultry were assessed for each poultry type in surveyed LBMs. The network of contacts between farms and LBMs resulting from commercial movements of live poultry was constructed to assess its connectivity and to identify the key premises influencing it.

RESULTS

Poultry trading practices varied according to the size of the LBMs and to the type of poultry traded. Industrial broiler chickens, the most commonly traded poultry, were generally sold in LBMs close to their production areas, whereas ducks and backyard chickens were moved over longer distances, and their transport involved several intermediates. The poultry trading network composed of 445 nodes (73.2% were LBMs) was highly connected and disassortative. However, the removal of only 5.6% of the nodes (25 LBMs with the highest betweenness scores), reduced the network's connectedness, and the maximum size of output and input domains by more than 50%.

CONCLUSIONS

Poultry types need to be discriminated in order to understand the way in which poultry trading networks are shaped, and the level of risk of disease spread that these networks may promote. Knowledge of the network structure could be used to target control and surveillance interventions to a small number of LBMs.

Modelling farm-to-farm disease transmission through personnel movements: from visits to contacts, and back

Infectious diseases in livestock can be transmitted through fomites: objects able to convey infectious agents. Between-farm spread of infections through fomites is mostly due to indirect contacts generated by on-farm visits of personnel that can carry pathogens on their clothes, equipment, or vehicles. However, data on farm visitors are often difficult to obtain because of the heterogeneity of their nature and privacy issues. Thus, models simulating disease spread between farms usually rely on strong assumptions about the contribution of indirect contacts on infection spread. By using data on veterinarian on-farm visits in a dairy farm system, we built a simple simulation model to assess the role of indirect contacts on epidemic dynamics compared to cattle movements (i.e. direct contacts). We showed that including in the simulation model only specific subsets of the information available on indirect contacts could lead to outputs widely different from those obtained with the full-information model. Then, we provided a simple preferential attachment algorithm based on the probability to observe consecutive on-farm visits from the same operator that allows overcoming the information gaps. Our results suggest the importance of detailed data and a deeper understanding of visit dynamics for the prevention and control of livestock diseases.

Implications of the cattle trade network in Cameroon for regional disease prevention and control

Movement of live animals is a major risk factor for the spread of livestock diseases and zoonotic infections. Understanding contact patterns is key to informing cost-effective surveillance and control strategies. In West and Central Africa some of the most rapid urbanization globally is expected to increase the demand for animal-source foods and the need for safer and more efficient animal production. Livestock trading points represent a strategic contact node in the dissemination of multiple pathogens. From October 2014 to May 2015 official transaction records were collected and a questionnaire-based survey was carried out in cattle markets throughout Western and Central-Northern Cameroon. The data were used to analyse the cattle trade network including a total of 127 livestock markets within Cameroon and five neighboring countries. This study explores for the first time the influence of animal trade on infectious disease spread in the region. The investigations showed that national borders do not present a barrier against pathogen dissemination and that non-neighbouring countries are epidemiologically connected, highlighting the importance of a regional approach to disease surveillance, prevention and control. Furthermore, these findings provide evidence for the benefit of strategic risk-based approaches for disease monitoring, surveillance and control, as well as for communication and training purposes through targeting key regions, highly connected livestock markets and central trading links.

Social network analysis of the movement of poultry to and from live bird markets in Bali and Lombok, Indonesia

Highly pathogenic avian influenza H5N1 has resulted in large losses to the Indonesian poultry sector. Evidence suggests that live bird markets (LBMs) play an important role in the epidemiology of the disease. Knowledge of the frequency and type of contact between the various poultry market players should allow animal health authorities to develop a better understanding of factors influencing virus transmission between Indonesian villages. A questionnaire-based cross-sectional survey was conducted in 17 LBMs on the neighbouring Indonesian islands of Bali and Lombok to investigate the movement patterns of poultry to and from markets. Using social network analyses, a network of contacts was created for each island from a total of 413 live poultry traders and 134 customers. Individual nodes with high degree and/or betweenness were identified in each network. The Lombok network was more dense and connected than the Bali network indicating that disease transmission would be more efficient in the Lombok network. Our findings indicate that whilst live poultry are typically transported over relatively short distances of approximately 10 km, it is not uncommon for traders and customers to travel in excess of 100 km to buy or sell poultry, which may facilitate the spread of disease over a large geographical area. This study highlights the different roles markets play in poultry movement networks and their potential for disease dissemination. The identification of highly influential market nodes allows authorities to target HPAI surveillance activities to locations where disease is more likely to be present, which is crucial in low-resource settings.

Using social network analysis to inform disease control interventions

Contact patterns between individuals are an important determinant for the spread of infectious diseases in populations. Social network analysis (SNA) describes contact patterns and thus indicates how infectious pathogens may be transmitted. Here we explore network characteristics that may inform the development of disease control programes. This study applies SNA methods to describe a livestock movement network of 180 farms in New Zealand from 2006 to 2010. We found that the number of contacts was overall consistent from year to year, while the choice of trading partners tended to vary. This livestock movement network illustrated how a small number of farms central to the network could play a potentially dominant role for the spread of infection in this population. However, fragmentation of the network could easily be achieved by "removing" a small proportion of farms serving as bridges between otherwise isolated clusters, thus decreasing the probability of large epidemics. This is the first example of a comprehensive analysis of pastoral livestock movements in New Zealand. We conclude that, for our system, recording and exploiting livestock movements can contribute towards risk-based control strategies to prevent and monitor the introduction and the spread of infectious diseases in animal populations.

Network analysis of wildlife translocations in New Zealand

AIMS

To identify network measures with relevance to disease spread in a network of movements derived from the Department of Conservation (DOC) translocation records from 1970 to mid-2014, and to identify conservation sites that should be prioritised for surveillance activities and improvements to data collection to make the best use of network analysis techniques in the future.

METHODS

Data included the source and destination of translocated specimens, the species and the dates the translocations were expected to occur. The data were used to construct a directed, non-weighted network in which a translocation event represented a tie in the network. Network density, in-degree (movements entering a node of interest) and out-degree (movements leaving a node of interest) and reciprocity were calculated.

RESULTS

The data analysed consisted of 692 unique translocations between 307 sites, with the majority (518; 73%) being for birds. The constructed network for bird, reptile and frog translocations comprised 260 nodes, with 34/260 (13%) having two-way movements and 47/260 (18%) non-reciprocal movements. The median degree score (sum of in- and out-degree) was two (min 0, max 36) with a mean of 3.5 in a right skewed distribution. Most sites acted as receivers or senders of consignments with only a few having both high in- and high out-degree, and thus had characteristics that made them sites of interest for surveillance activities. These included the National Wildlife Centre at Mount Bruce, Tiritiri Matangi Island and Te Kakahu (Chalky Island).

CONCLUSIONS

The presence of linking sites that join larger clusters within the network creates the potential for rapid disease spread if a pathogen were to be introduced. The important sites that supply or receive specimens for translocations are already well recognised by those performing translocations in New Zealand, and this paper provides further information by quantifying their role within the network.

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.

Network analysis of swine shipments in Ontario, Canada, to support disease spread modelling and risk-based disease management

Understanding contact networks are important for modelling and managing the spread and control of communicable diseases in populations. This study characterizes the swine shipment network of a multi-site production system in southwestern Ontario, Canada. Data were extracted from a company's database listing swine shipments among 251 swine farms, including 20 sow, 69 nursery and 162 finishing farms, for the 2-year period of 2006 to 2007. Several network metrics were generated. The number of shipments per week between pairs of farms ranged from 1 to 6. The medians (and ranges) of out-degree were: sow 6 (1-21), nursery 8 (0-25), and finishing 0 (0-4), over the entire 2-year study period. Corresponding estimates for in-degree of nursery and finishing farms were 3 (0-9) and 3 (0-12) respectively. Outgoing and incoming infection chains (OIC and IIC), were also measured. The medians (ranges) of the monthly OIC and IIC were 0 (0-8) and 0 (0-6), respectively, with very similar measures observed for 2-week intervals. Nursery farms exhibited high measures of centrality. This indicates that they pose greater risks of disease spread in the network. Therefore, they should be given a high priority for disease prevention and control measures affecting all age groups alike. The network demonstrated scale-free and small-world topologies as observed in other livestock shipment studies. This heterogeneity in contacts among farm types and network topologies should be incorporated in simulation models to improve their validity. In conclusion, this study provided useful epidemiological information and parameters for the control and modelling of disease spread among swine farms, for the first time from Ontario, Canada.

Analysis of horse movements from non-commercial horse properties in New Zealand

AIMS

To investigate property-level factors associated with the movement of horses from non-commercial horse properties, including the size and location of the property, number and reason for keeping horses.

METHODS

Using a cross-sectional survey 2,912 questionnaires were posted to randomly selected non-commercial horse properties listed in a rural property database. The survey collected information about the number of horses, and reasons for keeping horses on the property, and any movement of horses in the previous 12 months. Three property-level outcomes were investigated; the movement status of the property, the frequency of movement events, and the median distance travelled from a property. Associations were examined using logistic regression and Kruskal-Wallis analysis of variance.

RESULTS

In total 62.0% (488/791) of respondents reported at least one movement event in the year prior to the survey, for a total of 22,050 movement events. The number of movement events from a property varied significantly by the number of horses on the property (p<0.02), while the median distance travelled per property varied significantly by both region (p<0.03) and property size (p<0.01). Region, property size, the number of horses kept, and keeping horses for competition, recreation, racing or as pets were all significantly associated with movement status in the multivariable analyses (p<0.001).

CONCLUSION AND CLINICAL RELEVANCE

This study showed that there are characteristics of non-commercial horse properties that influence movement behaviour. During an exotic disease outbreak the ability to identify properties with these characteristics for targeted control will enhance the effectiveness of control measures.

The importance of location in contact networks: Describing early epidemic spread using spatial social network analysis

This paper explores methods for describing the dynamics of early epidemic spread and the clustering of infected cases in space and time when an underlying contact network structure is influencing disease spread. A novel method of describing an epidemic is presented that applies social network analysis to characterise the importance of both spatial location and contact network position. This method enables the development of a model of how these clusters formed, incorporating spatial clustering and contact network topology. Based on data from the first 30 days of the 2007 equine influenza outbreak in Australia, clusters of infected premises (IPs) were identified and delineated using social network analysis to integrate contact-tracing and spatial relationships. Clusters identified by this approach were compared to those detected using the space-time scan statistic to analyse the same data. To further investigate the importance of network and spatial location in epidemic spread, kriging by date of onset of clinical signs was used to model the spatio-temporal spread without reference to underlying contact network structure. Leave-one-out cross-validation was used to derive a summary estimate of the accuracy of the kriged surface. Observations poorly explained by the kriged surface were identified, and their position within the contact network was explored to determine if they could be better explained through analysis of the underlying contact network. The contact network was found to lie at the core of a combined network model of spread, with infected horse movements connecting spatial clusters of infected premises. Kriging was imprecise in describing the pattern of spread during this early phase of the outbreak (explaining only 13% of the variation in date of onset of IPs), because early dissemination was dominated by network-based spread. Combined analysis of spatial and contact network data demonstrated that over the first 30 days of this outbreak local spread emanated outwards from the small number of infected premises in the contact network, up to a distance of around 15km. Consequently, when a contact network structure underlies epidemic spread, we recommend applying a method of spatial analysis that incorporates the network component of disease spread. Linking the spatial and network analysis of epidemics facilitates inference of the methods of disease transmission, providing a description of the sequence of spatial cluster formation.