Unraveling the contact patterns and network structure of pig shipments in the United States and its association with porcine reproductive and respiratory syndrome virus (PRRSV) outbreaks

Evolution of the Swiss pork production systems and logistics: the impact on infectious disease resilience

Livestock production systems are complex and evolve over time, affecting their adaptability to economic, political, and disease-related changes. In Europe, disease resilience is crucial due to threats like the African swine fever virus, which jeopardizes pork production stability. The European Union identifies farm production type as a key risk factor for disease spread, making it important to track changes in farm production types to assess disease risk. However, detailed production type data is often lacking in national databases. For Swiss pig farms, we used prediction and clustering algorithms to classify 9'687 - 11'247 trading farms between 2014 and 2019 by one of eleven production types. We then analyzed the pig trade network and stratified farm centrality measures (ICC and OCC) by production type. We found that 145 farms belonging to three production types have substantially higher ICC and OCC than other farms, suggesting that they could be the target of disease surveillance programs. Our predictions until 2025 show an increase both in overall pig trade network connectivity and in proportion of production types with high ICC and OCC, indicating that the structural changes in the Swiss pig production system may increase infectious disease exposure over time.

A network evaluation of human and animal movement data across multiple swine farm systems in North America

INTRODUCTION

The U.S. swine industry is vulnerable to the rapid spread of disease due to systemic structural issues. While animal movement networks are used to identify disease spread risks and design response plans, human movement between farms were rarely accounted for. Human movements, when integrated with animal movement models, create a different, more inclusive, and accurate network structure when compared to animal movements alone.

METHODS

One year of propriety farm visit data was analyzed and consisted of anonymized property IDs, location, and user/truck IDs, along with visit dates, property, vehicle, and entry types from three swine management companies. A static directed network was created using the igraph package in R for all movements, with separate sub-networks for each entry type (animal, human, and subsets of vehicle types). Network statistics for each sub-network were compared.

RESULTS

The full network included 455 properties, 11 property types, 9 vehicle types, 12 entry types, and 320001 edges (trips between properties). The longest path length was 10 in the animal movement network but decreased to 5 for the full and human movement network, while the average path length decreased from 3.2 to 2.2. Edge density increased from 0.03 to 0.09 for the human network and 0.1 for the full network. For all network properties examined, the full and human movement networks demonstrated higher connectivity than the animal network. A heavy right skew in the degree distributions indicates a 'hub' structure (scale-free-like network) and the shorter path lengths indicates a small-world network topology.

DISCUSSION

The full network is very well connected, more so than expected based on animal movement alone. Hubs may indicate points of disease susceptibility and 'super-spreader' properties. The high connectivity shows that swine farm networks may be more susceptible to spread of an introduced disease than expected from previous analyses.

CONCLUSIONS

Monitoring human, as well as animal movement, provides for a more complete and accurate understanding of swine farm biosecurity risks.

Analyzing the intrastate and interstate swine movement network in the United States

Identifying and restricting animal movements is a common approach used to mitigate the spread of diseases between premises in livestock systems. Therefore, it is essential to uncover between-premises movement dynamics, including shipment distances and network-based control strategies. Here, we analyzed three years of between-premises pig movements, which include 197,022 unique animal shipments, 3973 premises, and 391,625,374 pigs shipped across 20 U.S. states. We constructed unweighted, directed, temporal networks at 180-day intervals to calculate premises-to-premises movement distances, the size of connected components, network loyalty, and degree distributions, and, based on the out-going contact chains, identified network-based control actions. Our results show that the median distance between premises pig movements was 74.37 km, with median intrastate and interstate movements of 52.71 km and 328.76 km, respectively. On average, 2842 premises were connected via 6705 edges, resulting in a weak giant connected component that included 91 % of the premises. The premises-level network exhibited loyalty, with a median of 0.65 (IQR: 0.45 - 0.77). Results highlight the effectiveness of node targeting to reduce the risk of disease spread; we demonstrated that targeting 25 % of farms with the highest degree or betweenness limited spread to 1.23 % and 1.7 % of premises, respectively. While there is no complete shipment data for the entire U.S., our multi-state movement analysis demonstrated the value and the needs of such data for enhancing the design and implementation of proactive- disease control tactics.

The role of vehicle movement in swine disease dissemination: Novel method accounting for pathogen stability and vehicle cleaning effectiveness uncertainties

Several propagation routes drive animal disease dissemination, and among these routes, contaminated vehicles traveling between farms have been associated with indirect disease transmission. In this study, we used near-real-time vehicle movement data and vehicle cleaning efficacy to reconstruct the between-farm dissemination of the African swine fever virus (ASFV). We collected one year of Global Positioning System data of 823 vehicles transporting feed, pigs, and people to 6363 swine production farms in two regions in the U.S. Without cleaning, vehicles connected up to 2157 farms in region one and 437 farms in region two. Individually, in region one vehicles transporting feed connected 2151 farms, pigs to farms 2089 farms, pigs to market 1507 farms, undefined vehicles 1760 farm, and personnel three farms. The simulation results indicated that the contact networks were reduced the most for crew transport vehicles with a 66% reduction, followed by vehicles carrying pigs to market and farms, with reductions of 43% and 26%, respectively, when 100% cleaning efficacy was achieved. The results of this study showed that even when vehicle cleaning and disinfection are 100% effective, vehicles are still connected to numerous farms. This emphasizes the importance of better understanding transmission risks posed by vehicles to the swine industry and regulatory agencies.

Infection prediction in swine populations with machine learning

The pork industry is an essential part of the global food system, providing a significant source of protein for people around the world. A major factor restraining productivity and compromising animal wellbeing in the pork industry is disease outbreaks in pigs throughout the production process: widespread outbreaks can lead to losses as high as 10% of the U.S. pig population in extreme years. In this study, we present a machine learning model to predict the emergence of infection in swine production systems throughout the production process on a daily basis, a potential precursor to outbreaks whose detection is vital for disease prevention and mitigation. We determine features that provide the most value in predicting infection, which include nearby farm density, historical test rates, piglet inventory, feed consumption during the gestation period, and wind speed and direction. We utilize these features to produce a generalizable machine learning model, evaluate the model's ability to predict outbreaks both seven and 30 days in advance, allowing for early warning of disease infection, and evaluate our model on two swine production systems and analyze the effects of data availability and data granularity in the context of our two swine systems with different volumes of data. Our results demonstrate good ability to predict infection in both systems with a balanced accuracy of [Formula: see text] on any disease in the first system and balanced accuracies (average prediction accuracy on positive and negative samples) of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] on porcine reproductive and respiratory syndrome, porcine epidemic diarrhea virus, influenza A virus, and Mycoplasma hyopneumoniae in the second system, respectively, using the six most important predictors in all cases. These models provide daily infection probabilities that can be used by veterinarians and other stakeholders as a benchmark to more timely support preventive and control strategies on farms.

Unveiling invisible farm-to-farm PRRSV-2 transmission links and routes through transmission tree and network analysis

The United States (U.S.) swine industry has struggled to control porcine reproductive and respiratory syndrome (PRRS) for decades, yet the causative virus, PRRSV-2, continues to circulate and rapidly diverges into new variants. In the swine industry, the farm is typically the epidemiological unit for monitoring, prevention, and control; breaking transmission among farms is a critical step in containing disease spread. Despite this, our understanding of farm transmission still is inadequate, precluding the development of tailored control strategies. Therefore, our objective was to infer farm-to-farm transmission links, estimate farm-level transmissibility as defined by reproduction numbers (R), and identify associated risk factors for transmission using PRRSV-2 open reading frame 5 (ORF5) gene sequences, animal movement records, and other data from farms in a swine-dense region of the U.S. from 2014 to 2017. Timed phylogenetic and transmission tree analyses were performed on three sets of sequences (n = 206) from 144 farms that represented the three largest genetic variants of the virus in the study area. The length of inferred pig-to-pig infection chains that corresponded to pairs of farms connected via direct animal movement was used as a threshold value for identifying other feasible transmission links between farms; these links were then transformed into farm-to-farm transmission networks and calculated farm-level R-values. The median farm-level R was one (IQR = 1-2), whereas the R value of 28% of farms was more than one. Exponential random graph models were then used to evaluate the influence of farm attributes and/or farm relationships on the occurrence of farm-to-farm transmission links. These models showed that, even though most transmission events cannot be directly explained by animal movement, movement was strongly associated with transmission. This study demonstrates how integrative techniques may improve disease traceability in a data-rich era by providing a clearer picture of regional disease transmission.

Disentangling transport movement patterns of trucks either transporting pigs or while empty within a swine production system before and during the COVID-19 epidemic

Transport of pigs between sites occurs frequently as part of genetic improvement and age segregation. However, a lack of transport biosecurity could have catastrophic implications if not managed properly as disease spread would be imminent. However, there is a lack of a comprehensive study of vehicle movement trends within swine systems in the Midwest. In this study, we aimed to describe and characterize vehicle movement patterns within one large Midwest swine system representative of modern pig production to understand movement trends and proxies for biosecurity compliance and identify potential risky behaviors that may result in a higher risk for infectious disease spread. Geolocation tracking devices recorded vehicle movements of a subset of trucks and trailers from a production system every 5 min and every time tracks entered a landmark between January 2019 and December 2020, before and during the COVID-19 pandemic. We described 6,213 transport records from 12 vehicles controlled by the company. In total, 114 predefined landmarks were included during the study period, representing 5 categories of farms and truck wash facilities. The results showed that trucks completed the majority (76.4%, 2,111/2,762) of the recorded movements. The seasonal distribution of incoming movements was similar across years (P > 0.05), while the 2019 winter and summer seasons showed higher incoming movements to sow farms than any other season, year, or production type (P < 0.05). More than half of the in-movements recorded occurred within the triad of sow farms, wean-to-market stage, and truck wash facilities. Overall, time spent at each landmark was 9.08% higher in 2020 than in 2019, without seasonal highlights, but with a notably higher time spent at truck wash facilities than any other type of landmark. Network analyses showed high connectivity among farms with identifiable clusters in the network. Furthermore, we observed a decrease in connectivity in 2020 compared with 2019, as indicated by the majority of network parameter values. Further network analysis will be needed to understand its impact on disease spread and control. However, the description and quantification of movement trends reported in this study provide findings that might be the basis for targeting infectious disease surveillance and control.

Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the United States

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movements of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. We found that downstream farms (i.e., finisher and nursery farms) were located in areas of significant-high relative risk of PRRSV. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies to farms and areas within the distance threshold of PRRSV positive farms.

Mapping the Dynamics of Contemporary PRRSV-2 Evolution and Its Emergence and Spreading Hotspots in the U.S. Using Phylogeography

The repeated emergence of new genetic variants of PRRSV-2, the virus that causes porcine reproductive and respiratory syndrome (PRRS), reflects its rapid evolution and the failure of previous control efforts. Understanding spatiotemporal heterogeneity in variant emergence and spread is critical for future outbreak prevention. Here, we investigate how the pace of evolution varies across time and space, identify the origins of sub-lineage emergence, and map the patterns of the inter-regional spread of PRRSV-2 Lineage 1 (L1)-the current dominant lineage in the U.S. We performed comparative phylogeographic analyses on subsets of 19,395 viral ORF5 sequences collected across the U.S. and Canada between 1991 and 2021. The discrete trait analysis of multiple spatiotemporally stratified sampled sets (n = 500 each) was used to infer the ancestral geographic region and dispersion of each sub-lineage. The robustness of the results was compared to that of other modeling methods and subsampling strategies. Generally, the spatial spread and population dynamics varied across sub-lineages, time, and space. The Upper Midwest was a main spreading hotspot for multiple sub-lineages, e.g., L1C and L1F, though one of the most recent emergence events (L1A(2)) spread outwards from the east. An understanding of historical patterns of emergence and spread can be used to strategize disease control and the containment of emerging variants.

Testable Candidate Immune Correlates of Protection for Porcine Reproductive and Respiratory Syndrome Virus Vaccination

Porcine reproductive and respiratory syndrome virus (PRRSV) is an on-going problem for the worldwide pig industry. Commercial and experimental vaccinations often demonstrate reduced pathology and improved growth performance; however, specific immune correlates of protection (CoP) for PRRSV vaccination have not been quantified or even definitively postulated: proposing CoP for evaluation during vaccination and challenge studies will benefit our collective efforts towards achieving protective immunity. Applying the breadth of work on human diseases and CoP to PRRSV research, we advocate four hypotheses for peer review and evaluation as appropriate testable CoP: (i) effective class-switching to systemic IgG and mucosal IgA neutralizing antibodies is required for protective immunity; (ii) vaccination should induce virus-specific peripheral blood CD4+ T-cell proliferation and IFN-γ production with central memory and effector memory phenotypes; cytotoxic T-lymphocytes (CTL) proliferation and IFN-γ production with a CCR7- phenotype that should migrate to the lung; (iii) nursery, finishing, and adult pigs will have different CoP; (iv) neutralizing antibodies provide protection and are rather strain specific; T cells confer disease prevention/reduction and possess greater heterologous recognition. We believe proposing these four CoP for PRRSV can direct future vaccine design and improve vaccine candidate evaluation.

Modeling nation-wide U.S. swine movement networks at the resolution of the individual premises

The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between-premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within- and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data.

A discrete-time survival model for porcine epidemic diarrhoea virus

Since the arrival of porcine epidemic diarrhea virus (PEDV) in the United States in 2013, elimination and control programmes have had partial success. The dynamics of its spread are hard to quantify, though previous work has shown that local transmission and the transfer of pigs within production systems are most associated with the spread of PEDV. Our work relies on the history of PEDV infections in a region of the southeastern United States. This infection data is complemented by farm-level features and extensive industry data on the movement of both pigs and vehicles. We implement a discrete-time survival model and evaluate different approaches to modelling the local-transmission and network effects. We find strong evidence in that the local-transmission and pig-movement effects are associated with the spread of PEDV, even while controlling for seasonality, farm-level features and the possible spread of disease by vehicles. Our fully Bayesian model permits full uncertainty quantification of these effects. Our farm-level out-of-sample predictions have a receiver-operating characteristic area under the curve (AUC) of 0.779 and a precision-recall AUC of 0.097. The quantification of these effects in a comprehensive model allows stakeholders to make more informed decisions about disease prevention efforts.

Characterizing the connection between swine production sites by personnel movements using a mobile application-based geofencing platform

Biosecurity is critical to productivity and profitability in swine production systems and can be achieved by incorporating external (bioexclusion) and internal (biocontainment) practices. Although increasing threats of foreign animal diseases have justified the need of rigorous external biosecurity plans, their effectiveness highly depend on the compliance of on-farm employees, farm-related personnel, and visitors. In this study, we evaluated the uses of a mobile application-based geofencing platform in two swine production systems for accurately identifying personnel movements between swine production sites and detecting potential biosecurity breaches by violating required downtime between site visits. The geofencing platform accurately recognized 95.2% (379/398) of personnel entries comparing to physical entry logs. Further, among 1861 entries over a period of one month, 19 strongly connected components and 12 potential biosecurity breaches were identified. Personnel with duty in communications and information systems committed 75% of biosecurity breaches. The results reported herein demonstrated the possible uses of geofencing platforms for investigating connections among swine production sites by personnel movements and identifying biosecurity breaches.

Modelling and assessing additional transmission routes for porcine reproductive and respiratory syndrome virus: Vehicle movements and feed ingredients

Accounting for multiple modes of livestock disease dissemination in epidemiological models remains a challenge. We developed and calibrated a mathematical model for transmission of porcine reproductive and respiratory syndrome virus (PRRSV), tailored to fit nine modes of between-farm transmission pathways including: farm-to-farm proximity (local transmission), contact network of batches of pigs transferred between farms (pig movements), re-break probabilities for farms with previous PRRSV outbreaks, with the addition of four different contact networks of transportation vehicles (vehicles to transport pigs to farms, pigs to markets, feed and crew) and the amount of animal by-products within feed ingredients (e.g., animal fat or meat and bone meal). The model was calibrated on weekly PRRSV outbreaks data. We assessed the role of each transmission pathway considering the dynamics of specific types of production (i.e., sow, nursery). Although our results estimated that the networks formed by transportation vehicles were more densely connected than the network of pigs transported between farms, pig movements and farm proximity were the main PRRSV transmission routes regardless of farm types. Among the four vehicle networks, vehicles transporting pigs to farms explained a large proportion of infections, sow = 20.9%; nursery = 15%; and finisher = 20.6%. The animal by-products showed a limited association with PRRSV outbreaks through descriptive analysis, and our model results showed that the contribution of animal fat contributed only 2.5% and meat and bone meal only .03% of the infected sow farms. Our work demonstrated the contribution of multiple routes of PRRSV dissemination, which has not been deeply explored before. It also provides strong evidence to support the need for cautious, measured PRRSV control strategies for transportation vehicles and further research for feed by-products modelling. Finally, this study provides valuable information and opportunities for the swine industry to focus effort on the most relevant modes of PRRSV between-farm transmission.

Network analysis of live pig movements in North Macedonia: Pathways for disease spread

Globalization of trade, and the interconnectivity of animal production systems, continues to challenge efforts to control disease. A better understanding of trade networks supports development of more effective strategies for mitigation for transboundary diseases like African swine fever (ASF), classical swine fever (CSF), and foot-and-mouth disease (FMD). North Macedonia, bordered to the north and east by countries with ongoing ASF outbreaks, recently reported its first incursion of ASF. This study aimed to describe the distribution of pigs and pig farms in North Macedonia, and to characterize the live pig movement network. Network analyses on movement data from 2017 to 2019 were performed for each year separately, and consistently described weakly connected components with a few primary hubs that most nodes shipped to. In 2019, the network demonstrated a marked decrease in betweenness and increase in communities. Most shipments occurred within 50 km, with movements <6 km being the most common (22.5%). Nodes with the highest indegree and outdegree were consistent across years, despite a large turnover among smallholder farms. Movements to slaughterhouses predominated (85.6%), with movements between farms (5.4%) and movements to market (5.8%) playing a lesser role. This description of North Macedonia's live pig movement network should enable implementation of more efficient and cost-effective mitigation efforts strategies in country, and inform targeted educational outreach, and provide data for future disease modeling, in the region.

Molecular Evolution of Porcine Reproductive and Respiratory Syndrome Virus Field Strains from Two Swine Production Systems in the Midwestern United States from 2001 to 2020

Porcine reproductive and respiratory syndrome virus (PRRSV) poses an extensive economic threat to the United States swine industry. The high degree of PRRSV genetic and antigenic variability challenges existing vaccination programs. We evaluated the ORF5 sequence of 1,931 PRRSV-2 strains detected from >300 farms managed by two pork production systems in the midwestern United States from 2001 to 2020 to assess the genetic diversity and molecular characteristics of heterologous PRRSV-2 strains. Phylogenetic analysis was performed on ORF5 sequences and classified using the global PRRSV classification system. N-glycosylation and the global and local selection pressure in the putative GP5 encoded by ORF5 were estimated. The PRRSV-2 sequences were classified into lineage 5 (L5; n = 438[22.7%]) or lineage 1 (L1; n = 1,493[77.3%]). The L1 strains belonged to one of three subclades: L1A (n = 1,225[63.4%]), L1B (n = 69[3.6%]), and L1C/D (n = 199[10.3%]). 10 N-glycosylation sites were predicted, and positions N44 and N51 were detected in most GP5 sequences (n = 1,801[93.3%]). Clade-specific N-glycosylation sites were observed: 57th in L1A, 33rd in L1B, 30th and 34th in L1C/D, and 30th and 33rd in L5. We identified nine and 19 sites in GP5 under significant positive selection in L5 and L1, respectively. The 13th, 151st, and 200th positive selection sites were exclusive to L5. Heterogeneity of N-glycosylation and positive selection sites may contribute to varying the evolutionary processes of PRRSV-2 strains circulating in these swine production systems. L1A and L5 strains denoted excellence in adaptation to the current swine population by their extensive positive selection sites with higher site-specific selection pressure. IMPORTANCE Porcine reproductive and respiratory syndrome virus (PRRSV) is known for its high genetic and antigenic variability. In this study, we evaluated the ORF5 sequences of PRRSV-2 strains circulating in two swine production systems in the midwestern United States from 2001 to 2020. All the field strains were classified into four major groups based on genetic relatedness, where one group is closely related to the Ingelvac PRRS MLV strain. Here, we systematically compared differences in the ORF5 polymorphisms, N-glycosylation sites, and local and global evolutionary dynamics between different groups. Sites 44 and 51 were common for N-glycosylation in most amino acid sequences (n = 1,801, 93.3%). We identified that the L5 sequences had more positive selection pressure compared to the L1 strains. Our findings will provide valuable insights into the evolutionary mechanisms of PRRSV-2 and these molecular changes may lead to suboptimal effectiveness of Ingelvac PRRS MLV vaccine.

Network analysis of pig movements in Ecuador: Strengthening surveillance of classical swine fever

The analysis of domestic pig movements has become useful to understand the disease spread patterns and epidemiology, which facilitates the development of more effective animal diseases control strategies. The aim of this work was to analyse the static and spatial characteristics of the pig network, to identify its trading communities and to study the contribution of the network to the transmission of classical swine fever. In this regard, we used the pig movement records from the National veterinary service of Ecuador (2017-2019), using social network analysis and spatial analysis to construct a network with registered premises as nodes and their movements as edges. Furthermore, we also created a network of parishes as its nodes by aggregating their premises movements as edges. The annual network metrics showed an average diameter of 20.33, a number of neighbours of 2.61, a shortest path length of 4.39 and a clustering coefficient of 0.38 (small-world structure). The most frequent movements were to or from markets (55%). Backyard producers made up 89% of the network premises, and the top 2% of parishes (highest degree) contributed to 50% of the movements. The highest frequencies of movements between parishes were in the centre of the country, while the highest frequency of movements to abattoirs was in the south-west. Finally, the pattern of CSF disease outbreaks within the Ecuador network was likely the result of network transmission processes. In conclusion, our results represented the first exploratory analysis of domestic pig movements at premise and parish levels. The surveillance system could consider these results to improve its procedures and update the disease control and management policy, and allow the implementation of targeted or risk-based surveillance. This article is protected by copyright. All rights reserved.

A Molecular and Epidemiological Description of a Severe Porcine Reproductive and Respiratory Syndrome Outbreak in a Commercial Swine Production System in Russia

Porcine reproductive and respiratory syndrome (PRRS) is an economically devastating disease of swine in many parts of the world. Porcine reproductive and respiratory syndrome virus (PRRSV) type 1 is endemic in Europe, and prevalence of the subtypes differ spatially. In this study, we investigated a severe PRRS outbreak reported in 30 farms located in eastern Russia that belong to a large swine production company in the region that was also experiencing a pseudorabies outbreak in the system. Data included 28 ORF5 sequences from samples across 18 of the 25 infected sites, reverse transcriptase real-time polymerase chain reaction (RT-qPCR) results from diagnostic testing, reports of clinical signs, and animal movement records. We observed that the outbreak was due to two distinct variants of wildtype PRRSV type 1 subtype 1 with an average genetic distance of 15%. Results suggest that the wildtype PRRSV variants were introduced into the region around 2019, before affecting this production system (i.e., sow farms, nurseries, and finisher farms). Clinical signs did not differ between the variants, but they did differ by stage of pig production. Biosecurity lapses, including movement of animals from infected farms contributed to disease spread.

Preventive effect of on-farm biosecurity practices against highly pathogenic avian influenza (HPAI) H5N6 infection on commercial layer farms in the Republic of Korea during the 2016-17 epidemic: A case-control study

Highly pathogenic avian influenza virus (HPAIv) H5N6 has destructive consequences on the global poultry production system. Recently, a growing number of layer farms have been heavily damaged from the HPAIv epidemic due to the increased virulence of the virus and the intensification of the production system. Therefore, stakeholders should implement effective preventive practices at the farm level that are aligned with contingency measures at the national level to minimize poultry losses. However, numerous biosecurity protocols for layer farm workers to follow have been developed, impeding efficient prevention and control. Furthermore, the effectiveness of biosecurity practices varies with the geographical condition and inter-farm contact structures. Hence, the objective of our study was to examine the preventive effect of five biosecurity actions commonly practiced at layer farms in the Republic of Korea against HPAIv H5N6: (i) fence installation around a farm, ii) rodent control inside a farm; iii) disinfection booth for visitors for disinfection protocols, iv) an anterior room in the sheds before entering the bird area and v) boots changes when moving between sheds in the same farm. We conducted a case-control study on 114 layer case farms and 129 layer control farms during the 2016-17 HPAI epidemic. The odds ratios for five on-farm biosecurity practices implemented in those study groups were estimated as a preventive effect on the HPAI infection with covariates, including seven geographical conditions and three network metrics using Bayesian hierarchical logistic regression and geographical location weighted logistic regression. The results showed that the use of a disinfection booth for personnel reduced the odds of HPAIv H5N6 infection (adjusted odds ratio [AOR] = 0.002, 95 % credible interval [CrI] = 0.00007 - 0.025) with relatively small spatial variation (minimum AOR - maximum AOR: 0.084-0.263). Changing boots between sheds on the same farm reduced the odds of HPAIv H5N6 infection (AOR = 0.160, 95 % CrI = 0.024-0.852) with relatively wide spatial variation (minimum AOR - maximum AOR = 0.270-0.688). Therefore, enhanced personnel biosecurity protocols at the farm of entry for layer farms is recommended to effectively prevent and respond to HPAIv H5N6 infection under different local condition. Our study provides an important message for layer farmers to effectively implement on-farm biosecurity actions against HPAIv H5N6 infection at their farms by setting priorities based on their spatial condition and network position.

Integrating animal movements with phylogeography to model the spread of PRRSV in the USA

Viral sequence data coupled with phylodynamic models have become instrumental in investigating the outbreaks of human and animal diseases, and the incorporation of the hypothesized drivers of pathogen spread can enhance the interpretation from phylodynamic inference. Integrating animal movement data with phylodynamics allows us to quantify the extent to which the spatial diffusion of a pathogen is influenced by animal movements and contrast the relative importance of different types of movements in shaping pathogen distribution. We combine animal movement, spatial, and environmental data in a Bayesian phylodynamic framework to explain the spatial diffusion and evolutionary trends of a rapidly spreading sub-lineage (denoted L1A) of porcine reproductive and respiratory syndrome virus (PRRSV) Type 2 from 2014 to 2017. PRRSV is the most important endemic pathogen affecting pigs in the USA, and this particular virulent sub-lineage emerged in 2014 and continues to be the dominant lineage in the US swine industry to date. Data included 984 open reading frame 5 (ORF5) PRRSV L1A sequences obtained from two production systems in a swine-dense production region (∼85,000 mi²) in the USA between 2014 and 2017. The study area was divided into sectors for which model covariates were summarized, and animal movement data between each sector were summarized by age class (wean: 3–4 weeks; feeder: 8–25 weeks; breeding: ≥21 weeks). We implemented a discrete-space phylogeographic generalized linear model using Bayesian evolutionary analysis by sampling trees (BEAST) to infer factors associated with variability in between-sector diffusion rates of PRRSV L1A. We found that between-sector spread was enhanced by the movement of feeder pigs, spatial adjacency of sectors, and farm density in the destination sector. The PRRSV L1A strain was introduced in the study area in early 2013, and genetic diversity and effective population size peaked in 2015 before fluctuating seasonally (peaking during the summer months). Our study underscores the importance of animal movements and shows, for the first time, that the movement of feeder pigs (8–25 weeks old) shaped the spatial patterns of PRRSV spread much more strongly than the movements of other age classes of pigs. The inclusion of movement data into phylodynamic models as done in this analysis may enhance our ability to identify crucial pathways of disease spread that can be targeted to mitigate the spatial spread of infectious human and animal pathogens.

Characterization and influencing factors of the pig movement network in Hunan Province, China

In terms of pig production in China, Hunan was the third largest province where the number of hogs accounted for 9.0 % of the national number of hogs in 2017. To propose the precise strategy for supervision of pig movements in Hunan Province, a weighted directed one-mode network was constructed using the data from the electronic animal health certificate platform in 2017. The nodes were designed as districts in Hunan and edges as flows of pig movement between districts. Social network analysis was used to analyse network characteristics and generalized linear models were performed to ascertain the socioeconomic factors that affect the pig movement network. During 2017, the pig movement network within the Hunan Province was composed of 122 nodes and 8562 directed connections, with a total of 510,973 shipments and 17,815,040 pigs moved. The network displayed a small-world topology, which had a higher clustering coefficient (0.4 vs. 0.1) and shorter average shortest path length (1.8 vs. 3.7) compared with equivalent random networks. The degree centrality positively correlated with closeness centrality (r = 0.99, P < 0.001) as well as betweenness centrality (r = 0.91, P < 0.001). After restricting the cross-regional pig movements in areas with the top 10 % of degree centrality, the number of pigs was reduced by nearly 50 % in the network, whereas the number of pigs was reduced by 94.0 % when movement restrictions were implemented in areas with the top 50 % of degree centrality. Observed network metrics showed an upward trend during the months of 2017, peaking in November and December. Generalized linear models showed that the size of the human population and per capita gross domestic product were the most important socioeconomic drivers of pig movements. The pig movement network in Hunan Province is a small-world network in which the introduction and spread of diseases may be quicker. More human, material, and financial resources should be allocated to areas with higher centrality. Swine movements were seasonal, and the inspection and quarantine work should be reinforced in the fourth quarter, especially in November and December. Pig movements were more active in areas with larger populations and advanced economy, and stricter supervision in these areas should be implemented. Our findings contribute to understanding the movement of pigs and the associated influencing factors in a big pig producing province in China, and the supervision strategies proposed in this study can be extended to other regions in China if proved to be viable.

Temporal stability of swine movement networks in the U.S

As a consequence of multi-site pig production practiced in North America, frequent and widespread animal movements create extensive networks of interaction between farms. Social network analysis (SNA) has been used to understand disease transmission risks within these complex and dynamic production ecosystems and is particularly relevant for designing risk-based surveillance and control strategies targeting highly connected farms. However, inferences from SNA and the effectiveness of targeted strategies may be influenced by temporal changes in network structure. Since farm movements represent a temporally dynamic network, it is also unclear how many months of data are required to gain an accurate picture of an individual farm's connectivity pattern and the overall network structure. The extent to which shipments between two specific farms are repeated (i.e., "loyalty" of farm contacts) can influence the rate at which the structure of a network changes over time, which may influence disease dynamics. In this study, we aimed to describe temporal stability and loyalty patterns of pig movement networks in the U.S. swine industry. We analyzed a total of 282,807 animal movements among 2724 farms belonging to two production systems between 2014 and 2017. Loyalty trends were largely driven by contacts between sow farms and nurseries and between nurseries and finisher farms; mean loyalty (percent of contacts that were repeated at least once within a 52-week interval) of farm contacts was 51-60 % for farm contacts involving weaned pigs, and 12-22% for contacts involving feeder pigs. A cyclic pattern was observed for both weaned and feeder pig movements, with episodes of increased loyalty observed at intervals of 8 and 17-20 weeks, respectively. Network stability was achieved when six months of data were aggregated, and only small shifts in node-level and global network metrics were observed when adding more data. This stability is relevant for designing targeted surveillance programs for disease management, given that movements summarized over too short a period may lead to stochastic swings in network metrics. A temporal resolution of six months would be reliable for the identification of potential super-spreaders in a network for targeted intervention and disease control. The temporal stability observed in these networks suggests that identifying highly connected farms in retrospective network data (up to 24 months) is reliable for future planning, albeit with reduced effectiveness.

Dynamic network connectivity influences the spread of a sub-lineage of porcine reproductive and respiratory syndrome virus

Swine production in the United States is characterized by dynamic farm contacts through animal movements; such movements shape the risk of disease occurrence on farms. Pig movements have been linked to the spread of a virulent porcine reproductive and respiratory syndrome virus (PRRSV), RFLP type 1-7-4, herein denoted as phylogenetic sub-lineage 1A [L1A]. This study aimed to quantify the contribution of pig movements to the risk of L1A occurrence on farms in the United States. Farms were defined as L1A-positive in a given 6-month period if at least one L1A sequence was recovered from the farm. Temporal network autocorrelation modelling was performed using data on animal movements and 1,761 PRRSV ORF5 sequences linked to 494 farms from a dense pig production area in the United States between 2014 and 2017. A farm's current and past exposure to L1A and other PRRSV variants was assessed through its primary and secondary contacts in the animal movement network. Primary and secondary contacts with an L1A-positive farm increased the likelihood of L1A occurrence on a farm by 19% (p = .04) and 23% (p = .03), respectively. While the risk posed by primary contacts with PRRS-positive farms is unsurprising, the observation that secondary contacts also increase the likelihood of infection is novel. Risk of L1A occurrence on a farm also increased by 3.0% (p = .01) for every additional outgoing shipment, possibly due to biosecurity breaches during loading and transporting pigs from the farm. Finally, use of vaccines or field virus inoculation on sow farms one year prior reduced the risk of L1A occurrence in downstream farms by 36% (p = .04), suggesting that control measures that reduce viral circulation and enhance immunological protection in sow farms have a carry-over effect on L1A occurrence in downstream farms. Therefore, coordinated disease management interventions between farms connected via animal movements may be more effective than individual farm-based interventions.

Modelling the transmission and vaccination strategy for porcine reproductive and respiratory syndrome virus

Many aspects of the porcine reproductive and respiratory syndrome virus (PRRSV) between-farm transmission dynamics have been investigated, but uncertainty remains about the significance of farm type and different transmission routes on PRRSV spread. We developed a stochastic epidemiological model calibrated on weekly PRRSV outbreaks accounting for the population dynamics in different pig production phases, breeding herds, gilt development units, nurseries and finisher farms, of three hog producer companies. Our model accounted for indirect contacts by the close distance between farms (local transmission), between-farm animal movements (pig flow) and reinfection of sow farms (re-break). The fitted model was used to examine the effectiveness of vaccination strategies and complementary interventions such as enhanced PRRSV detection and vaccination delays and forecast the spatial distribution of PRRSV outbreak. The results of our analysis indicated that for sow farms, 59% of the simulated infections were related to local transmission (e.g. airborne, feed deliveries, shared equipment) whereas 36% and 5% were related to animal movements and re-break, respectively. For nursery farms, 80% of infections were related to animal movements and 20% to local transmission; while at finisher farms, it was split between local transmission and animal movements. Assuming that the current vaccines are 1% effective in mitigating between-farm PRRSV transmission, weaned pigs vaccination would reduce the incidence of PRRSV outbreaks by 3%, indeed under any scenario vaccination alone was insufficient for completely controlling PRRSV spread. Our results also showed that intensifying PRRSV detection and/or vaccination pigs at placement increased the effectiveness of all simulated vaccination strategies. Our model reproduced the incidence and PRRSV spatial distribution; therefore, this model could also be used to map current and future farms at-risk. Finally, this model could be a useful tool for veterinarians, allowing them to identify the effect of transmission routes and different vaccination interventions to control PRRSV spread.

The between-farm transmission dynamics of porcine epidemic diarrhoea virus: A short-term forecast modelling comparison and the effectiveness of control strategies

A limited understanding of the transmission dynamics of swine disease is a significant obstacle to prevent and control disease spread. Therefore, understanding between-farm transmission dynamics is crucial to developing disease forecasting systems to predict outbreaks that would allow the swine industry to tailor control strategies. Our objective was to forecast weekly porcine epidemic diarrhoea virus (PEDV) outbreaks by generating maps to identify current and future PEDV high-risk areas, and simulating the impact of control measures. Three epidemiological transmission models were developed and compared: a novel epidemiological modelling framework was developed specifically to model disease spread in swine populations, PigSpread, and two models built on previously developed ecosystems, SimInf (a stochastic disease spread simulations) and PoPS (Pest or Pathogen Spread). The models were calibrated on true weekly PEDV outbreaks from three spatially related swine production companies. Prediction accuracy across models was compared using the receiver operating characteristic area under the curve (AUC). Model outputs had a general agreement with observed outbreaks throughout the study period. PoPS had an AUC of 0.80, followed by PigSpread with 0.71, and SimInf had the lowest at 0.59. Our analysis estimates that the combined strategies of herd closure, controlled exposure of gilts to live viruses (feedback) and on-farm biosecurity reinforcement reduced the number of outbreaks. On average, 76% to 89% reduction was seen in sow farms, while in gilt development units (GDU) was between 33% to 61% when deployed to sow and GDU farms located in probabilistic high-risk areas. Our multi-model forecasting approach can be used to prioritize surveillance and intervention strategies for PEDV and other diseases potentially leading to more resilient and healthier pig production systems.

Impact of oxytetracycline on anaerobic wastewater treatment and mitigation using enhanced hydrolysis pretreatment

In this study, two parallel-operated up-flow anaerobic sludge bed reactors, one used to treat synthetic wastewater spiked with oxytetracycline and the other used to treat the same wastewater after enhanced hydrolysis, were used to evaluate the impact of oxytetracycline on anaerobic digestion and resistance development and the efficacy of enhanced hydrolysis pretreatment on the elimination of adverse effects. The reactors were operated under a constant organic-loading rate (10 g/L/d) with increasing oxytetracycline doses (0 mg/L to 200 mg/L) over a period of 15 months. For the reactor without pretreatment, the chemical oxygen demand removal reached up to 89.5%%at oxytetracycline doses ranging from 0 mg/L to 100 mg/L, which collapsed at higher oxytetracycline doses. Miseq sequencing showed that a diverse hydrolysis/fermentation/acetogenesis bacterial community was maintained as the oxytetracycline dose was increased from 0 mg/L to 100 mg/L, while extreme dominance of Macellibacteroides (65.70%%- 71.56%) was found to occur at higher oxytetracycline doses. The total abundance of antibiotic resistance genes increased from 1.3 × 10^-1 copies per cell to 2.6 × 10^-1 copies per cell with increasing oxytetracycline dose from 0 mg/L to 5 mg/L, remained unchanged at oxytetracycline doses ranging from 25 mg/L to 100 mg/L, and then increased to 4.8 × 10^-1 copies per cell and 1.3 copies per cell at oxytetracycline doses of 150 mg/L and 200 mg/L, respectively. Multidrug resistance developed in response to oxytetracycline treatment at 200 mg/L. Poor chemical oxygen demand removal and a marked enrichment in antibiotic resistance genes was validated using a full-scale up-flow anaerobic sludge bed system fed with an influent oxytetracycline concentration of approximately 200 mg/L. For the reactor treating wastewater pretreated with enhanced hydrolysis (85 °C for 6 h), the chemical oxygen demand removal rate and antibiotic resistance genes level over the whole oxytetracycline dose range were found to be similar to those achieved with zero oxytetracycline treatment. These results demonstrated that the control of conventional pollutants and ARGs could be achieved simultaneously in the UASB reactor by employing enhanced hydrolysis pretreatment.

Fine-scale tracking of wild waterfowl and their impact on highly pathogenic avian influenza outbreaks in the Republic of Korea, 2014-2015

Wild migratory waterfowl are considered one of the most important reservoirs and long-distance carriers of highly pathogenic avian influenza (HPAI). Our study aimed to explore the spatial and temporal characteristics of wild migratory waterfowl’s wintering habitat in the Republic of Korea (ROK) and to evaluate the impact of these habitats on the risk of HPAI outbreaks in commercial poultry farms. The habitat use of 344 wild migratory waterfowl over four migration cycles was estimated based on tracking records. The association of habitat use with HPAI H5N8 outbreaks in poultry farms was evaluated using a multilevel logistic regression model. We found that a poultry farm within a wild waterfowl habitat had a 3–8 times higher risk of HPAI outbreak than poultry farms located outside of the habitat. The range of wild waterfowl habitats increased during autumn migration, and was associated with the epidemic peak of HPAI outbreaks on domestic poultry farms in the ROK. Our findings provide a better understanding of the dynamics of HPAI infection in the wildlife–domestic poultry interface and may help to establish early detection, and cost-effective preventive measures.

Data-Driven Network Modeling as a Framework to Evaluate the Transmission of Piscine Myocarditis Virus (PMCV) in the Irish Farmed Atlantic Salmon Population and the Impact of Different Mitigation Measures

Cardiomyopathy syndrome (CMS) is a severe cardiac disease of Atlantic salmon caused by the piscine myocarditis virus (PMCV), which was first reported in Ireland in 2012. In this paper, we describe the use of data-driven network modeling as a framework to evaluate the transmission of PMCV in the Irish farmed Atlantic salmon population and the impact of different mitigation measures. Input data included live fish movement data from 2009 to 2017, population dynamics events and the spatial location of the farms. With these inputs, we fitted a network-based stochastic infection spread model. After assumed initial introduction of the agent in 2009, our results indicate that it took 5 years to reach a between-farm prevalence of 100% in late 2014, with older fish being most affected. Local spread accounted for only a small proportion of new infections, being more important for sustained infection in a given area. Spread via movement of subclinically infected fish was most important for explaining the observed countrywide spread of the agent. Of the targeted intervention strategies evaluated, the most effective were those that target those fish farms in Ireland that can be considered the most connected, based on the number of farm-to-farm linkages in a specific time period through outward fish movements. The application of these interventions in a proactive way (before the first reported outbreak of the disease in 2012), assuming an active testing of fish consignments to and from the top 8 ranked farms in terms of outward fish movement, would have yielded the most protection for the Irish salmon farming industry. Using this approach, the between-farm PMCV prevalence never exceeded 20% throughout the simulation time (as opposed to the simulated 100% when no interventions are applied). We argue that the Irish salmon farming industry would benefit from this approach in the future, as it would help in early detection and prevention of the spread of viral agents currently exotic to the country.

Application of network analysis and cluster analysis for better prevention and control of swine diseases in Argentina

RATIONALE/BACKGROUND

Though much smaller than the bovine industry, the porcine sector in Argentina involves a large number of farms and represents a significant economic sector. In recent years Argentina has implemented a national registry of swine movements amongst other measures, in an effort to control and eventually eradicate endemic Aujesky's disease. Such information can prove valuable in assessing the risk of transmission between farms for endemic diseases but also for other diseases at risk of emergence.

METHODS

Shipment data from 2011 to 2016 were analyzed in an effort to define strategic locations and times at which control and surveillance efforts should be focused to provide cost-effective interventions. Social network analysis (SNA) was used to characterize the network as a whole and at the individual farm and market level to help identify important nodes. Spatio-temporal trends of pig movements were also analyzed. Finally, in an attempt to classify farms and markets in different groups based on their SNA metrics, we used factor analysis for mixed data (FAMD) and hierarchical clustering.

RESULTS

The network involved approximate 136,000 shipments for a total of 6 million pigs. Over 350 markets and 17,800 production units participated in shipments with another 83,500 not participating. Temporal data of shipments and network metrics showed peaks in shipments in September and October. Most shipments where within provinces, with Buenos Aires, Cordoba and Santa Fe concentrating 61% of shipments. Network analysis showed that markets are involved in relatively few shipments but hold strategic positions with much higher betweenness compared to farms. Hierarchical clustering yielded four groups based on SNA metrics and node characteristics which can be broadly described as: 1. small and backyard farms; 2. industrial farms; 3. markets; and 4. a single outlying market with extreme centrality values.

CONCLUSION

Characterizing the network structure and spatio-temporal characteristics of Argentine swine shipments provides valuable information that can guide targeted and more cost-effective surveillance and control programs. We located key nodes where efforts should be prioritized. Pig network characteristics and patterns can be used to create dynamic disease transmission models, which can both be used in assessing the impact of emerging diseases and guiding efforts to eradicate endemic ones.

Network Analysis of Swine Shipments in China: The First Step to Inform Disease Surveillance and Risk Mitigation Strategies

China's pork industry has been dramatically changing in the last few years. Pork imports are increasing, and small-scale farms are being consolidated into large-scale multi-site facilities. These industry changes increase the need for traceability and science-based decisions around disease monitoring, surveillance, risk mitigation, and outbreak response. This study evaluated the network structure and dynamics of a typical large-scale multi-site swine facility in China, as well as the implications for disease spread using network-based metrics. Forward reachability paths were used to demonstrate the extent of epidemic spread under variable site and temporal disease introductions. Swine movements were found to be seasonal, with more movements at the beginning of the year, and fewer movements of larger pigs later in the year. The network was highly egocentric, with those farms within the evaluated production system demonstrating high connectivity. Those farms which would contribute the highest epidemic potential were identified. Among these, different farms contributed to higher expected epidemic spread at different times of the year. Using these approaches, increased availability of swine movement networks in China could help to identify priority locations for surveillance and risk mitigation for both endemic problems and transboundary diseases such as the recently introduced, and rapidly spreading, African swine fever virus.

Contrasting animal movement and spatial connectivity networks in shaping transmission pathways of a genetically diverse virus

Analyses of livestock movement networks has become key to understanding an industry's vulnerability to infectious disease spread and for identifying farms that play disproportionate roles in pathogen dissemination. In addition to animal movements, many pathogens can spread between farms via mechanisms mediated by spatial proximity. Heterogeneities in contact patterns based on spatial proximity are less commonly considered in network studies, and studies that jointly consider spatial connectivity and animal movement are rare. The objective of this study was to determine the extent to which movement versus spatial proximity networks determine the distribution of an economically important endemic virus, porcine reproductive and respiratory syndrome virus (PRRSV), within a swine-dense region of the U.S. PRRSV can be classified into numerous phylogenetic lineages. Such data can be used to better resolve between-farm infection chains and elucidate types of contact most associated with transmission. Here, we construct movement and spatial proximity networks; farms within the networks were classified as cases if a given PRRSV lineage had been recovered at least once in a year for each of three years analyzed. We evaluated six lineages and sub-lineages across three years, and evaluated the epidemiological relevance of each network by applying network k-tests to statistically evaluate whether the pattern of case occurrence within the network was consistent with transmission via network linkages. Our results indicated that animal movements, not local area spread, play a dominant role in shaping transmission pathways, though there were differences amongst lineages. The median number of case farms inter-linked via animal movements was approximately 4.1x higher than random expectations (range: 1.7-13.7; p < 0.05, network k-test), whereas this measure was only 2.7x higher than random expectations for farms linked via spatial proximity (range: 1.3-5.4; p < 0.05, network k-test). For spatial proximity networks, contact based on proximities of <5 km appeared to have greater epidemiological relevance than longer distances, likely related to diminishing probabilities of local area spread at greater distances. However, the greater overall levels of connectivity of the spatial network compared to the movement network highlights the vulnerability of pig populations to widespread transmission via this route. By combining genetic data with network analysis, this research advances our understanding of dynamics of between-farm spread of PRRSV, helps establish the relative importance of transmission via animal movements versus local area spread, and highlights the potential for targeted control strategies based upon heterogeneities in network connectivity.

Pig trade networks through live pig markets in Guangdong Province, China

This study used social network analysis to investigate the indirect contact network between counties through the movement of live pigs through four wholesale live pig markets in Guangdong Province, China. All 14,118 trade records for January and June 2016 were collected from the markets and the patterns of pig trade in these markets analysed. Maps were developed to show the movement pathways. Evaluating the network between source counties was the primary objective of this study. A 1‐mode network was developed. Characteristics of the trading network were explored, and the degree, betweenness and closeness were calculated for each source county. Models were developed to compare the impacts of different disease control strategies on the potential magnitude of an epidemic spreading through this network. The results show that pigs from 151 counties were delivered to the four wholesale live pig markets in January and/or June 2016. More batches (truckloads of pigs sourced from one or more piggeries) were traded in these markets in January (8,001) than in June 2016 (6,117). The pigs were predominantly sourced from counties inside Guangdong Province (90%), along with counties in Hunan, Guangxi, Jiangxi, Fujian and Henan provinces. The major source counties (46 in total) contributed 94% of the total batches during the two‐month study period. Pigs were sourced from piggeries located 10 to 1,417 km from the markets. The distribution of the nodes' degrees in both January and June indicates a free‐scale network property, and the network in January had a higher clustering coefficient (0.54 vs. 0.39) and a shorter average pathway length (1.91 vs. 2.06) than that in June. The most connected counties of the network were in the central, northern and western regions of Guangdong Province. Compared with randomly removing counties from the network, eliminating counties with higher betweenness, degree or closeness resulted in a greater reduction of the magnitude of a potential epidemic. The findings of this study can be used to inform targeted control interventions for disease spread through this live pig market trade network in south China.

An investigation of transportation practices in an Ontario swine system using descriptive network analysis

The objectives of this research were to describe the contact structure of transportation vehicles and swine facilities in an Ontario swine production system, and to assess their potential contribution to possible disease transmission over different time periods. A years’ worth of data (2015) was obtained from a large swine production and data management company located in Ontario, Canada. There was a total of 155 different transportation vehicles, and 220 different farms within the study population. Two-mode networks were constructed for 1-,3-, and 7-day time periods over the entire year and were analyzed. Trends in the size of the maximum weak component and outgoing contact chain over discrete time periods were investigated using linear regression. Additionally, the number of different types of facilities with betweenness >0 and in/out degree>0 were analyzed using Poisson regression. Maximum weekly outgoing contact chain (MOCC_w) contained between 2.1% and 7.1% of the study population. This suggests a potential maximum of disease spread within this population if the disease was detected within one week. Frequency of node types within MOCC_w showed considerable variability; although nursery sites were relatively most frequent. The regression analysis of several node and network level statistics indicated a potential peak time of connectivity during the summer months and warrants further confirmation and investigation. The inclusion of transportation vehicles contributed to the linear increase in the maximum weekly weak component (MWC_w) size over time. This finding in combination with constant population dynamics, may have been driven by the differential utilization of trucks over time. Despite known limitations of maximum weak components as an estimator of possible outbreaks, this finding suggests that transportation vehicles should be included, when possible and relevant, in the evaluation of contacts between farms.

Network analysis of pig movements in Argentina: Identification of key farms in the spread of infectious diseases and their biosecurity levels

This study uses network analysis to evaluate how swine movements in Argentina could contribute to disease spread. Movement data for the 2014-2017 period were obtained from Argentina's online livestock traceability registry and categorized as follows: animals of high genetic value sent to other farms, animals to or from markets, animals sent to finisher operations and slaughterhouse. A network analysis was carried out considering the first three movement types. First, descriptive, centrality and cohesion measures were calculated for each movement type and year. Next, to determine whether networks had a small-world topology, these were compared with the results from random Erdös-Rényi network simulations. Then, the basic reproductive number (R₀ ) of the genetic network, the group of farms with higher potential for disease spread standing at the top of the production chain, was calculated to identify farms acting as super-spreaders. Finally, their external biosecurity scores were evaluated. The genetic network in Argentina presented a scale-free and small-world topology. Thus, we estimate that disease spread would be fast, preferably to highly connected nodes and with little chances of being contained. Throughout the study, 31 farms were identified as super-spreaders in the genetic network for all years, while other 55 were super-spreaders at least once, from an average of 1,613 farms per year. Interestingly, removal of less than 5% of higher degree and betweenness farms resulted in a >90% reduction of R₀ indicating that few farms have a key role in disease spread. When biosecurity scores of the most relevant super-spreaders were examined, it was evident that many were at risk of introducing and disseminating new pathogens across the whole of Argentina's pig production network. These results highlight the usefulness of establishing targeted surveillance and intervention programmes, emphasizing the need for better biosecurity scores in Argentinean swine production units, especially in super-spreader farms.

Network analysis of swine movements in a multi-site pig production system in Iowa, USA

Pig production in the United States is based on multi-site systems in which pigs are transported between farms after the conclusion of each particular production phase. Although ground transportation is a critical component of the pork supply chain, it might constitute a potential route of infectious disease dissemination. Here, we used a time series network analysis to: (1) describe pig movement flow in a multi-site production system in Iowa, USA, (2) conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and (3) assess the potential impact of each farm type on disease dissemination across the system. Movement reports from 2014-2016 were provided by Iowa Select Farms, Iowa Fall, IA. A total of 76,566 shipments across sites was analyzed, and time series network analyses with temporal resolution of 1, 3, 6, 12, and 36 months were considered. The general topological properties of networks with resolution of 1, 3, 6, and 12 months were compared with the whole period static network (36 months) and included the following features: number of nodes and edges, degree assortativity, density, average path length, diameter, clustering coefficients, giant strongly connected component, giant weakly connected component, giant in component, and giant out component. Small-world and scale-free topologies, centrality parameters, and percolation analysis were investigated for the networks with 1-month window. Networks' robustness to interventions was assessed by using the Basic Reproduction Number (R₀). Centrality parameters indicate that gilt development units (GDU), nursery, and sow farms have more central role in the pig production hierarchical structure. Therefore, they are potentially major factors of introduction and spread of diseases over the system. Wean-to-finishing and finishing sites displayed high in-degree values, indicating that they are more susceptible to be infected. Percolation analysis combined with general properties (i.e. heavy-tailed distributions and degree disassortative) suggested that networks with 1-month time resolution were highly responsive to interventions. Furthermore, the characteristics of a disease should have strong implications in the biosecurity practices across production sites. For instance, biosecurity practices should be focused on sow farms for highly contagious disease (e.g., foot and mouth disease), while it should target nursery sites in the case of a less contagious diseases (i.e. mycobacterial infections). Understanding the patterns of swine movements is crucial for the swine industry decision-making in the case of an epidemic, as well as to design cost-effective approaches to monitor, prevent, control and eradicate infectious diseases in multi-site systems.

Modeling Epidemics in Seed Systems and Landscapes To Guide Management Strategies: The Case of Sweet Potato in Northern Uganda

Seed systems are critical for deployment of improved varieties but also can serve as major conduits for the spread of seedborne pathogens. As in many other epidemic systems, epidemic risk in seed systems often depends on the structure of networks of trade, social interactions, and landscape connectivity. In a case study, we evaluated the structure of an informal sweet potato seed system in the Gulu region of northern Uganda for its vulnerability to the spread of emerging epidemics and its utility for disseminating improved varieties. Seed transaction data were collected by surveying vine sellers weekly during the 2014 growing season. We combined data from these observed seed transactions with estimated dispersal risk based on village-to-village proximity to create a multilayer network or "supranetwork." Both the inverse power law function and negative exponential function, common models for dispersal kernels, were evaluated in a sensitivity analysis/uncertainty quantification across a range of parameters chosen to represent spread based on proximity in the landscape. In a set of simulation experiments, we modeled the introduction of a novel pathogen and evaluated the influence of spread parameters on the selection of villages for surveillance and management. We found that the starting position in the network was critical for epidemic progress and final epidemic outcomes, largely driven by node out-degree. The efficacy of node centrality measures was evaluated for utility in identifying villages in the network to manage and limit disease spread. Node degree often performed as well as other, more complicated centrality measures for the networks where village-to-village spread was modeled by the inverse power law, whereas betweenness centrality was often more effective for negative exponential dispersal. This analysis framework can be applied to provide recommendations for a wide variety of seed systems.[Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Estimation of swine movement network at farm level in the US from the Census of Agriculture data

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.

Spatio-temporal patterns and characteristics of swine shipments in the U.S. based on Interstate Certificates of Veterinary Inspection

Domestic swine production in the United States is a critical economic and food security industry, yet there is currently no large-scale quantitative assessment of swine shipments available to support risk assessments. In this study, we provide a national-level characterization of the swine industry by quantifying the demographic (i.e. age, sex) patterns, spatio-temporal patterns, and the production diversity within swine shipments. We characterize annual networks of swine shipments using a 30% stratified sample of Interstate Certificates of Veterinary Inspection (ICVI), which are required for the interstate movement of agricultural animals. We used ICVIs in 2010 and 2011 from eight states that represent 36% of swine operations and 63% of the U.S. swine industry. Our analyses reflect an integrated and spatially structured industry with high levels of spatial heterogeneity. Most shipments carried young swine for feeding or breeding purposes and carried a median of 330 head (range: 1–6,500). Geographically, most shipments went to and were shipped from Iowa, Minnesota, and Nebraska. This work, therefore, suggests that although the swine industry is variable in terms of its size and type of swine, counties in states historically known for breeding and feeding operations are consistently more central to the shipment network.

Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods

The spread of pathogens in swine populations is in part determined by movements of animals between farms. However, understanding additional characteristics that predict disease outbreaks and uncovering landscape factors related to between-farm spread are crucial steps toward risk mitigation. This study integrates animal movements with environmental risk factors to identify the occurrence of porcine epidemic diarrhea virus (PEDV) outbreaks. Using weekly farm-level incidence data from 332 sow farms, we applied machine-learning algorithms to quantify associations between risk factors and PEDV outbreaks with the ultimate goal of training predictive models and to identify the most important factors associated with PEDV occurrence. Our best algorithm was able to correctly predict whether an outbreak occurred during one-week periods with >80% accuracy. The most important predictors included pig movements into neighboring farms. Other important neighborhood attributes included hog density, environmental and weather factors such as vegetation, wind speed, temperature, and precipitation, and topographical features such as slope. Our neighborhood-based approach allowed us to simultaneously capture disease risks associated with long-distance animal movement as well as local spatial dynamics. The model presented here forms the foundation for near real-time disease mapping and will advance disease surveillance and control for endemic swine pathogens in the United States.

Characterization of swine movements in the United States and implications for disease control

Understanding between-farm movement patterns is an essential component in developing effective surveillance and control programs in livestock populations. Quantitative knowledge on movement patterns is particularly important for the commercial swine industry, in which large numbers of pigs are frequently moved between farms. Here, we described the annual movement patterns between swine farms in three production systems of the United States and identified farms that may be targeted to increase the efficacy of infectious disease control strategies. Research results revealed a high amount of variability in movement patterns across production systems, indicating that quantities captured from one production system and applied to another may lead to invalid estimations of disease spread. Furthermore, we showed that targeting farms based on their mean infection potential, a metric that captured the temporal sequence of movements, substantially reduced the potential for transmission of an infectious pathogen in the contact network and performed consistently well across production systems. Specifically, we found that by targeting farms based on their mean infection potential, we could reduce the potential spread of an infectious pathogen by 80% when removing approximately 25% of farms in each of the production systems. Whereas other metrics, such as degree, required 26-35% of farms to be removed in two of the production systems to reach the same outcome; this outcome was not achievable in one of the production systems. Our results demonstrate the importance of fine-scale temporal movement data and the need for in-depth understanding of the contact structure in developing more efficient disease surveillance and response strategies in swine production systems.

Evaluation of the impact of live pig trade network, vaccination coverage and socio-economic factors in the classical swine fever eradication program in Peru

Classical swine fever (CSF) is a viral infectious disease of swine with significant economic impact in the affected countries due to the limitation of trade, culling of infected animals and production losses. In Latin America, CSF is endemic in several countries including Ecuador, Bolivia, Brazil and Peru. Since 2010, the National Veterinary Services of Peru have been working to better control and eradicate the disease with an intensive vaccination program. The aim of this study was to evaluate the effectiveness of the vaccination program and determine which factors are still contributing to the persistence of the disease in certain regions of Peru. We integrated the data from the vaccination campaign, the live pig movement network and other socioeconomic indicators into a multilevel logistic regression model to evaluate their association with CSF occurrence at district level. The results revealed that high vaccination coverage significantly reduces the risk of CSF occurrence (OR = 0.07), supporting the effectiveness of the vaccination program. Districts belonging to large and medium pig trade network communities (as identified with walktrap algorithm) had higher probability to CSF occurrence (OR = 2.83 and OR = 5.83, respectively). The human development index (HDI) and the presence of a slaughterhouse in the district was also significantly associated with an increased likelihood of CSF occurrence (OR = 1.52 and OR = 3.25, respectively). Districts receiving a high proportion of the movements from districts that were infected in the previous year were also at higher risk of CSF occurrence (OR = 3.30). These results should be useful to guide the prioritization of vaccination strategies and may help to design other intervention strategies (e.g., target education, movement restrictions, etc.) in high-risk areas to more rapidly advance in the eradication of CSF in Peru.

The implementation of cattle market closure strategies to mitigate the foot-and-mouth disease epidemics: A contact modeling approach

Foot-and-mouth disease (FMD) is one of the most endemic diseases in livestock worldwide. The disease occurrence generally results in a huge economic impact. The virus may distribute across countries or even continents along the contact network of animal movements. The present study, therefore, aimed to explore a cattle movement network originated in Tak, a Thailand-Myanmar bordered province and to demonstrate how FMDV spread among the nodes of market, source and destination. Subsequently, we examined the effectiveness of market closure intervention. The market-market (M-M) network was constructed to highlight the inter-market connections and the FMDV was modeled to spread along the trade chain. Four market closure scenarios based on rapidness and duration of implementation were examined. Our results indicate that two of the three major markets located in the province were highly connected and a strongly connected component was identified. The intra-provincial animal movements, which were currently overlooked, should be moved into sights as most of the high-risk sources for FMD epidemics were recognized in a close proximity to the cattle markets. Simultaneously, remote destinations across the country were identified. The inter-provincial animal movement control must be strengthened once FMD outbreak is notified. Based on our simulations, closing markets with low inter-market connectivity may not prevent the spread of FMDV. A selective market closure strategy targeting highly connected markets together with cattle trader tracking system was an alternative approach. However, socio-economic consequences regarding this intervention must be considered.

A descriptive analysis of swine movements in Ontario (Canada) as a contributor to disease spread

In recent times, considerable efforts have been made to develop infrastructure and processes of tracing livestock movements. One of common use of this type of data is to assess the potential for spread of infections in source populations. The objectives of this research were to describe Ontario pig movements in 2015, and to understand the potential for disease transmission through animal movement on a weekly and yearly basis. Swine shipments from January to December 2015 represented 224 production facilities and a total of 5398 unique animal movements. This one-mode directed network of animal movements was then analyzed using common descriptive network measures. The maximum yearly (_y) weak component (WC_y) size and maximum weekly (_w) weak component size (WC_w) was 224 facilities, and 83 facilities, respectively. The maximum WC_w did not change significantly (p > 0.05) over time. The maximum strong component (SC) consisted of two facilities both on a weekly, and on a yearly basis. The size of the maximum ingoing contact chain on a yearly basis (ICC_y) was 173 nodes with one abattoir as the end point, and the maximum ICC_w consisted of 53 nodes. The size of the maximum outgoing contact chain (OCC_y) contained 79 nodes, with one sow herd as a starting point. The maximum OCC_w was 6 nodes. Regression models resulted in significant quadratic associations between weekly count of finisher facilities with betweenness >0 (p = 0.02) and weekly count of finisher facilities with in-degree and out-degree >0 (p = 0.01) and week number. Higher weekly counts of nursery and finisher facilities with betweenness >0 and in-degree and out-degree both >0 values occurred during summer months. All study facilities were connected when direction of animal movement was not taken into consideration in the yearly network. As such, yearly networks are potentially representative of infections with long incubation periods, subclinical infections, or endemic infections for which active control measures have not being taken. When the direction of animal movement was considered, such infection could still spread substantially and affect 35% of the study population (79/224). In the study population, finisher sites were proportionally and consistently most represented in WC_w (min = 51%, max = 78%), which reflects current Ontario herd demographics. However, abattoirs were over-represented when the number of facilities in the study population was taken into consideration. This, and the size of the maximum ICC_w both suggest that abattoirs could be, at least for some infectious diseases, suitable establishments for targeted sampling.

Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus

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The emergence of porcine epidemic diarrhea virus (PEDv) caused a major epidemic.

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We developed a model simulating the between-farm spread of PEDv.

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Probabilities of each transmission mode were calibrated to match observed dynamics.

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Transmission was mostly between neighboring farms or through pig movements.

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However, long-distance jumps were primarily due to contaminated fomites and feed.

Epidemiological models of the spread of pathogens in livestock populations primarily focus on direct contact between farms based on animal movement data, and in some cases, local spatial spread based on proximity between premises. The roles of other types of indirect contact among farms is rarely accounted for. In addition, data on animal movements is seldom available in the United States. However, the spread of porcine epidemic diarrhea virus (PEDv) in U.S. swine represents one of the best documented emergences of a highly infectious pathogen in the U.S. livestock industry, providing an opportunity to parameterize models of pathogen spread via direct and indirect transmission mechanisms in swine. Using observed data on pig movements during the initial phase of the PEDv epidemic, we developed a network-based and spatially explicit epidemiological model that simulates the spread of PEDv via both indirect and direct movement-related contact in order to answer unresolved questions concerning factors facilitating between-farm transmission. By modifying the likelihood of each transmission mechanism and fitting this model to observed epidemiological dynamics, our results suggest that between-farm transmission was primarily driven by direct mechanisms related to animal movement and indirect mechanisms related to local spatial spread based on geographic proximity. However, other forms of indirect transmission among farms, including contact via contaminated vehicles and feed, were responsible for high consequence transmission events resulting in the introduction of the virus into new geographic areas. This research is among the first reports of farm-level animal movements in the U.S. swine industry and, to our knowledge, represents the first epidemiological model of commercial U.S. swine using actual data on farm-level animal movement.

Modelling the economic efficiency of using different strategies to control Porcine Reproductive & Respiratory Syndrome at herd level

PRRS is among the diseases with the highest economic impact in pig production worldwide. Different strategies have been developed and applied to combat PRRS at farm level. The broad variety of available intervention strategies makes it difficult to decide on the most cost-efficient strategy for a given farm situation, as it depends on many farm-individual factors like disease severity, prices or farm structure. Aim of this study was to create a simulation tool to estimate the cost-efficiency of different control strategies at individual farm level. Baseline is a model that estimates the costs of PRRS, based on changes in health and productivity, in a specific farm setting (e.g. farm type, herd size, type of batch farrowing). The model evaluates different intervention scenarios: depopulation/repopulation (D/R), close & roll-over (C&R), mass vaccination of sows (MS), mass vaccination of sows and vaccination of piglets (MS + piglets), improvements in internal biosecurity (BSM), and combinations of vaccinations with BSM. Data on improvement in health and productivity parameters for each intervention were obtained through literature review and from expert opinions. The economic efficiency of the different strategies was assessed over 5 years through investment appraisals: the resulting expected value (EV) indicated the most cost-effective strategy. Calculations were performed for 5 example scenarios with varying farm type (farrow-to-finish - breeding herd), disease severity (slightly - moderately - severely affected) and PRRSV detection (yes - no). The assumed herd size was 1000 sows with farm and price structure as commonly found in Germany. In a moderately affected (moderate deviations in health and productivity parameters from what could be expected in an average negative herd), unstable farrow-to-finish herd, the most cost-efficient strategies according to their median EV were C&R (€1'126'807) and MS + piglets (€ 1'114'649). In a slightly affected farrow-to-finish herd, no virus detected, the highest median EV was for MS + piglets (€ 721'745) and MS (€ 664'111). Results indicate that the expected benefits of interventions and the most efficient strategy depend on the individual farm situation, e.g. disease severity. The model provides new insights regarding the cost-efficiency of various PRRSV intervention strategies at farm level. It is a valuable tool for farmers and veterinarians to estimate expected economic consequences of an intervention for a specific farm setting and thus enables a better informed decision.

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs

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A method is proposed to explore the role of pig movements on pathogen epidemiology.

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Pig farm centrality in the network is associated with higher HEV seroprevalence.

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Some local areas are more at risk for HEV due to incoming pig movements.

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Animal movements should be included in risk-based surveillance strategies.

Animal movements between farms are a major route of pathogen spread in the pig production sector. This study aimed to pair network analysis and epidemiological data in order to evaluate the impact of animal movements on pathogen prevalence in farms and assess the risk of local areas being exposed to diseases due to incoming movements. Our methodology was applied to hepatitis E virus (HEV), an emerging foodborne zoonotic agent of concern that is highly prevalent in pig farms. Firstly, the pig movement network in France (data recorded in 2013) and the results of a nation-wide seroprevalence study (data collected in 178 farms in 2009) were modelled and analysed. The link between network centrality measures of farms and HEV seroprevalence levels was explored using a generalised linear model. The in-degree and ingoing closeness of farms were found to be statistically associated with high HEV within-farm seroprevalence (p < 0.05). Secondly, the risk of a French département (i.e. French local administrative areas) being exposed to HEV was calculated by combining the distribution of farm-level HEV prevalence in source départements with the number of movements coming from those same départements. By doing so, the risk of exposure for départements was mapped, highlighting differences between geographical patterns of HEV prevalence and the risk of exposure to HEV. These results suggest that not only highly prevalent areas but also those having at-risk movements from infected areas should be monitored. Pathogen management and surveillance options in the pig production sector should therefore take animal movements into consideration, paving the way for the development of targeted and risk-based disease surveillance strategies.

Pig movements in France: Designing network models fitting the transmission route of pathogens

Pathogen spread between farms results from interaction between the epidemiological characteristics of infectious agents, such as transmission route, and the contact structure between holdings. The objective of our study was to design network models of pig movements matching with epidemiological features of pathogens. Our first model represents the transmission of infectious diseases between farms only through the introduction of animals to holdings (Animal Introduction Model AIM), whereas the second one also accounts for pathogen spread through intermediate transit of trucks through farms even without any animal unloading (i.e. indirect transmission–Transit Model TM). To take the pyramidal organisation of pig production into consideration, these networks were studied at three different scales: the whole network and two subnetworks containing only breeding or production farms. The two models were applied to pig movement data recorded in France from June 2012 to December 2014. For each type of model, we calculated network descriptive statistics, looked for weakly/strongly connected components (WCCs/SCCs) and communities, and analysed temporal patterns. Whatever the model, the network exhibited scale-free and small-world topologies. Differences in centrality values between the two models showed that nucleus, multiplication and post-weaning farms played a key role in the spread of diseases transmitted exclusively by the introduction of infected animals, whereas farrowing and farrow-to-finish herds appeared more vulnerable to the introduction of infectious diseases through indirect contacts. The second network was less fragmented than the first one, a giant SCC being detected. The topology of network communities also varied with modelling assumptions: in the first approach, a huge geographically dispersed community was found, whereas the second model highlighted several small geographically clustered communities. These results underline the relevance of developing network models corresponding to pathogen features (e.g. their transmission route), and the need to target specific types of holdings/areas for surveillance depending on the epidemiological context.

Modeling the live-pig trade network in Georgia: Implications for disease prevention and control

Live pig trade patterns, drivers and characteristics, particularly in backyard predominant systems, remain largely unexplored despite their important contribution to the spread of infectious diseases in the swine industry. A better understanding of the pig trade dynamics can inform the implementation of risk-based and more cost-effective prevention and control programs for swine diseases. In this study, a semi-structured questionnaire elaborated by FAO and implemented to 487 farmers was used to collect data regarding basic characteristics about pig demographics and live-pig trade among villages in the country of Georgia, where very scarce information is available. Social network analysis and exponential random graph models were used to better understand the structure, contact patterns and main drivers for pig trade in the country. Results indicate relatively infrequent (a total of 599 shipments in one year) and geographically localized (median Euclidean distance between shipments = 6.08 km; IQR = 0-13.88 km) pig movements in the studied regions. The main factors contributing to live-pig trade movements among villages were being from the same region (i.e., local trade), usage of a middleman or a live animal market to trade live pigs by at least one farmer in the village, and having a large number of pig farmers in the village. The identified villages' characteristics and structural network properties could be used to inform the design of more cost-effective surveillance systems in a country which pig industry was recently devastated by African swine fever epidemics and where backyard production systems are predominant.

Modeling the spatio-temporal dynamics of porcine reproductive & respiratory syndrome cases at farm level using geographical distance and pig trade network matrices

Background

Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. A better understanding of the disease dynamics and the transmission pathways under diverse epidemiological scenarios is a key for the successful PRRS control and elimination in endemic settings. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US. For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. We estimated five PD models with different weights namely: (i) geographical distance weight which contains the inverse distance between each pair of farms in kilometers, (ii) pig trade weight (PT_ji) which contains the absolute number of pig movements between each pair of farms, (iii) the product between the distance weight and the standardized relative pig trade weight, (iv) the product between the standardized distance weight and the standardized relative pig trade weight, and (v) the product of the distance weight and the pig trade weight.

Results

The model that included the pig trade weight matrix provided the best fit to model the dynamics of PRRS cases on a 6-month basis from 2012 to 2015 and was able to predict PRRS outbreaks in the subsequent time period with an area under the ROC curve (AUC) of 0.88 and the accuracy of 85% (105/124).

Conclusion

The result of this study reinforces the importance of pig trade in PRRS transmission in the US. Methods and results of this study may be easily adapted to any production system to characterize the PRRS dynamics under diverse epidemic settings to more timely support decision-making.