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

Connectivity at the human-wildlife interface: starling movements relate to carriage of E. coli

Synanthropic bird species in human, poultry or livestock environments can increase the spread of pathogens and antibiotic-resistant bacteria between wild and domestic animals. We present the first telemetry-based spatial networks for a small songbird. We quantified landscape connectivity exerted by spotless starling movements, and aimed to determine if connectivity patterns were related to carriage of potential pathogens. We captured 28 starlings on a partridge farm in 2020 and tested them for Avian influenza virus, West Nile virus WNV, Avian orthoavulavirus 1, Coronavirus, Salmonella spp. and Escherichia coli. We did not detect any viruses or Salmonella, but one individual had antibodies against WNV or cross-reacting Flaviviruses. We found E. coli in 61 % (17 of 28) of starlings, 76 % (13 of 17) of which were resistant to gentamicin, 12 % (2 of 17) to cefotaxime/enrofloxacin and 6 % (1 of 17) were phenotypic extended spectrum beta-lactamase (ESBL) carriers. We GPS-tracked 17 starlings and constructed spatial networks showing how their movements (i.e. links) connect different farms with nearby urban and natural habitats (i.e. nodes with different attributes). Using E. coli carriage as a proxy for acquisition/dispersal of bacteria, we found differences across spatial networks constructed for E. coli positive (n = 7) and E. coli negative (n = 9) starlings. We used Exponential Random Graph Models to reveal significant differences between networks. In particular, an urban roost was more connected to other sites by movements of E. coli positive than by movements of E. coli negative starlings. Furthermore, an open pine forest used mainly for roosting was more connected to other sites by movements of E. coli negative than by movements of E. coli positive starlings. Using E. coli as a proxy for a potential pathogen carried by starlings, we reveal the pathways of spread that starlings could provide between farms, urban and natural habitats.

Understanding Smallholder Pigkeepers' Awareness and Perceptions of African Swine Fever and Its Control Measures in Ukraine

African swine fever (ASF) has posed a significant threat to Ukrainian pig farming since its identification in 2012. In this study, recognising the pivotal role of pigkeepers in disease control, we conducted ten focus groups involving 52 smallholders across eight regions in Ukraine. Using participatory methods, we revealed their awareness of ASF signs, transmission routes, preventive measures, and the perceptions of stakeholders involved in ASF control. Furthermore, we identified the smallholders' acceptance of eradication and restriction measures, the perceived impact of zoning consequences, and their main sources of ASF information. Smallholders identified fever and skin haemorrhage as the most indicative signs of ASF and highlighted rodents as a primary transmission concern. Disinfection was seen as the most effective measure for preventing the introduction of ASF. Pigkeepers who perceived their stakeholder role in ASF control showed more trust in themselves and veterinarians than in central veterinary authorities. Farm-level ASF eradication measures were generally accepted; however, culling within the protection zone was least accepted, with economic losses listed as the most impactful consequence for pigkeepers. For ASF information, pigkeepers favour web searches and veterinarians, as well as traditional media and word-of-mouth news. This study provides valuable insights into refining the ASF communication strategies in Ukraine.

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015-2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.

Innovative Research Offers New Hope for Managing African Swine Fever Better in Resource-Limited Smallholder Farming Settings: A Timely Update

African swine fever (ASF) in domestic pigs has, since its discovery in Africa more than a century ago, been associated with subsistence pig keeping with low levels of biosecurity. Likewise, smallholder and backyard pig farming in resource-limited settings have been notably affected during the ongoing epidemic in Eastern Europe, Asia, the Pacific, and Caribbean regions. Many challenges to managing ASF in such settings have been identified in the ongoing as well as previous epidemics. Consistent implementation of biosecurity at all nodes in the value chain remains most important for controlling and preventing ASF. Recent research from Asia, Africa, and Europe has provided science-based information that can be of value in overcoming some of the hurdles faced for implementing biosecurity in resource-limited contexts. In this narrative review we examine a selection of these studies elucidating innovative solutions such as shorter boiling times for inactivating ASF virus in swill, participatory planning of interventions for risk mitigation for ASF, better understanding of smallholder pig-keeper perceptions and constraints, modified culling, and safe alternatives for disposal of carcasses of pigs that have died of ASF. The aim of the review is to increase acceptance and implementation of science-based approaches that increase the feasibility of managing, and the possibility to prevent, ASF in resource-limited settings. This could contribute to protecting hundreds of thousands of livelihoods that depend upon pigs and enable small-scale pig production to reach its full potential for poverty alleviation and food security.

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.

Modelling African swine fever virus spread in pigs using time-respective network data: Scientific support for decision-makers

African Swine Fever (ASF) represents the main threat to swine production, with heavy economic consequences for both farmers and the food industry. The spread of the virus that causes ASF through Europe raises the issues of identifying transmission routes and assessing their relative contributions in order to provide insights to stakeholders for adapted surveillance and control measures. A simulation model was developed to assess ASF spread over the commercial swine network in France. The model was designed from raw movement data and actual farm characteristics. A metapopulation approach was used, with transmission processes at the herd level potentially leading to external spread to epidemiologically connected herds. Three transmission routes were considered: local transmission (e.g. fomites, material exchange), movement of animals from infected to susceptible sites, and transit of trucks without physical animal exchange. Surveillance was represented by prevalence and mortality detection thresholds at herd level, which triggered control measures through movement ban for detected herds and epidemiologically related herds. The time from infection to detection varied between 8 and 21 days, depending on the detection criteria, but was also dependent on the types of herds in which the infection was introduced. Movement restrictions effectively reduced the transmission between herds, but local transmission was nevertheless observed in higher proportions highlighting the need of global awareness of all actors of the swine industry to mitigate the risk of local spread. Raw movement data were directly used to build a dynamic network on a realistic time-scale. This approach allows for a rapid update of input data without any pre-treatment, which could be important in terms of responsiveness, should an introduction occur. This article is protected by copyright. All rights reserved.

Complex network analysis to understand trading partnership in French swine production

The circulation of livestock pathogens in the pig industry is strongly related to animal movements. Epidemiological models developed to understand the circulation of pathogens within the industry should include the probability of transmission via between-farm contacts. The pig industry presents a structured network in time and space, whose composition changes over time. Therefore, to improve the predictive capabilities of epidemiological models, it is important to identify the drivers of farmers’ choices in terms of trade partnerships. Combining complex network analysis approaches and exponential random graph models, this study aims to analyze patterns of the swine industry network and identify key factors responsible for between-farm contacts at the French scale. The analysis confirms the topological stability of the network over time while highlighting the important roles of companies, types of farm, farm sizes, outdoor housing systems and batch-rearing systems. Both approaches revealed to be complementary and very effective to understand the drivers of the network. Results of this study are promising for future developments of epidemiological models for livestock diseases. This study is part of the One Health European Joint Programme: BIOPIGEE.

The impact of the epidemic experience on the recovery of production of pig farmers after the outbreak-Evidence from the impact of African swine fever (ASF) in Chinese pig farming

The African swine fever (ASF) has triggered considerable shocks to the pig farming industry, which has become a significant animal disease epidemic. The study explores the effect of epidemic experience on post-outbreak production recovery from resilience and risk perception based on 340 micro-survey data from Sichuan, Henan, and Shandong provinces. Epidemic experience has been shown to impact the degree of post-outbreak production recovery positively, and farmers who have endured epidemics are more likely to recover their production after outbreaks. The mechanistic study indicates that past epidemics in African swine fever shocks can effectively improve farmers' cognitive resilience and management capability, enhance recovery, and reduce risk perception in the aftermath of production recovery. In order to alleviate the endogenous problems caused by selection bias, missing variables, and two-way causality. This paper uses factor analysis to comprehensively measure production recovery capacity and production risk perception, and uses propensity score matching(PSM), instrumental variable method and replacement measurement methods to conduct robustness tests, and find the conclusions are still robust. The empirical analysis shows that the experience of the epidemic will promote the recovery of farmers after the outbreak; the experience of the epidemic will significantly impact the recovery of production after the outbreak for both free-range and professional farmers.

Descriptive and Multivariate Analysis of the Pig Sector in North Macedonia and Its Implications for African Swine Fever Transmission

North Macedonia, a country in the Balkan region of Europe, is currently bordered to the north and east by countries with active African swine fever (ASF) outbreaks. The predominantly traditional backyard pig farming sector in this country is under imminent threat of disease incursion. The characteristics and practices of such sectors have rarely been described, and thus the implications for these factors on disease introduction and spread are poorly understood. Using a semi-structured questionnaire, 457 pig producers were interviewed, providing information on 77.7% of the pig population in North Macedonia. In addition, a pilot study of 25 pig producers in Kosovo was performed. This study aimed to provide a detailed description of the North Macedonian pig sector, to make comparisons with nearby Kosovo, and to identify areas with high-risk practices for targeted mitigation. Descriptive data were summarized. Results of the questionnaire were used to identify farm-level risk factors for disease introduction. These factors were used in the calculation of a biosecurity risk score. Kernel density estimation methods were used to generate density maps highlighting areas where the risk of disease introduction was particularly concentrated. Multiple correspondence analysis with hierarchical clustering on principal components was used to explore patterns in farm practices. Results show that farms were predominantly small-scale with high rates of turnover. Pig movement was predominantly local. The highest biosecurity risk scores were localized in the eastern regions of North Macedonia, concerningly the same regions with the highest frequency of wild boar sightings. Veterinarians were highly regarded, regularly utilized, and trusted sources of information. Practices that should be targeted for improvement include isolation of new pigs, and consistent application of basic sanitary practices including washing hands, use of disinfection mats, and separation of clean and dirty areas. This study provides the most complete description of the North Macedonian pig sector currently available. It also identifies regions and practices that could be targeted to mitigate the risk of disease incursion and spread. These results represent the first steps to quantify biosecurity gaps and high-risk behaviors in North Macedonia, providing baseline information to design risk-based, more cost-effective, prevention, surveillance, and control strategies.

Socio-spatial relations observed in the global city network of firms

One of the prevailing approaches to the study of the global economy is the analysis of global city networks based on the activities of multinational firms. Research in this vein generally conceptualises cities as nodes, and the intra-firm relations between them as ties, forming the building blocks for globally scaled interurban networks. While such an approach has provided a valuable heuristic for understanding how cities are globally connected, and how the global economy can be conceived of as a network of cities, there is a lack of understanding as to how and why cities are connected, and which factors contribute to the existence of ties between cities. Here, we explain how five distinct socio-spatial dimensions contribute to global city network structure through their diverse effects on interurban dyads. Based on data from 13,583 multinational firms with 163,821 international subsidiary locations drawn from 208 global securities exchanges, we hypothesise how regional, linguistic, industrial, developmental, and command & control relations may contribute to network structure. We then test these by applying an exponential random graph model (ERGM) to explain how each dimension may contribute to cities' embeddedness within the overall network. Though all are shown to shape interurban relations to some extent, we find that two cities sharing a common industrial base are more likely to be connected. The ERGM also reveals a strong core-periphery structure in that cities in middle- and low-income countries are more reliant on connectivity than those in high-income countries. Our findings indicate that, despite claims seeking to de-emphasise the top-heavy organisational structure of the global urban economic network, interurban relations are characterised by uneven global development in which socio-spatial embeddedness manifests through a combination of similarity (homophily) and difference (heterophily) as determined by heterogeneous power relationships underlying global systems of production, exchange and consumption.

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.

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

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

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.

A Review of Risk Factors of African Swine Fever Incursion in Pig Farming within the European Union Scenario

African swine fever (ASF) is a notifiable viral disease of pigs and wild boars that could lead to serious economic losses for the entire European pork industry. As no effective treatment or vaccination is available, disease prevention and control rely on strictly enforced biosecurity measures tailored to the specific risk factors of ASF introduction within domestic pig populations. Here, we present a review addressing the risk factors associated with different European pig farming systems in the context of the actual epidemiological scenario. A list of keywords was combined into a Boolean query, “African swine fever” AND (“Risk factors” OR “Transmission” OR “Spread” OR “Pig farming” OR “Pigs” OR “Wild boars”); was run on 4 databases; and resulted in 52 documents of interest being reviewed. Based on our review, each farming system has its own peculiar risk factors: commercial farms, where best practices are already in place, may suffer from unintentional breaches in biosecurity, while backyard and outdoor farms may suffer from poor ASF awareness, sociocultural factors, and contact with wild boars. In the literature selected for our review, human-related activities and behaviours are presented as the main risks, but we also stress the need to implement biosecurity measures also tailored to risks factors that are specific for the different pig farming practices in the European Union (EU).

Mechanical transmission of African swine fever virus by Stomoxys calcitrans: Insights from a mechanistic model

African swine fever (ASF) represents a global threat with huge economic consequences for the swine industry. Even though direct contact is likely to be the main transmission route from infected to susceptible hosts, recent epidemiological investigations have raised questions regarding the role of haematophagous arthropods, in particular the stable fly (Stomoxys calcitrans). In this study, we developed a mechanistic vector-borne transmission model for ASF virus (ASFV) within an outdoor domestic pig farm in order to assess the relative contribution of stable flies to the spread of the virus. The model was fitted to the ecology of the vector, its blood-feeding behaviour and pig-to-pig transmission dynamic. Model outputs suggested that in a context of low abundance (<5 flies per pig), stable flies would play a minor role in the spread of ASFV, as they are expected to be responsible for around 10% of transmission events. However, with abundances of 20 and 50 stable flies per pig, the vector-borne transmission would likely be responsible for almost 30% and 50% of transmission events, respectively. In these situations, time to reach a pig mortality of 10% would be reduced by around 26% and 40%, respectively. The sensitivity analysis emphasized that the expected relative contribution of stable flies was strongly dependent on the volume of blood they regurgitated and the infectious dose for pigs. This study identified crucial knowledge gaps that need to be filled in order to assess more precisely the potential contribution of stable flies to the spread of ASFV, including a quantitative description of the populations of haematophagous arthropods that could be found in pig farms, a better understanding of blood-feeding behaviours of stable flies and the quantification of the probability that stable flies partially fed with infectious blood transmit the virus to a susceptible pig during a subsequent blood-feeding attempt.

Modelling infectious viral diseases in swine populations: a state of the art

Mathematical modelling is nowadays a pivotal tool for infectious diseases studies, completing regular biological investigations. The rapid growth of computer technology allowed for development of computational tools to address biological issues that could not be unravelled in the past. The global understanding of viral disease dynamics requires to account for all interactions at all levels, from within-host to between-herd, to have all the keys for development of control measures. A literature review was performed to disentangle modelling frameworks according to their major objectives and methodologies. One hundred and seventeen articles published between 1994 and 2020 were found to meet our inclusion criteria, which were defined to target papers representative of studies dealing with models of viral infection dynamics in pigs. A first descriptive analysis, using bibliometric indexes, permitted to identify keywords strongly related to the study scopes. Modelling studies were focused on particular infectious agents, with a shared objective: to better understand the viral dynamics for appropriate control measure adaptation. In a second step, selected papers were analysed to disentangle the modelling structures according to the objectives of the studies. The system representation was highly dependent on the nature of the pathogens. Enzootic viruses, such as swine influenza or porcine reproductive and respiratory syndrome, were generally investigated at the herd scale to analyse the impact of husbandry practices and prophylactic measures on infection dynamics. Epizootic agents (classical swine fever, foot-and-mouth disease or African swine fever viruses) were mostly studied using spatio-temporal simulation tools, to investigate the efficiency of surveillance and control protocols, which are predetermined for regulated diseases. A huge effort was made on model parameterization through the development of specific studies and methodologies insuring the robustness of parameter values to feed simulation tools. Integrative modelling frameworks, from within-host to spatio-temporal models, is clearly on the way. This would allow to capture the complexity of individual biological variabilities and to assess their consequences on the whole system at the population level. This would offer the opportunity to test and evaluate in silico the efficiency of possible control measures targeting specific epidemiological units, from hosts to herds, either individually or through their contact networks. Such decision support tools represent a strength for stakeholders to help mitigating infectious diseases dynamics and limiting economic consequences.

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 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.

Simulation modeling of influenza transmission through backyard pig trade networks in a wildlife/livestock interface area

Influenza constitutes a challenge to animal and human health. It is a highly contagious disease with wildlife reservoirs and considered as endemic among swine populations. Pigs are crucial in the disease dynamics due to their capacity to generate new reassortant viruses. The risk of informal animal trade in the spread of zoonotic diseases is well recognized worldwide. Nevertheless, the contribution of the backyard pig trade network in the transmission of influenza in a wildlife/livestock interface area is unknown. This study provides the first simulation of influenza transmission based on backyard farm connections in Mexico. A susceptible-infectious-recovered (SIR) model was implemented using the Epimodel software package in R, and 260 backyard farms were considered as nodes. Three different scenarios of connectivity (low, medium, and high) mediated by trade were generated and compared. Our results suggest that half of the pig population were infected within 5 days in the high connectivity scenario and the number of infected farms was approximately 65-fold higher compared to the low connected one. The consequence of connectivity variations directly influenced both time and duration of influenza virus transmission. Therefore, high connectivity driven by informal trade constitutes a significant risk to animal health. Trade patterns of animal movements are complex. This approach emphasizes the importance of pig movements and spatial dynamics among backyard production, live animal markets, and wildlife.

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.

Epidemiological considerations on African swine fever in Europe 2014-2018

In 2007 African swine fever (ASF) arrived at a Black Sea harbour in Georgia and in 2014 the infection reached the European Union (EU), where it still expands its territory. ASF is a fatal viral disease affecting domestic pigs and wild boar of all ages with clinical presentations ranging from per-acute to chronic disease, including apparently asymptomatic courses. Until the detection of the first case inside the EU, infections in the current epidemic were mainly seen among pig farms with generally low biosecurity, and with incidental spill over to the wild boar population. In the EU, however, the infection survived locally in the wild boar population independently from outbreaks in domestic pigs, with a steady and low prevalence. Apart from the wild boar population and the habitat, the current epidemic recognizes humans as the main responsible for both long distance transmission and virus introduction in the domestic pig farms. This underlines the importance to include social science when planning ASF-prevention, -control, or -eradication measures. Based on experiences, knowledge and data gained from the current epidemic this review highlights some recent developments in the epidemiological understanding of ASF, especially concerning the role of wild boar and their habitats in ASF epidemiology. In this regard, the qualities of three epidemiological traits: contagiousity, tenacity, and case fatality rate, and their impact on ASF persistence and transmission are especially discussed.

Descriptive and multivariate analysis of the pig sector in Georgia and its implications for disease transmission

BACKGROUND

Georgia is a country in the Caucasus region with a traditional backyard and highly variable pig farming system. The practices of such sectors have seldom been described and analyzed to better understand their implication in the introduction and spread of infectious pig diseases. Moreover, the Georgian pig sector was badly hit by an epidemic of African swine fever in 2007 that quickly spread throughout the region.

MATERIALS AND METHODS

We interviewed 487 pig farmers and 116 butchers using closed questionnaires on socioeconomic issues related to pig production, husbandry practices, biosecurity, marketing and movements, and disease awareness. Surveys were conducted in four regions of Georgia and descriptive statistics were computed. Factorial analyses of mixed data and hierarchical clustering on principal components were applied to study the relationship among collected variables for both farmers and butchers.

RESULTS

Results show that pig farming in Georgia is a non-professional sector, highly heterogeneous by region, characterized by smallholdings of few animals, with low inputs, outdated technologies, and poor biosecurity, which all translates into low outputs and productivity. The hierarchical clustering on principal components confirmed that there are five major production and husbandry strategies, which match the four regions where the study was conducted.

CONCLUSIONS

Our results are the first step to quantify biosecurity gaps and risky behaviours, develop risk profiles, and identify critical control points across the market chain where to implement mitigation measures. This study provides the baseline information needed to design realistic and sustainable prevention, surveillance and control strategies.

Relevant Measures to Prevent the Spread of African Swine Fever in the European Union Domestic Pig Sector

During the past decade, African swine fever (ASF) has spread from the Caucasus region to eastern European Union countries affecting domestic pig and wild boar populations. In order to avert ASF spread, mitigation measures targeting both populations have been established. However, despite these efforts, ASF has been reported in thirteen different countries (Georgia, Azerbaijan, Armenia, the Russian Federation, Ukraine, Belarus, Estonia, Latvia, Lithuania, Poland, Moldova, Czech Republic, and Romania). In the absence of an effective vaccine or treatment to ASF, introduction and spread of ASF onto domestic pig farms can only be prevented by strict compliance to control measures. This study systematically reviewed available measures to prevent the spread of ASF in the EU domestic pig sector distinguishing between commercial, non-commercial, and outdoor farms. The search was performed in PubMed and using a common browser. A total of 52 documents were selected for the final review process, which included scientific articles, reports, EU documents and official recommendations, among others. From this literature review, 37 measures were identified as preventive measures for the introduction and spread of ASF. Subsequently, these measures were assessed by ASF experts for their relevance in the mitigation of ASF spread on the three mentioned types of farms. All experts agreed that some of the important preventive measures for all three types of farms were: the identification of animals and farm records; strict enforcement of the ban on swill feeding; and containment of pigs, so as to not allow direct or indirect pig–pig and/or pig–wild boar contacts. Other important preventive measures for all farms were education of farmers, workers, and operators; no contact between farmers and farm staff and external pigs; appropriate removal of carcasses, slaughter residues, and food waste; proper disposal of manure and dead animals, and abstaining from hunting activities during the previous 48 h (allowing a 48 h interval between hunting and being in contact with domestic pigs). Finally, all experts identified that the important preventive measures for non-commercial and outdoor farms is to improve access of those farms to veterinarians and health services.

Third wave of African swine fever infection in Armenia: Virus demonstrates the reduction of pathogenicity

Aim:

First cases of clinically uncommon African swine fever (ASF), caused by virus genotype II are described in this article. These cases occurred in Armenia, Tavush region, Dilijan municipality in 2011. The aim of this study was to identify and describe the new pathogenic forms of ASF in Armenia.

Materials and Methods:

The isolation and identification of ASF virus (ASFV) were carried out using conventional techniques. Clinical signs of infection were recorded daily. Gross anatomical pathology characteristics were observed during routine postmortem examinations. Blood and serum were obtained by puncture of the jugular vein using a vacutainer system.

Results:

The presence of ASFV DNA in the spleens was confirmed by polymerase chain reaction. Sequenced sections of p72 showed phylogenetic identity to genotype 2. The pathology exhibits unusual manifestations of the main disease. The unusual form of ASF demonstrates characteristics of a subacute form of the disease, with the possibility of conversion to a chronic form. Decreased lethality, low level of hemorrhages, and absence of severe pancytopenia in smears from spleen, lymph nodes, and blood are common features of the new form of ASF. Unlike severe thrombocytopenia in the typical ASF, the unusual form exhibited moderate or minor decrease of this feature. Despite a moderate decrease in hemadsorption titers, the unusual pattern of the disease was characterized by viremia and the presence of the virus in the visceral organs, including the brain.

Conclusion:

Our data allow assuming that new nosological form of ASF (genotype II) may present as a transitional form of the disease with the possibility of chronization.