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

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

Network epidemiology of cattle and cattle farms in Great Britain

Infectious diseases of livestock can cause substantial production losses and have detrimental impacts upon human health, and animal health and welfare. To limit the impact of diseases, understanding more about the dynamics of transmission can assist in the control and prevention of infectious disease. In particular, understanding infection transmission on networks, ‘network epidemiology’, offers a flexible approach, incorporating between-host heterogeneity in potentially infectious contacts drawn from empirical study of interactions among individual animals, or among farms. Trading animals and optimising productivity are vital to the commercial viability of farms, however they necessarily involve compromises in biosecurity, animal health, and welfare. Better understanding of the relationships among these multiple factors might facilitate the development of sustainable livestock industries that are more resilient to disease outbreaks. In this thesis I examine cattle interactions at two spatial scales, first at a national-level by studying the trading connections among farms, and then at a finer scale by analysing the social interactions among cattle. First, I introduce the concept of superspreaders, hosts that generate many more secondary infections than the rest of the population, and evaluate evidence for the notion that some farms might act as superspreaders of infection. I utilise the example of bovine tuberculosis (bTB) to illustrate this concept and find that farms might act as superspreaders in three main ways; first, via exceptional trading between farms, second, by factors that facilitate high within-herd transmission and trading of high-risk animals, and third, by harbouring undetected infection for long periods. I find mechanisms that align with all three processes in the cattle industry in Great Britain that might allow superspreader farms to contribute to the current bTB epidemic. At a national level, I describe cattle movements among farms over time, finding that some farms consistently act as ‘hubs’ in trading networks, functioning in a similar way to markets, in that they are highly connected to other farms by many direct trades. Utilising the temporal network measure of ‘contact chains’, I quantify the farms that represent potential sources of infection (ingoing contact chains) and the potential farms that a farm might infect over 1 year periods. Farms divide into two groups: those with very few connections (less than 10 farms) that are relatively isolated from the network, and those with very many connections (more than 1000 farms) that are highly connected within the network. I find that a substantial number of farms have over 10,000 farms in both their ingoing and outgoing contact chains, such that, if infected, they might potentially act as superspreaders by being more at risk of both acquiring and spreading infection. Building on my previous analysis, I then characterise the ‘source farms’ in the ingoing contact chains, in terms of their location and bTB history. I find that after controlling for previously-established risk factors for bTB, having more source farms in areas of higher bTB risk in the ingoing contact chain increases the odds of a bTB incident on the root farm, whilst having more source farms in lower risk areas is associated with lower odds of a bTB incident on the root farm. At a finer scale of contacts among animals, I explore interactions among dairy cattle in multiple herds using automated proximity sensors and GPS devices. When aggregated over long periods, cattle interactions appear dense and unstructured, however finer time spatial and temporal perspectives revealed structure and variation in contacts. Herds in our study had variable grazing and housing access, allowing us to determine that cattle interact with more other cows, for longer time periods when they are in buildings compared to contacts at pasture. Cattle exhibited heterogeneity in their number and duration of contacts, and although the majority of cattle interacted more equally with other cows, a small proportion of cows in each group showed evidence of stronger social ties. Next, I consider associations between social interactions, production, and health. I review the existing literature on social parameters such as dominance rank and re-grouping of cattle, and find inconclusive outcomes regarding their impact on milk yield and somatic cell count, an indicator of udder health. I perform my own analysis to examine the relationship between the time cows spend with other cows, milk yield and somatic cell count, and do not find a statistically significant relationship. In considering social preference, cows that had experienced the same number of lactations were more likely to interact, but cows spending more time with cows in the same lactation did not appreciably affect their milk yield or somatic cell count. Finally, I draw together the findings of this thesis and reflect on how the identification of higher-risk farms might be useful in the control of livestock infections, and specifically bTB in Great Britain. I conclude that network analysis is a valuable tool to study the interactions of cattle and cattle farms, identifying unique opportunities for targeted approaches to disease control.Animal Health Veterinary Laboratories Agency BBSRC - BB/M015874/

Contact chains of cattle farms in Great Britain

This is the final version. Available from the publisher via the DOI in this record.Further information and figures supporting this article have been uploaded as part of the electronic supplementary material. Underlying data consist of every movement of cattle between all farms in Great Britain. Aside from the size of the dataset, there are substantial issues of confidentiality (locations, trading practices) and commercial sensitivity in these data. They are collated and managed by Defra, via the Animal and Plant Health Agency, who grant access to the data with specific permissions for specific studies. In practice, this means that the data can be used for the stated purpose only, and making the data publicly accessible would not conform to the licence the authors have been granted to use these data. With the agreement of the journal’s Editorial Office, the authors will not be able to make the dataset available on this occasion, but encourage readers, referees and editors to contact the Animal and Plant Health Agency data manager for data access requests. At the time of submission, the data manager is Andy Mitchell ([email protected])Network analyses can assist in predicting the course of epidemics. Time-directed paths or ‘contact chains’ provide a measure of hostconnectedness across specified timeframes, and so represent potential pathways for spread of infections with different epidemiological characteristics. We analysed networks and contact chains of cattle farms in Great Britain using Cattle Tracing System data from 2001 to 2015. We focused on the potential for between-farm transmission of bovine tuberculosis, a chronic infection with potential for hidden spread through the network. Networks were characterized by scale-free type properties, where individual farms were found to be influential ‘hubs’ in the network. We found a markedly bimodal distribution of farms with either small or very large ingoing and outgoing contact chains (ICCs and OCCs). As a result of their cattle purchases within 12-month periods, 47% of British farms were connected by ICCs to more than 1000 other farms and 16% were connected to more than 10 000 other farms. As a result of their cattle sales within 12-month periods, 66% of farms had OCCs that reached more than 1000 other farms and 15% reached more than 10 000 other farms. Over 19 000 farms had both ICCs and OCCs reaching more than 10 000 farms for two or more years. While farms with more contacts in their ICCs or OCCs might play an important role in disease spread, farms with extensive ICCs and OCCs might be particularly important by being at higher risk of both acquiring and disseminatinginfections.Biotechnology and Biological Science Research Council (BBSRC

Characterization of potential superspreader farms for bovine tuberculosis: A review

This is the final version. Available on open access from Wiley via the DOI in this recordBACKGROUND: Variation in host attributes that influence their contact rates and infectiousness can lead some individuals to make disproportionate contributions to the spread of infections. Understanding the roles of such 'superspreaders' can be crucial in deciding where to direct disease surveillance and controls to greatest effect. In the epidemiology of bovine tuberculosis (bTB) in Great Britain, it has been suggested that a minority of cattle farms or herds might make disproportionate contributions to the spread of Mycobacterium bovis, and hence might be considered 'superspreader farms'. OBJECTIVES AND METHODS: We review the literature to identify the characteristics of farms that have the potential to contribute to exceptional values in the three main components of the farm reproductive number - Rf : contact rate, infectiousness and duration of infectiousness, and therefore might characterize potential superspreader farms for bovine tuberculosis in Great Britain. RESULTS: Farms exhibit marked heterogeneity in contact rates arising from between-farm trading of cattle. A minority of farms act as trading hubs that greatly augment connections within cattle trading networks. Herd infectiousness might be increased by high within-herd transmission or the presence of supershedding individuals, or infectiousness might be prolonged due to undetected infections or by repeated local transmission, via wildlife or fomites. CONCLUSIONS: Targeting control methods on putative superspreader farms might yield disproportionate benefits in controlling endemic bovine tuberculosis in Great Britain. However, real-time identification of any such farms, and integration of controls with industry practices, present analytical, operational and policy challenges.Biotechnology and Biological Sciences Research Council (BBSRC)Animal and Plant Health Agenc

Infection through the farm gate

This thesis is based on studies of movements of livestock, on-farm biosecurity and disease awareness among farmers in Sweden; factors which can affect the spread of contagious livestock diseases. The structure of the cattle and pig movements were analysed using data obtained from the Swedish Board of Agriculture. Most movements were within 100 km, however, there were also long distance movements up to 1200 km for cattle and 1000 km for pigs. This supports an initial total standstill in case of an outbreak of foot-and-mouth disease. Network analysis was used to investigate the contact patterns. Many of the farms did not sell or buy animals or had only limited trade, whereas some farms had many contacts. The measure 'ingoing infection chain' was constructed to capture indirect movements, some farms with few direct contacts had many indirect contacts and this measure can potentially be very useful for disease control and risk based surveillance. On-farm biosecurity was investigated through a posted questionnaire study to 1498 farmers and response was retrieved from 34% of them. Among farmers declining participation, the major reason was not having livestock. There was large variation in biosecurity routines among farmers. In general farmers with pigs had higher biosecurity compared to farmers with cattle, sheep/goats or mixed species or hobby farmers. Many farmers and visitors did not have sufficient routines to prevent spread of disease and some reported inconsistent routines, indicating a lack of knowledge of how to prevent spread of disease. A need for improvement of onfarm biosecurity was identified. Disease awareness and information retrieval among pig farmers in relation to an outbreak of PRRS in 2007 was investigated using posted questionnaires to 153 farmers. Farmers with large herds were in general aware of the outbreak and how to protect their farm. However, hobby farmers were identified as a group difficult to reach with information in case of an outbreak. Active search for information was associated with distance from the outbreak. The Swedish Animal Health Service, followed by the veterinary authorities, were considered the most important and reliable source of information

Characterization of potential superspreader farms for bovine tuberculosis:A review

Background: Variation in host attributes that influence their contact rates and infectiousness can lead some individuals to make disproportionate contributions to the spread of infections. Understanding the roles of such ‘superspreaders’ can be crucial in deciding where to direct disease surveillance and controls to greatest effect. In the epidemiology of bovine tuberculosis (bTB) in Great Britain, it has been suggested that a minority of cattle farms or herds might make disproportionate contributions to the spread of Mycobacterium bovis, and hence might be considered ‘superspreader farms’.Objectives and Methods: We review the literature to identify the characteristics of farms that have the potential to contribute to exceptional values in the three main components of the farm reproductive number - Rf: contact rate, infectiousness and duration of infectiousness, and therefore might characterize potential superspreader farms for bovine tuberculosis in Great Britain.Results: Farms exhibit marked heterogeneity in contact rates arising from between-farm trading of cattle. A minority of farms act as trading hubs that greatly augment connections within cattle trading networks. Herd infectiousness might be increased by high within-herd transmission or the presence of supershedding individuals, or infectiousness might be prolonged due to undetected infections or by repeated local transmission, via wildlife or fomites.Conclusions: Targeting control methods on putative superspreader farms might yield disproportionate benefits in controlling endemic bovine tuberculosis in Great Britain. However, real-time identification of any such farms, and integration of controls with industry practices, present analytical, operational and policy challenges.<br/

Simulation models for between farm transmission of PRRS virus in Canadian swine herds

Porcine reproductive and respiratory syndrome (PRRS) is a viral disease of pigs, which affects all production stages and has severe economic consequences for the swine industry. The virus is primarily spread between farms through direct and indirect contacts. A limited number of studies have been carried out to understand the between-farm transmission dynamics of the virus. The objectives of this thesis were to explore the contact structures among swine farms in Canada and to use these contact structures to better understand the pattern and dynamics of between-farm spread of PRRS virus among Canadian swine herds. Four different studies were designed and implemented to achieve these objectives. The first study used network analysis tools to analyse pig movement data which revealed characteristics of contact patterns between swine herds and suggested a hierarchical structure within the Canadian swine industry, where pigs typically move in a unidirectional manner from one production stage to another. The median in-degree and out-degree for farms in this study was 1 and ranged between 0-26 and 0-10 respectively for the overall network. The degree distributions demonstrated characteristics of a power-law distribution, suggesting the presence of scale-free structure while the size of clustering coefficient suggested presence of small-world structure in the swine movement network. Additionally, high levels of truck sharing between farms were noted in this study, with a typical truck, during the study period, being shared among four different farms. The second and third studies simulated the between farm spread of the PRRS based on the movement of pigs and the sharing of trucks among swine farms, using the North American Animal Disease Spread Model and the network-based models respectively. These studies provided a means to assess the relative importance of direct and indirect contact via truck sharing on between farm spread of PRRS virus. By including the transmission by trucks in the model, the median number of infected farms increased by 18% and the median epidemic size increased by 44% in the spatial model. Furthermore, with the addition of trucks in the model, the hierarchical structure of the industry was significantly altered and multidirectional disease spread was observed. On the other hand, the network-based models assessed the impact of scale-free, small-world and random network structures on the between farm spread of PRRS virus and demonstrated the influence these network structures can have on the spread of the virus. The spread on scale-free networks resulted in the smallest stochastic die-out percentage with highest epidemic sizes compared to spread on small-world or random networks. Similarly, the incorporation of transmission by trucks in the model had the highest impact on small-world and random networks, where the epidemic size doubled, compared to scale-free networks, where it increased by 20-29%. Given the importance of transmission of the virus via truck (e.g. indirect contacts) identified in the previous studies, the last chapter aims at (i) quantifying the likelihood that a pig transport truck shared among farms could remain contaminated with PRRS virus at the end of Day 1 and to (ii) evaluate the efficacy of commonly used cleaning and disinfection protocols in eliminating the virus from these trucks. The results of this study suggested, when no cleaning and disinfection protocol is applied, that it is moderately likely that the truck could become contaminated and remain infected with the PRRS virus (mean probability ranged between 0.338-0.352, when the truck was shared between two farms), and that this risk marginally increased with an increase in the number of farms the truck was shared among. This final study also suggested that once contaminated, most of the contaminated trucks would likely remain infected for more than one day. The studies presented in this thesis have not only provided a clearer insight into the pattern of contacts between farms, and the impact these contacts can have on PRRS virus spread, but have also highlighted the importance of including data on the sharing of trucks among farms, since trucks will tend to connect farms which would otherwise share no connection. Moreover, the studies in this thesis have reinforced the importance of the proper cleaning and disinfection of trucks between successive shipments, as the findings presented here suggest that with an increasing level of truck sharing between farms, shared trucks are likely to remain contaminated with the virus and sharing of trucks significantly increased the risk of between farm spread of PRRS virus. Not only do the shared trucks have a high probability of becoming contaminated with the virus, but once contaminated, they are likely to remain infected for a comparatively long period particularly in the absence of adequate disinfection. It should be noted that the pig movement data used in this study was not very recent and consisted of movements reported for only four months time period. Additionally, the described models could not be validated due to unavailability of data is another noteworthy limitation of the studies described in this thesis

Risk analysis of Bovine viral diarrhea and Bovine herpesvirus-1 introduction based on biosecurity measures implemented in dairy cattle farms

L'objectiu general d'aquesta tesi doctoral va ser desenvolupar una eina d'avaluació de riscos per a donar suport a la presa de decisions de priorització de mesures de bioseguretat en granges lleteres. En el primer estudi, es va desenvolupar un model d'anàlisi de risc estocàstic per quantificar la probabilitat d'introducció de Virus de la Diarrea Vírica Bovina (BVDV) i de l'Herpesvirus Boví tipus 1 (BoHV-1) a través de el moviment d'animals, és a dir , compra o introducció d'animals, recria criada fora de la granja i moviments a fires de bestiar. Els paràmetres es van obtenir a partir de literatura científica, així com de la base de dades de moviments animals de l'administració, enquestes de bioseguretat i opinió dels veterinaris de camp. En l'anàlisi es van incloure 46 granges de Galícia i Catalunya que van moure animals durant el 2017. La probabilitat anual d'introducció de BVDV i BoHV-1 va ser molt heterogènia, sent propera a 0 en algunes granges i en altres proper a 1. La mitjana d' la probabilitat anual d'introducció de BVDV va ser 12% i per BoHV-1 9%, amb un rang interquartílic entre 1.2% i 28% per BVDV i 3% a 23% per BoHV-1. Les probabilitats més altes estaven associades a el moviment local d'animals (i.e., dins de la mateixa comunitat autònoma) i a el fet de compartir el vehicle de transport entre granges. A l'avaluar l'efecte de les mesures de bioseguretat en una granja, la implementació d'una correcta quarantena i el no compartir el transport amb altres granges disminueixo considerablement aquesta probabilitat. En el segon estudi, es va quantificar la probabilitat d'introducció de BVDV i BoHV-1 a través de contactes indirectes per la qual cosa també es va desenvolupar un model d'anàlisi de risc estocàstic. Es van considerar els vehicles que transporten vedells, animals a l'escorxador, cadàvers i barreja d'alimentació, així com els visitants, entre els quals es van incloure veterinaris, podòlegs, treballadors de les granges i veïns. Per a aquest estudi es van avaluar les 127 granges que van participar en el projecte i la informació per a l'estimació dels paràmetres de el model, a més de les fonts citades anteriorment, es va obtenir d'entrevistes a podòlegs, transportadors d'animals i una empresa de recollida de cadàvers. Els resultats van mostrar que la mitjana de la probabilitat anual d'introducció per BVDV va ser de 2.1% i per BoHV-1 3.9%, en un rang de 0.5% a 14.6% i de 1.0% a 24.9% per BVDV i BoHV-1, respectivament . L'entrada de el vehicle de transport de vedells i les visites dels veterinaris van ser les vies d'entrada amb major risc. Les mesures de bioseguretat amb un major impacte en la disminució de la probabilitat d'introducció dels dos virus van ser l'ús de botes i roba propis de la granja i evitar que el conductor que transporta animals entri en contacte amb els animals de la granja. Els dos models van permetre establir les vies d'entrada més perilloses per a cada granja estudiada i d'aquesta forma són una eina útil per prioritzar les mesures de bioseguretat que s'han d'implementar o millorar per reduir la probabilitat de l'ingrés de BVDV i BoHV-1 en una granja.El objetivo general de esta tesis doctoral fue desarrollar una herramienta de evaluación de riesgos para apoyar la toma de decisiones de priorización de medidas de bioseguridad en granjas lecheras. En el primer estudio, se desarrolló un modelo de análisis de riesgo estocástico para cuantificar la probabilidad de introducción del Virus de la Diarrea Vírica Bovina (BVDV) y del Herpesvirus Bovino tipo 1 (BoHV-1) a través del movimiento de animales, es decir, compra o introducción de animales, recría criada fuera de la granja y movimientos a ferias de ganado. Los parámetros se obtuvieron a partir de literatura científica, así como de la base de datos de movimientos animales de la administración, encuestas de bioseguridad y opinión de los veterinarios de campo. En el análisis se incluyeron 46 granjas de Galicia y Catalunya que movieron animales durante el 2017. La probabilidad anual de introducción de BVDV y BoHV-1 fue muy heterogénea, siendo cercana a 0 en algunas granjas y en otras cercano a 1. La mediana de la probabilidad anual de introducción de BVDV fue 12% y para BoHV-1 9%, con un rango intercuartilico entre 1.2% y 28% para BVDV y 3% a 23% para BoHV-1. Las probabilidades más altas estaban asociadas al movimiento local de animales (i.e., dentro de la misma comunidad autónoma) y al hecho de compartir el vehículo de transporte entre granjas. Al evaluar el efecto de las medidas de bioseguridad en una granja, la implementación de una correcta cuarentena y el no compartir el transporte con otras granjas disminuyo considerablemente esta probabilidad. En el segundo estudio, se cuantifico la probabilidad de introducción de BVDV y BoHV-1 a través de contactos indirectos para lo cual también se desarrolló un modelo de análisis de riesgo estocástico. Se consideraron los vehículos que transportan terneros, animales a matadero, cadáveres y mezcla de alimentación, así como los visitantes, entre los que se incluyeron veterinarios, podólogos, trabajadores de las granjas y vecinos. Para este estudio se evaluaron las 127 granjas que participaron en el proyecto y la información para la estimación de los parámetros del modelo, además de las fuentes citadas anteriormente, se obtuvo de entrevistas a podólogos, transportadores de animales y una empresa de recolección de cadáveres. Los resultados mostraron que la mediana de la probabilidad anual de introducción para BVDV fue de 2.1% y para BoHV-1 3.9%, en un rango de 0.5% a 14.6% y de 1.0% a 24.9% para BVDV y BoHV-1, respectivamente. La entrada del vehículo de transporte de terneros y las visitas de los veterinarios fueron las vías de entrada con mayor riesgo. Las medidas de bioseguridad con un mayor impacto en la disminución de la probabilidad de introducción de los dos virus fueron el uso de botas y ropa propios de la granja y evitar que el conductor que transporta animales entre en contacto con los animales de la granja. Los dos modelos permitieron establecer las vías de entrada más riesgosas para cada granja estudiada y de esta forma son una herramienta útil para priorizar las medidas de bioseguridad que deben implementarse o mejorarse para reducir la probabilidad del ingreso de BVDV y BoHV-1en una granja.The general aim of this PhD thesis was to develop a risk assessment tool to support biosecurity measures prioritization decision making in dairy farms. In the first study, a stochastic risk analysis model was developed to quantify Bovine viral diarrhea virus (BVDV) and Bovine herpesvirus type 1 (BoHV-1) introduction through animal movements. Purchasing cattle, rearing replacement heifers offsite and showing cattle at competitions, were considered in the model. Besides a review of the scientific literature, parameters were estimated using animal movement database, biosecurity surveys and the opinion of field veterinarians. In this model, 46 farms from Galicia and Catalonia that moved animals during 2017 were included. Results showed that the annual probability of BVDV and BoHV-1 introduction was very heterogeneous, being close to 0 in some farms and in others close to 1. The median of the probability of introduction of BVDV was 12% and for BoHV-1 9%, with an inter-quartile range from 1.2% to 28% for BVDV and 3% to 23% for BoHV-1. The highest probabilities were associated with local movements of cattle (i.e., inside the same autonomous community) and the fact of sharing the transport vehicle between farms. By evaluating the effect of biosecurity measures on a selected farm, implementation of a correct quarantine or not sharing transport with other farms greatly decreased this probability. In the second study, the probability of BVDV and BoHV-1 introduction through indirect contacts was quantified also with a stochastic risk analysis model. Vehicles transporting calves, cattle to slaughterhouse, dead animals, and food mix, as well as visits by veterinarians and hoof trimmers, farm workers and contacts with neighbors were considered in the model. For this study were included the 127 farms that participated in the project. Data to estimate model parameters was obtained from the sources indicated before as well as from interviews with hoof trimmers, animal transporters and a rendering company. Results evidenced that the median annual probability of introduction for BVDV was 2.1% and for BoHV-1 3.9%, in a range of 0.5% to 14.6% and 1.0% to 24.9% for BVDV and BoHV-1, respectively. The calf transport vehicle and veterinarians&#8217; visits were the routes with the highest risk. The biosecurity measures with the greatest impact in reducing the probability of introduction of both viruses were the use of boots and clothing belonging to the farm and avoiding the driver that transports cattle coming into contact with the animals on the farm. The two models allowed establishing the riskiest pathways for each studied farm and thus are a useful tool to prioritize biosecurity measures that must be implemented or improved to reduce the probability of BVDV and BoHV-1 introduction into a farm.Universitat Autònoma de Barcelona. Programa de Doctorat en Medicina i Sanitat Animal

Multilayer and multiplex networks: an introduction to their use in veterinary epidemiology

This is the final version. Available from Frontiers Media via the DOI in this record.Contact network analysis has become a vital tool for conceptualizing the spread of pathogens in animal populations and is particularly useful for understanding the implications of heterogeneity in contact patterns for transmission. However, the transmission of most pathogens cannot be simplified to a single mode of transmission and, thus, a single definition of contact. In addition, host-pathogen interactions occur in a community context, with many pathogens infecting multiple host species and most hosts being infected by multiple pathogens. Multilayer networks provide a formal framework for researching host-pathogen systems in which multiple types of transmission-relevant interactions, defined as network layers, can be analyzed jointly. Here, we provide an overview of multilayer network analysis and review applications of this novel method to epidemiological research questions. We then demonstrate the use of this technique to analyze heterogeneity in direct and indirect contact patterns amongst swine farms in the United States. When contact among nodes can be defined in multiple ways, a multilayer approach can advance our ability to use networks in epidemiological research by providing an improved approach for defining epidemiologically relevant groups of interacting nodes and changing the way we identify epidemiologically important individuals such as superspreaders.Biotechnology and Biological Sciences Research Council (BBSRC)NIFA-NSF-NIH Ecology and Evolution of Infectious Disease awardAgriculture and Food Research InitiativeSwine Health Information Center (SHIC)University of MinnesotaUniversity of Exete