A network model of E. coli O157 transmission within a typical UK dairy herd: the effect of heterogeneity and clustering on the prevalence of infection

Impact of the WHO Integrated Stewardship Policy on the Control of Methicillin-Resistant Staphyloccus aureus and Third-Generation Cephalosporin-Resistant Escherichia coli: Using a Mathematical Modeling Approach

Methicillin-resistant Staphylococcus aureus (MRSA) and third-generation cephalosporin-resistant Escherichia coli (3GCREc) are community and hospital-associated pathogens causing serious infections among populations by infiltrating into hospitals and surrounding environment. These main multi-drug resistant or antimicrobial resistance (AMR) bacterial pathogens are threats to human health if not properly tackled and controlled. Tackling antimicrobial resistance (AMR) is one of the issues for the World Health Organization (WHO) to design a comprehensive set of interventions which also helps to achieve the end results of the developing indicators proposed by the same organization. A deterministic mathematical model is developed and studied to investigate the impact of the WHO policy on integrated antimicrobial stewardship activities to use effective protection measures to control the spread of AMR diseases such as MRSA and 3GCREc in hospital settings by incorporating the contribution of the healthcare workers in a hospital and the environment in the transmission dynamics of the diseases. The model also takes into account the parameters describing various intervention measures and is used to quantify their contribution in containing the diseases. The impact of combinations of various possible control measures on the overall dynamics of the disease under study is investigated. The model analysis suggests that the contribution of the interventions: screening and isolating the newly admitted patients, improving the hygiene in hospital settings, decolonizing the pathogen carriers, and increasing the frequency of disinfecting the hospital environment are effective tools to contain the disease from invading the population. The study revealed that without any intervention, the diseases will continue to be a major cause of morbidity and mortality in the affected communities. In addition, the study indicates that a coordinated implementation of the integrated control measures suggested by WHO is more effective in curtailing the spread of the diseases than piecemeal strategies. Numerical experiments are provided to support the theoretical analysis.

Spatial and temporal variation in proximity networks of commercial dairy cattle in Great Britain

The nature of contacts between hosts can be important in facilitating or impeding the spread of pathogens within a population. Networks constructed from contacts between hosts allow examination of how individual variation might influence the spread of infections. Studying the contact networks of livestock species managed under different conditions can additionally provide insight into their influence on these contact structures. We collected high-resolution proximity and GPS location data from nine groups of domestic cattle (mean group size = 85) in seven dairy herds employing a range of grazing and housing regimes. Networks were constructed from cattle contacts (defined by proximity) aggregated by different temporal windows (2 h, 24 h, and approximately 1 week) and by location within the farm. Networks of contacts aggregated over the whole study were highly saturated but dividing contacts by space and time revealed substantial variation in cattle interactions. Cows showed statistically significant variation in the frequency of their contacts and in the number of cows with which they were in contact. When cows were in buildings, compared to being on pasture, contact durations were longer and cows contacted more other cows. A small number of cows showed evidence of consistent relationships but the majority of cattle did not. In one group where management allowed free access to all farm areas, cows showed asynchronous space use and, while at pasture, contacted fewer other cows and showed substantially greater between-individual variation in contacts than other groups. We highlight the degree to which variations in management (e.g. grazing access, milking routine) substantially alter cattle contact patterns, with potentially major implications for infection transmission and social interactions. In particular, where individual cows have free choice of their environment, the resulting contact networks may have a less-risky structure that could reduce the likelihood of direct transmission of infections.

Livestock Network Analysis for Rhodesiense Human African Trypanosomiasis Control in Uganda

Background: Infected cattle sourced from districts with established foci for Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) migrating to previously unaffected districts, have resulted in a significant expansion of the disease in Uganda. This study explores livestock movement data to describe cattle trade network topology and assess the effects of disease control interventions on the transmission of rHAT infectiousness. Methods: Network analysis was used to generate a cattle trade network with livestock data which was collected from cattle traders (n = 197) and validated using random graph methods. Additionally, the cattle trade network was combined with a susceptible, infected, recovered (SIR) compartmental model to simulate spread of rHAT (R _o 1.287), hence regarded as "slow" pathogen, and evaluate the effects of disease interventions. Results: The cattle trade network exhibited a low clustering coefficient (0.5) with most cattle markets being weakly connected and a few being highly connected. Also, analysis of the cattle movement data revealed a core group comprising of cattle markets from both eastern (rHAT endemic) and northwest regions (rHAT unaffected area). Presence of a core group may result in rHAT spread to unaffected districts and occurrence of super spreader cattle market or markets in case of an outbreak. The key cattle markets that may be targeted for routine rHAT surveillance and control included Namutumba, Soroti, and Molo, all of which were in southeast Uganda. Using effective trypanosomiasis such as integrated cattle injection with trypanocides and spraying can sufficiently slow the spread of rHAT in the network. Conclusion: Cattle trade network analysis indicated a pathway along which T. b. rhodesiense could spread northward from eastern Uganda. Targeted T. b. rhodesiense surveillance and control in eastern Uganda, through enhanced public-private partnerships, would serve to limit its spread.

Reviewing age-structured epidemiological models of cattle diseases tailored to support management decisions: Guidance for the future

Mechanistic simulation models are being increasingly used as tools to assist with animal health decision-making in the cattle sector. We reviewed scientific literature for studies reporting age-structured cattle management models in application to infectious diseases. Our emphasis was on papers dedicated to support decision making in the field. In this systematic review we considered 1290 manuscripts and identified 76 eligible studies. These are based on 52 individual models from 10 countries addressing 9 different pathogens. We provide an overview of these models and present in detail their theoretical foundations, design paradigms and incorporated processes. We propose a structure of the characteristics of cattle disease models using three main features: [1] biological processes, [2] farming-related processes and [3] pathogen-related processes. It would be of benefit if future cattle disease models were to follow this structure to facilitate science communication and to allow increased model transparency.

Mathematical Modeling Tools to Study Preharvest Food Safety

This article provides an overview of the emerging field of mathematical modeling in preharvest food safety. We describe the steps involved in developing mathematical models, different types of models, and their multiple applications. The introduction to modeling is followed by several sections that introduce the most common modeling approaches used in preharvest systems. We finish the chapter by outlining potential future directions for the field.

'Super' or just 'above average'? Supershedders and the transmission of Escherichia coli O157:H7 among feedlot cattle

Supershedders have been suggested to be major drivers of transmission of Escherichia coli O157:H7 (E. coli O157:H7) among cattle in feedlot environments, despite our relatively limited knowledge of the processes that govern periods of high shedding within an individual animal. In this study, we attempt a data-driven approach, estimating the key characteristics of high shedding behaviour, including effects on transmission to other animals, directly from a study of natural E. coli O157:H7 infection of cattle in a research feedlot, in order to develop an evidence-based definition of supershedding. In contrast to the hypothesized role of supershedders, we found that high shedding individuals only modestly increased the risk of transmission: individuals shedding over 10(3) cfu g(-1) faeces were estimated to pose a risk of transmission only 2.45 times greater than those shedding below that level. The data suggested that shedding above 10(3) cfu g(-1) faeces was the most appropriate definition of supershedding behaviour and under this definition supershedding was surprisingly common, with an estimated prevalence of 31.3% in colonized individuals. We found no evidence that environmental contamination by faeces of shedding cattle contributed to transmission over timescales longer than 3 days and preliminary evidence that higher stocking density increased the risk of transmission.

Impact of the shedding level on transmission of persistent infections in Mycobacterium avium subspecies paratuberculosis (MAP)

Super-shedders are infectious individuals that contribute a disproportionate amount of infectious pathogen load to the environment. A super-shedder host may produce up to 10,000 times more pathogens than other infectious hosts. Super-shedders have been reported for multiple human and animal diseases. If their contribution to infection dynamics was linear to the pathogen load, they would dominate infection dynamics. We here focus on quantifying the effect of super-shedders on the spread of infection in natural environments to test if such an effect actually occurs in Mycobacterium avium subspecies paratuberculosis (MAP). We study a case where the infection dynamics and the bacterial load shed by each host at every point in time are known. Using a maximum likelihood approach, we estimate the parameters of a model with multiple transmission routes, including direct contact, indirect contact and a background infection risk. We use longitudinal data from persistent infections (MAP), where infectious individuals have a wide distribution of infectious loads, ranging upward of three orders of magnitude. We show based on these parameters that the effect of super-shedders for MAP is limited and that the effect of the individual bacterial load is limited and the relationship between bacterial load and the infectiousness is highly concave. A 1000-fold increase in the bacterial contribution is equivalent to up to a 2-3 fold increase in infectiousness.

Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control

Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.

Infectious disease transmission and contact networks in wildlife and livestock

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.

Bovine tuberculosis: within-herd transmission models to support and direct the decision-making process

Use of mathematical models to study the transmission dynamics of infectious diseases is becoming increasingly common in veterinary sciences. However, modeling chronic infectious diseases such as bovine tuberculosis (bTB) is particularly challenging due to the substantial uncertainty associated with the epidemiology of the disease. Here, the methodological approaches used to model bTB and published in the peer-reviewed literature in the last decades were reviewed with a focus on the impact that the models' assumptions may have had on their results, such as the assumption of density vs. frequency-dependent transmission, the existence of non-infectious and non-detectable stages, and the effect of extrinsic sources of infection (usually associated with wildlife reservoirs). Although all studies suggested a relatively low rate of within-herd transmission of bTB when test-and-cull programs are in place, differences in the estimated length of the infection stages, sensitivity and specificity of the tests used and probable type of transmission (density or frequency dependent) were observed. Additional improvements, such as exploring the usefulness of contact-networks instead of assuming homogeneous mixing of animals, may help to build better models that can help to design, evaluate and monitor control and eradication strategies against bTB.

Transmission of Escherichia coli O157:H7 in cattle is influenced by the level of environmental contamination

A pen infection-transmission experiment was conducted to elucidate the role of pathogen strain and environmental contamination in transmission of Escherichia coli O157:H7 (ECO157) in cattle. Five steers were inoculated with a three-strain mixture of ECO157 and joined with five susceptible steers in each of two experimental replicates. Faecal and environmental samples were monitored for ECO157 presence over 30 days. One ECO157 strain did not spread. Transmission rates for the other two strains were estimated using a generalized linear model developed based on a modified ‘Susceptible–Infectious–Susceptible’ mathematical model. Transmission rates estimated for the two strains (0·11 and 0·14) were similar. However, the rates significantly (P = 0·0006) increased 1·5 times for every 1-unit increase in the level of environmental contamination measured as log₁₀ c.f.u. Depending on the level of environmental contamination, the estimated basic reproduction numbers varied from <1 to 8. The findings indicate the importance of on-farm measures to reduce environmental contamination for ECO157 control in cattle that should be validated under field conditions.

Temporal-spatial heterogeneity in animal-environment contact: implications for the exposure and transmission of pathogens

Contact structure, a critical driver of infectious disease transmission, is not completely understood and characterized for environmentally transmitted pathogens. In this study, we assessed the effects of temporal and spatial heterogeneity in animal contact structures on the dynamics of environmentally transmitted pathogens. We used real-time animal position data to describe contact between animals and specific environmental areas used for feeding and watering calves. The generated contact structure varied across days and among animals. We integrated animal and environmental heterogeneity into an agent-based simulation model for Escherichia coli O157 environmental transmission in cattle to simulate four different scenarios with different environmental bacteria concentrations at different areas. The simulation results suggest heterogeneity in environmental contact structure among cattle influences pathogen prevalence and exposure associated with each environment. Our findings suggest that interventions that target environmental areas, even relatively small areas, with high bacterial concentration can result in effective mitigation of environmentally transmitted pathogens.

Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis)

Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli. This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis.

A stochastic model for transmission, extinction and outbreak of Escherichia coli O157:H7 in cattle as affected by ambient temperature and cleaning practices

Many infectious agents transmitting through a contaminated environment are able to persist in the environment depending on the temperature and sanitation determined rates of their replication and clearance, respectively. There is a need to elucidate the effect of these factors on the infection transmission dynamics in terms of infection outbreaks and extinction while accounting for the random nature of the process. Also, it is important to distinguish between the true and apparent extinction, where the former means pathogen extinction in both the host and the environment while the latter means extinction only in the host population. This study proposes a stochastic-differential equation model as an approximation to a Markov jump process model, using Escherichia coli O157:H7 in cattle as a model system. In the model, the host population infection dynamics are described using the standard susceptible-infected-susceptible framework, and the E. coli O157:H7 population in the environment is represented by an additional variable. The backward Kolmogorov equations that determine the probability distribution and the expectation of the first passage time are provided in a general setting. The outbreak and apparent extinction of infection are investigated by numerically solving the Kolmogorov equations for the probability density function of the associated process and the expectation of the associated stopping time. The results provide insight into E. coli O157:H7 transmission and apparent extinction, and suggest ways for controlling the spread of infection in a cattle herd. Specifically, this study highlights the importance of ambient temperature and sanitation, especially during summer.

Investigating effects of between- and within-host variability on Escherichia coli O157 shedding pattern and transmission

Healthy cattle and their environment are the reservoir for the human pathogen Escherichia coli O157. In E. coli O157 epidemiology, supershedders have been loosely defined as cattle that shed high concentrations of E. coli O157 (≥ 10(4)colony-forming cells (CFU)/g of feces) at a single (or multiple) cross-section in time. Due to the variability in the pathogen shedding level among animals (between-host variability), as well as fluctuations in the level shed by a single animal (within-host variability), it is difficult to interpret fecal bacteria distributions, as well as to parse the relative contribution of between- and within-host variability to the observed shedding patterns at the pen level. We developed an agent-based model that integrates individual animal data on temporal fecal shedding dynamics with pen-level E. coli O157 transmission to study how the temporal (and aggregation) patterns of E. coli O157 shedding loads and prevalence arise at the pen level. We demonstrated that even without between-host variability, the prevalence of animals with concentration of E. coli O157 in feces that exceeds 10(4)CFU/g is similar to that observed in cross-sectional field data. Both within-host and between-host variability can generate supershedders.

Adding the spatial dimension to the social network analysis of an epidemic: investigation of the 2007 outbreak of equine influenza in Australia

Equine influenza is a highly contagious and widespread viral respiratory disease of horses and other equid species, characterised by fever and a harsh dry cough. In 2007, in the first reported outbreak in Australia, the virus spread through the horse populations of two states within 4 months. Most of the geographic spread occurred within the first 10 days and was associated with the movement of infected horses prior to the implementation of movement controls. This study applies social network analysis to describe spread of equine influenza between horse premises infected in the early outbreak period, identifying spread occurring through a contact network and secondary local spatial spread. Social networks were constructed by combining contact-tracing data on horse movements with a distance matrix between all premises holding horses infected within the first 10 days of the outbreak. These networks were analysed to provide a description of the epidemic, identify premises that were central to disease spread and to estimate the relative proportion of premises infected through infected horse movements and through local spatial spread. We then explored the effect of distance on disease spread by estimating the range of local spread (through direct contact, transmission on fomites and windborne transmission) based on the level of fragmentation in the network and also by directly estimating the shape of the outbreak's spatial transmission kernel. During the first 10 days of this epidemic, 197 horse premises were infected; 70 of these were included in the contact-traced network. Most local spread occurred within 5 km. Local spread was estimated to have occurred up to a distance of 15.3 km - based on the contact-and-proximity network - and at a very low incidence beyond this distance based on the transmission kernel estimate. Of the 70 premises in the contact network, spread to 14 premises (95% CI: 9, 20 premises) was likely to have occurred through local spatial spread from nearby infected premises, suggesting that 28.3% of spread in the early epidemic period was 'network-associated' (95% CI: 25.6, 31.0%). By constructing a 'maximal network' of contact and proximity (based on a distance cut-off of 15.3 km), 44 spatial clusters were described, and the horse movements that initiated infection in these locations were identified. Characteristics of the combined network, incorporating both spatial and underlying contact relationships between infected premises, explained the high rate of spread, the sequence of cluster formation and the widespread dispersal experienced in the early phase of this epidemic. These results can inform outbreak control planning by guiding the imposition of appropriate control zone diameters around infected premises and the targeting of surveillance and interventions.

The influence of empirical contact networks on modelling diseases in cattle

We present two stochastic models of the passage of an SEIR (susceptible-latent-infected-resistant) disease through herds of cattle. One model is based on a contact network constructed via continuously recorded interaction data from two herds of cattle, the other, a matching network constructed using the principles of mass-action mixing. The recorded contact data were produced by attaching proximity data loggers to two separate herds of cattle during two separate recording periods. The network constructed using the principles of mass-action mixing uses the same number of contacts as the recorded network but distributes them randomly amongst the animals. The recorded networks had a greater number of repeated contacts, lower closeness and clustering scores and greater average path length than the mass-action networks. A lower proportion of simulations of the recorded network produce any disease spread when compared to those simulations of the mass-action network and, of those that did, fewer infected animals were predicted. For all parameter values tested, within the sensitivity analysis, similar differences were found between the recorded and mass-action network models.

Exploiting strain diversity to expose transmission heterogeneities and predict the impact of targeting supershedding

When a few individuals generate disproportionately many secondary cases, targeted interventions can theoretically lead to highly efficient control of the spread of infection. Practical exploitation of heterogeneous transmission requires the sources of variability to be quantified, yet it is unusual to have empirical data of sufficient resolution to distinguish their effects. Here, we exploit extensive data on pathogen shedding densities and the distribution of cases, collected from the same population within the same spatio-temporal window, to expose the comparative epidemiology of independent Escherichia coli O157 strains. For this zoonotic pathogen, which exhibits high-density shedding (supershedding) and heterogeneous transmission in its cattle reservoir, whether targeting supershedding could be an effective control depends critically on the proposed link between shedding density and transmissibility. We substantiate this link by showing that our supershedder strain has nearly triple the R(0) of our non-supershedder strain. We show that observed transmission heterogeneities are strongly driven by superspreading in addition to supershedding, but that for the supershedder strain, the dominant strain in our study population, there remains sufficient heterogeneity in contribution to R(0) from different shedding densities to allow exploitation for control. However, in the presence of substantial within-host variability, our results indicate that rather than seek out supershedders themselves, the most effective controls would directly target the phenomenon of pathogen supershedding with the aim of interrupting or preventing high shedding densities. In this system, multiple sources of heterogeneity have masked the role of shedding densities-our potential targets for control. This analysis demonstrates the critical importance of disentangling the effects of multiple sources of heterogeneity when designing targeted interventions.

Modeling on-farm Escherichia coli O157:H7 population dynamics

Escherichia coli O157:H7 is a potentially fatal foodborne pathogen with a putative reservoir for human infection in feedlot cattle. In order to more effectively identify targets for intervention strategies, we aimed to (1) assess the role of various feedlot habitats in E. coli O157:H7 propagation and (2) provide a framework for examining the relative contributions of animals and the surrounding environment to observed pathogen dynamics. To meet these goals we developed a mathematical model based on an ecological metapopulation framework to track bacterial population dynamics inside and outside the host. We used E. coli O157:H7 microbiological and epidemiological literature to characterize E. coli O157:H7 habitats at the pen level and account for E. coli O157:H7 population processes in water troughs, feedbunks, cattle hosts, and pen floors in the model. Simulations indicated that E. coli O157:H7 was capable of maintaining viable populations in the feedlot without net growth in the cattle gastrointestinal tract, suggesting E. coli O157:H7 may not always act as an obligate parasite. Water troughs and contaminated pen floors appeared to be particularly influential sources driving E. coli O157:H7 population dynamics and thus would serve as prime environmental targets for interventions to effectively reduce the E. coli O157:H7 load at the pen level.

Disease spread in small-size directed networks: epidemic threshold, correlation between links to and from nodes, and clustering

Network epidemiology has mainly focused on large-scale complex networks. It is unclear whether findings of these investigations also apply to networks of small size. This knowledge gap is of relevance for many biological applications, including meta-communities, plant-pollinator interactions and the spread of the oomycete pathogen Phytophthora ramorum in networks of plant nurseries. Moreover, many small-size biological networks are inherently asymmetrical and thus cannot be realistically modelled with undirected networks. We modelled disease spread and establishment in directed networks of 100 and 500 nodes at four levels of connectance in six network structures (local, small-world, random, one-way, uncorrelated, and two-way scale-free networks). The model was based on the probability of infection persistence in a node and of infection transmission between connected nodes. Regardless of the size of the network, the epidemic threshold did not depend on the starting node of infection but was negatively related to the correlation coefficient between in- and out-degree for all structures, unless networks were sparsely connected. In this case clustering played a significant role. For small-size scale-free directed networks to have a lower epidemic threshold than other network structures, there needs to be a positive correlation between number of links to and from nodes. When this correlation is negative (one-way scale-free networks), the epidemic threshold for small-size networks can be higher than in non-scale-free networks. Clustering does not necessarily have an influence on the epidemic threshold if connectance is kept constant. Analyses of the influence of the clustering on the epidemic threshold in directed networks can also be spurious if they do not consider simultaneously the effect of the correlation coefficient between in- and out-degree.

Social network analysis. Review of general concepts and use in preventive veterinary medicine

Social network analysis (SNA) and graph theory have been used widely in sociology, psychology, anthropology, biology and medicine. Social network analysis and graph theory provide a conceptual framework to study contact patterns and to identify units of analysis that are frequently or intensely connected within the network. Social network analysis has been used in human epidemiology as a tool to explore the potential transmission of infectious agents such as HIV, tuberculosis, hepatitis B and syphilis. In preventive veterinary medicine, SNA is an approach that offers benefits for exploring the nature and extent of the contacts between animals or farms, which ultimately leads to a better understanding of the potential risk for disease spread in a susceptible population. Social network analysis, however, has been applied only recently in preventive veterinary medicine, therefore the characteristics of the technique and the potential benefits of its use remain unknown for an important section of the international veterinary medicine community. The objectives of this paper were to review the concepts and theoretical aspects underlying the use of SNA and graph theory, with particular emphasis on their application to the study of infectious diseases of animals. The paper includes a review of recent applications of SNA in preventive veterinary medicine and a discussion of the potential uses and limitations of this methodology for the study of animal diseases.

Contact networks in a wildlife-livestock host community: identifying high-risk individuals in the transmission of bovine TB among badgers and cattle

BACKGROUND

The management of many pathogens, which are of concern to humans and their livestock, is complicated by the pathogens' ability to cross-infect multiple host species, including wildlife. This has major implications for the management of such diseases, since the dynamics of infection are dependent on the rates of both intra- and inter-specific transmission. However, the difficulty of studying transmission networks in free-living populations means that the relative opportunities for intra- versus inter-specific disease transmission have not previously been demonstrated empirically within any wildlife-livestock disease system.

METHODOLOGY/PRINCIPAL FINDINGS

Using recently-developed proximity data loggers, we quantify both intra- and inter-specific contacts in a wildlife-livestock disease system, using bovine tuberculosis (bTB) in badgers and cattle in the UK as our example. We assess the connectedness of individuals within the networks in order to identify whether there are certain 'high-risk' individuals or groups of individuals for disease transmission within and between species. Our results show that contact patterns in both badger and cattle populations vary widely, both between individuals and over time. We recorded only infrequent interactions between badger social groups, although all badgers fitted with data loggers were involved in these inter-group contacts. Contacts between badgers and cattle occurred more frequently than contacts between different badger groups. Moreover, these inter-specific contacts involved those individual cows, which were highly connected within the cattle herd.

CONCLUSIONS/SIGNIFICANCE

This work represents the first continuous time record of wildlife-host contacts for any free-living wildlife-livestock disease system. The results highlight the existence of specific individuals with relatively high contact rates in both livestock and wildlife populations, which have the potential to act as hubs in the spread of disease through complex contact networks. Targeting testing or preventive measures at high-contact groups and individuals within livestock populations would enhance the effectiveness and efficiency of disease management strategies.

Super-shedding and the link between human infection and livestock carriage of Escherichia coli O157

Cattle that excrete more Escherichia coli O157 than others are known as super-shedders. Super-shedding has important consequences for the epidemiology of E. coli O157 in cattle--its main reservoir--and for the risk of human infection, particularly owing to environmental exposure. Ultimately, control measures targeted at super-shedders may prove to be highly effective. We currently have only a limited understanding of both the nature and the determinants of super-shedding. However, super-shedding has been observed to be associated with colonization at the terminal rectum and might also occur more often with certain pathogen phage types. More generally, epidemiological evidence suggests that super-shedding might be important in other bacterial and viral infections.