Diseases In Animal Populations Research Articles

MATHEMATICAL ANALYSIS OF TRANSMISSION DYNAMICS AND CONTROL OF SALMONELLA BACTERIAL INFECTION

The most prevalent health issue is salmonella infection. Every year, between 200 million and 1.3 billion people are infected with Salmonella. Salmonella spreads by direct animal contact, food and drink, and very infrequently, person-to-person contact via the fecal-oral route. It is estimated that 94% of salmonellosis transmission occurs through food. Salmonella infection is, in fact, a bacterial infection. It is crucial to utilize mathematical modeling to describe how biological and biomedical systems behave dynamically. The SIRS model of epidemics is the most often used mathematical model. Describing the patterns of Salmonella infectious disease in animal populations and the human population, we have formulated a SIRS epidemic model in our work. In significant part, the model was developed as a collection of ODEs based on the traits of transmission of infection. The occurrence of infection-free and endemic stable states was initially noted, and stability of the critical points of the model was later proven (infection-free and endemic). Following that, the fundamental reproduction number was calculated using the method of a next-generation matrix. Finally, the numerical simulations of this SIRS model have been completed. The analytical finding is explained using numerical simulations.

Isolation and characterisation of Brucella melitensis by bacteriological and molecular methods from livestock in North Cyprus

In this study, the isolation, biotyping and molecular characterisation of Brucella melitensis from cattle, sheep and goats in North Cyprus are reported on. A total of 319 raw milk samples obtained from seropositive dairy livestock (190 cattle, 74 sheep and 55 goats) and tissue samples including the liver, spleen and abomasal contents obtained from 32 aborted foetal samples (5 cattle, 18 sheep and 9 goats) were analysed for the presence and characterisation of the agent. B. melitensis was isolated and identified from 90 out of 319 (28.2%) milk and 19 out of 32 (59.4%) foetal samples by conventional bacteriological methods. Identification of all 109 isolates was confirmed by using real-time PCR with genus and species-specific primers. Following the preliminary identification, 27 selected isolates representing various counties and herds were further analysed by conventional methods. Twenty (74.1%) isolates were identified as B. melitensis biovar 1 and seven (25.9%) were identified as B. melitensis biovar 3. The Bruce-ladder multiplex PCR assay revealed that all the isolates were field strains. The results of the present study confirmed the presence of B. melitensis in livestock including the cattle population in North Cyprus. Even though the majority of the samples came from seropositive cattle, Brucella abortus was not isolated in the study. The results also revealed the potential public health risk of brucellosis in livestock emphasising the need of implementing strict control and eradication strategies against the disease in animal populations in order to protect human health.

Data-driven agent-based model building for animal movement through Exploratory Data Analysis

Fields studying animal movement are data-starved due to large monetary and time costs of data collection, and, moreover, not all quantities of interest can be measured. Despite limited data, diverse models have been developed to study animal movement. Many of these models are based on random walks that use Gaussian noise. Examinations of real movement data show that the assumptions made in these models are not always valid on all spatial and time scales, and these shortcomings suggest that new models may be needed. We provide methods for developing and training more realistic models, specifically agent-based models of animal movement, by taking a data-driven approach. We took an exploratory data analysis (EDA) approach that allowed us to develop a model that best reproduced the movement patterns we observed in a real dataset of animal locations. In our EDA, we examined distributions of positions, calculated the autocorrelation of the movement data, used Fourier analysis, calculated mean-squared displacements, and tested for correlations. We introduce the fused-lasso regression-analysis method for identifying large and sudden changes in position through a non-parametric fit that is sensitive to discontinuities. We introduce the copula and kernel-density estimates as methods for approximating coordinate-system-independent movement correlations from the marginal location differences represented in our data set; use of the copula allows us to create correlated, non-Gaussian noise. With the insights gained from our EDA, we created a Langevin model that describes the movements of an individual animal, features non-Gaussian noise and incorporates multiple movement patterns. We extended this model to an agent-based model that describes the movements of several groups of deer. We compared our Langevin model to three models built on different assumptions that result in substantial differences in the area covered by an animal. Using our agent-based model, we simulated three groups of deer with parameters sampled from our data to illustrate the amount of area covered by deer in our model and the ability of groups to overlap in space. This overlapping behavior is an important feature of models of processes driven by interactions between animals, such as infectious diseases; our model could be useful for studying the spread of infectious diseases in animal populations, such as chronic wasting disease. While we have applied our data-driven methods to animal movement, these methods are widely applicable to developing and training models using a wide range of data sources.

A systematic review of waterborne and water-related disease in animal populations of Florida from 1999-2019.

Florida's waters are a reservoir for a host of pathogens and toxins. Many of these microorganisms cause water-related diseases in people that are reportable to the Florida Department of Health. Our objective in this review was to ascertain which water-related pathogens and toxins of public health importance have been found in animal populations in Florida over the last twenty years. Nineteen databases were searched, including PubMed and Web of Science Core Collection, using keywords and search terms for the waterborne diseases, water-related vector-borne diseases, and water-based toxins reportable to the Florida Department of Health. For inclusion, peer-reviewed journal articles were to be written in English, published between January 1, 1999 and December 31, 2019, and contain primary research findings documenting at least one of the water-related pathogens or toxins of interest in an animal population within Florida during this same time frame. Of over eight thousand initial search results, 65 studies were included for final analysis. The most common animal types implicated in the diseases of interest included marine mammals, fish and shellfish, wild birds, and livestock. Toxins or pathogens most often associated with these animals included toxin-producer Karenia brevis, vibriosis, Escherichia coli, and Salmonellosis. Findings from this review elucidate the water-related disease-causing pathogens and toxins which have been reported within animal populations in recent Florida history. As most of these diseases are zoonotic, our results suggest a One Health approach is necessary to support and maintain healthy water systems throughout the state of Florida for the protection of both human and animal populations.

Factors associated with cattle necropsy submissions in Switzerland, and their importance for surveillance

Pathology data have been reported to be important for surveillance, as they are crucial for correctly recognizing and identifying new or re-emerging diseases in animal populations. However, there are no reports in the literature of necropsy data being compared or complemented with other data. In our study, we compared cattle necropsy reports extracted from 3 laboratories with the Swiss fallen stock data and clinical data collected by the association of Swiss Cattle Breeders. The objective was to assess the completeness, validity and representativeness of the necropsy data, as well as evaluate potential factors for necropsy submission and how they can benefit animal health surveillance. Our results showed that, on average, 1% of Swiss cattle that die are submitted for post-mortem examinations. However, different factors influence cattle necropsy submissions, such as the age of the animal, the geographical location and the number of sick and/or dead animals on the farm. There was a median of five animals reported sick and two animals reported dead within 30 days prior to a necropsy submission, providing quantitative evidence of a correlation between on farm morbidity/mortality and post-mortem examination. Our results also showed that necropsy data can help improve the accuracy and completeness of health data for surveillance systems. In this study, we were able to demonstrate the importance of veterinary pathology data for AHS by providing quantitative evidence that necropsied animals are indicative of farms with important disease problems and are therefore critically important for surveillance. Furthermore, thanks to the amount of information provided by combined data sources, the epidemiology (e.g. season, geographic region, risk factors) of potential diseases can be analysed more precisely and help supporting animal health surveillance systems.

Efficient correction of a deleterious point mutation in primary horse fibroblasts with CRISPR-Cas9

Phenotypic selection during animal domestication has resulted in unwanted incorporation of deleterious mutations. In horses, the autosomal recessive condition known as Glycogen Branching Enzyme Deficiency (GBED) is the result of one of these deleterious mutations (102C > A), in the first exon of the GBE1 gene (GBE1102C>A). With recent advances in genome editing, this type of genetic mutation can be precisely repaired. In this study, we used the RNA-guided nuclease CRISPR-Cas9 (clustered regularly-interspaced short palindromic repeats/CRISPR-associated protein 9) to correct the GBE1102C>A mutation in a primary fibroblast cell line derived from a high genetic merit heterozygous stallion. To correct this mutation by homologous recombination (HR), we designed a series of single guide RNAs (sgRNAs) flanking the mutation and provided different single-stranded donor DNA templates. The distance between the Cas9-mediated double-stranded break (DSB) to the mutation site, rather than DSB efficiency, was the primary determinant for successful HR. This framework can be used for targeting other harmful diseases in animal populations.

Distribution of Brucella field strains isolated from livestock, wildlife populations, and humans in Italy from 2007 to 2015.

Brucellosis is a major public health problem still prevalent as a neglected endemic zoonosis requiring proactive attention in many communities worldwide. The present study involved analysis of Brucella field strains submitted for typing to the Italian National Reference Laboratory for Brucellosis from 2007 to 2015. Strains were identified at the species and biovar levels by classic and molecular techniques according to the World Organisation for Animal Health Manual. In total, 5,784 strains were typed: 3,089 Brucella abortus (53.4%), 2,497 B. melitensis (43.2%), 10 B. ovis (0.2%), 181 B. suis (3.1%), and 7 B. ceti (0.1%). The 2,981 strains from cattle were typed as B. abortus biovars 1, 3, and 6 (90.1%) and B. melitensis biovar 3 (9.9%). The 318 strains from water buffalo were typed as B. abortus biovars 1, 3 (95.9%) and B. melitensis biovar 3 (4.1%). The 2,279 strains from sheep and goats were typed as B. abortus biovars 1 and 3 (4.3%); B. melitensis biovars 1, 3, (95.3%); and B. ovis (0.4%). The 173 strains from wild boar were typed as B. suis biovar 2 (98.3%) and B. melitensis biovar 3 (1.7%). The 11 strains from pigs were typed as B. suis biovar 2. The 13 strains from humans were typed as B. melitensis biovar 3. The two strains from horses were typed as B. abortus biovar 1, while the seven strains from dolphins were typed as B. ceti. This additional knowledge on the epidemiology of brucellosis in Italy may be useful to formulate policies and strategies for the control and eradication of the disease in animal populations. The animal species affected, biovars typed, geographical origins, and spatial distributions of isolates are herein analyzed and discussed.

Inferring the Ecological Niche of Toxoplasma gondii and Bartonella spp. in Wild Felids.

Traditional epidemiological studies of disease in animal populations often focus on directly transmitted pathogens. One reason pathogens with complex lifecycles are understudied could be due to challenges associated with detection in vectors and the environment. Ecological niche modeling (ENM) is a methodological approach that overcomes some of the detection challenges often seen with vector or environmentally dependent pathogens. We test this approach using a unique dataset of two pathogens in wild felids across North America: Toxoplasma gondii and Bartonella spp. in bobcats (Lynx rufus) and puma (Puma concolor). We found three main patterns. First, T. gondii showed a broader use of environmental conditions than did Bartonella spp. Also, ecological niche models, and Normalized Difference Vegetation Index satellite imagery, were useful even when applied to wide-ranging hosts. Finally, ENM results from one region could be applied to other regions, thus transferring information across different landscapes. With this research, we detail the uncertainty of epidemiological risk models across novel environments, thereby advancing tools available for epidemiological decision-making. We propose that ENM could be a valuable tool for enabling understanding of transmission risk, contributing to more focused prevention and control options for infectious diseases.

Demographics of dogs, cats, and rabbits attending veterinary practices in Great Britain as recorded in their electronic health records

BackgroundUnderstanding the distribution and determinants of disease in animal populations must be underpinned by knowledge of animal demographics. For companion animals, these data have been difficult to collect because of the distributed nature of the companion animal veterinary industry. Here we describe key demographic features of a large veterinary-visiting pet population in Great Britain as recorded in electronic health records, and explore the association between a range of animal’s characteristics and socioeconomic factors.ResultsElectronic health records were captured by the Small Animal Veterinary Surveillance Network (SAVSNET), from 143 practices (329 sites) in Great Britain. Mixed logistic regression models were used to assess the association between socioeconomic factors and species and breed ownership, and preventative health care interventions. Dogs made up 64.8% of the veterinary-visiting population, with cats, rabbits and other species making up 30.3, 2.0 and 1.6% respectively. Compared to cats, dogs and rabbits were more likely to be purebred and younger. Neutering was more common in cats (77.0%) compared to dogs (57.1%) and rabbits (45.8%). The insurance and microchipping relative frequency was highest in dogs (27.9 and 53.1%, respectively). Dogs in the veterinary-visiting population belonging to owners living in least-deprived areas of Great Britain were more likely to be purebred, neutered, insured and microchipped. The same association was found for cats in England and for certain parameters in Wales and Scotland.ConclusionsThe differences we observed within these populations are likely to impact on the clinical diseases observed within individual veterinary practices that care for them. Based on this descriptive study, there is an indication that the population structures of companion animals co-vary with human and environmental factors such as the predicted socioeconomic level linked to the owner’s address. This ‘co-demographic’ information suggests that further studies of the relationship between human demographics and pet ownership are warranted.

CRISPR-Cas Defense System and Potential Prophages in Cyanobacteria Associated with the Coral Black Band Disease.

Understanding how pathogens maintain their virulence is critical to developing tools to mitigate disease in animal populations. We sequenced and assembled the first draft genome of Roseofilum reptotaenium AO1, the dominant cyanobacterium underlying pathogenicity of the virulent coral black band disease (BBD), and analyzed parts of the BBD-associated Geitlerinema sp. BBD_1991 genome in silico. Both cyanobacteria are equipped with an adaptive, heritable clustered regularly interspaced short palindromic repeats (CRISPR)-Cas defense system type I-D and have potential virulence genes located within several prophage regions. The defense system helps to prevent infection by viruses and mobile genetic elements via identification of short fingerprints of the intruding DNA, which are stored as templates in the bacterial genome, in so-called “CRISPRs.” Analysis of CRISPR target sequences (protospacers) revealed an unusually high number of self-targeting spacers in R. reptotaenium AO1 and extraordinary long CRIPSR arrays of up to 260 spacers in Geitlerinema sp. BBD_1991. The self-targeting spacers are unlikely to be a form of autoimmunity; instead these target an incomplete lysogenic bacteriophage. Lysogenic virus induction experiments with mitomycin C and UV light did not reveal an actively replicating virus population in R. reptotaenium AO1 cultures, suggesting that phage functionality is compromised or excision could be blocked by the CRISPR-Cas system. Potential prophages were identified in three regions of R. reptotaenium AO1 and five regions of Geitlerinema sp. BBD_1991, containing putative BBD relevant virulence genes, such as an NAD-dependent epimerase/dehydratase (a homolog in terms of functionality to the third and fourth most expressed gene in BBD), lysozyme/metalloendopeptidases and other lipopolysaccharide modification genes. To date, viruses have not been considered to be a component of the BBD consortium or a contributor to the virulence of R. reptotaenium AO1 and Geitlerinema sp. BBD_1991. We suggest that the presence of virulence genes in potential prophage regions, and the CRISPR-Cas defense systems are evidence of an arms race between the respective cyanobacteria and their bacteriophage predators. The presence of such a defense system likely reduces the number of successful bacteriophage infections and mortality in the cyanobacteria, facilitating the progress of BBD.

Using social network analysis to inform disease control interventions

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

Investigation of the Occurrence of Porcine Reproductive and Respiratory Virus in Swine Herds Participating in an Area Regional Control and Elimination Project in Ontario, Canada.

The main goal of this study was to investigate the occurrence of porcine reproductive and respiratory syndrome virus (PRRSV)-specific genotypes in swine sites in Ontario (Canada) using molecular, spatial and network data from a porcine reproductive and respiratory syndrome (PRRS) regional control project. For each site, location, animal movement service provider (truck companies), PRRSV status and sequencing data of the open reading frame 5 (ORF5) were obtained. Three-kilometre buffers were created to evaluate neighbourhood characteristics for each site. Social network analysis was conducted on swine sites and trucking companies to assemble the network and define network components. Three different PRRSV genotypes were used as outcomes for statistical analysis based on the region's phylogenetic tree of the ORF5. Multivariable exact logistic regression was conducted to investigate the association between being positive for a specific genotype and two main exposures of interest: (i) having at least one neighbour within threekm also positive for the same genotype outside the production system and (ii) having at least one positive site for the same genotype in the same truck network component outside the production system. Results showed that the importance of area spread and truck network on PRRSV occurrence differed according to genotype. Additionally, the Ontario PRRS database appears suitable for conducting regional disease investigations. Finally, the use of relatively new tools available for network, spatial and molecular analysis could be useful in investigation, control and prevention of endemic infectious diseases in animal populations.

Ungulates as model systems for the study of disease processes in natural populations.

Parasites and pathogens are a fundamental driving force in the ecology and evolution of mammalian populations, and understanding disease processes in natural populations is an urgent priority in the face of increased rates of infectious disease emergence. In this review, we argue that mammalogists are uniquely placed to contribute to addressing these challenges because in-depth knowledge of mammal species is fundamental to the development of wild model systems that could accelerate discovery in disease ecology. The use of animal models—species for which a broad range of diagnostic, molecular, and genetic tools have been developed—in tightly controlled laboratory environments has been instrumental in driving progress in the biomedical sciences. However, in natural populations, disease processes operate in the context of enormous genetic, phenotypic, and environmental variability. Understanding diseases in animal populations (including humans) thus requires investment in “wild animal models” that explicitly include individual variation and relevant environmental gradients. Wild mammal groups such as primates and rodents have already been identified as potentially useful models of infectious diseases in the wild. Here, we discuss the enormous potential that ungulates hold as candidates for wild model systems. The diversity, broad geographic distribution, and often high abundance of species in this group make them a highly accessible target for disease research. Moreover, a depth of background knowledge, close relationships to domesticated animals, and ongoing management of many wild ungulate species provide context, tools, and opportunity for cutting-edge research at the interface of ecological and biomedical sciences. Studies of wild ungulates are already helping to unravel some key challenges in infectious disease research, including the role of parasites in trophic cascades, the consequences of climate change for disease dynamics, and the systems biology of host–parasite interactions. Other areas where ungulate studies may provide new insight include research on the sources and drivers of emerging infectious diseases.

Using exceedance probabilities to detect anomalies in routinely recorded animal health data, with particular reference to foot-and-mouth disease in Viet Nam

The widespread availability of computer hardware and software for recording and storing disease event information means that, in theory, we have the necessary information to carry out detailed analyses of factors influencing the spatial distribution of disease in animal populations. However, the reliability of such analyses depends on data quality, with anomalous records having the potential to introduce significant bias and lead to inappropriate decision making. In this paper we promote the use of exceedance probabilities as a tool for detecting anomalies when applying hierarchical spatio-temporal models to animal health data. We illustrate this methodology through a case study data on outbreaks of foot-and-mouth disease (FMD) in Viet Nam for the period 2006–2008. A flexible binomial logistic regression was employed to model the number of FMD infected communes within each province of the country. Standard analyses of the residuals from this model failed to identify problems, but exceedance probabilities identified provinces in which the number of reported FMD outbreaks was unexpectedly low. This finding is interesting given that these provinces are on major cattle movement pathways through Viet Nam.

Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations.

In this globalized world, the spread of new, exotic and re-emerging diseases has become one of the most important threats to animal production and public health. This systematic review analyses conventional and novel early detection methods applied to surveillance. In all, 125 scientific documents were considered for this study. Exotic (n = 49) and re-emerging (n = 27) diseases constituted the most frequently represented health threats. In addition, the majority of studies were related to zoonoses (n = 66). The approaches found in the review could be divided in surveillance modalities, both active (n = 23) and passive (n = 5); and tools and methodologies that support surveillance activities (n = 57). Combinations of surveillance modalities and tools (n = 40) were also found. Risk-based approaches were very common (n = 60), especially in the papers describing tools and methodologies (n = 50). The main applications, benefits and limitations of each approach were extracted from the papers. This information will be very useful for informing the development of tools to facilitate the design of cost-effective surveillance strategies. Thus, the current literature review provides key information about the advantages, disadvantages, limitations and potential application of methodologies for the early detection of new, exotic and re-emerging diseases.

Towards One Health: Increasing Awareness of Animal Health Among Public Health Stakeholders

The recent focus on the 'One Medicine' concept has resulted in an increased awareness that the control of diseases in animal populations, whether zoonotic or not, can be of great public health importance. However, awareness of the activities developed in the field of animal health is still low among public health workers. In this roundtable the facilitators will encourage discussion regarding what can public and animal health learn from each other, focusing particularly on how this cooperation can be promoted.

Transmission Patterns of Pinworms in Two Sympatric Congeneric Primate Species

Understanding pathogen transmission is essential to addressing the dynamics of infectious diseases in animal populations. Directly transmitted parasites spread in host populations via 1) contact with infected individuals and 2) contact with contaminated substrates. Although studies exist that support social or ranging effects on transmission, it is less clear how these factors interact. We test the hypothesis that a combination of social, ranging, diet, and intrinsic factors account for Trypanoxyuris minutus (pinworm) infections in sympatric howler species Alouatta palliata and A. pigra. We collected 211 howler fecal samples from 34 adults living in four groups, two of each species, in Tabasco (Mexico), and calculated pinworm prevalence and eggs per gram of feces (EPG). We followed each group for 80 h to determine ranging, diet, frequency of contact, and conspecific proximity. Prevalence of Trypanoxyurisminutus was high, with 82% of all individuals infected. Logistic modeling indicated that pinworm prevalence was positively associated with proximity and the proportion of group members contacted by focal individuals. Although EPG results should be interpreted cautiously owing to variable egg excretion, this index was also positively associated with proximity and the proportion of group members that were contacted, as well as with dietary diversity and use of non-tree foods. Neither intrinsic factors such as species and sex, nor group and population level variables, such as group and home range size, home range overlap, and intensity of range use, were significant predictors of pinworm infection. We conclude that both sociality and feeding behavior are key factors in infection dynamics of Trypanoxyuris minutus in sympatric Alouatta palliata and A. pigra, confirming that contact with infected conspecifics and contaminated substrates are important mechanisms for directly transmitted parasites.

InterSpread Plus: a spatial and stochastic simulation model of disease in animal populations

We describe the spatially explicit, stochastic simulation model of disease spread, InterSpread Plus, in terms of its epidemiological framework, operation, and mode of use. The input data required by the model, the method for simulating contact and infection spread, and methods for simulating disease control measures are described. Data and parameters that are essential for disease simulation modelling using InterSpread Plus are distinguished from those that are non-essential, and it is suggested that a rational approach to simulating disease epidemics using this tool is to start with core data and parameters, adding additional layers of complexity if and when the specific requirements of the simulation exercise require it.We recommend that simulation models of disease are best developed as part of epidemic contingency planning so decision makers are familiar with model outputs and assumptions and are well-positioned to evaluate their strengths and weaknesses to make informed decisions in times of crisis.

家畜衛生分野における実践疫学教育

Important animal diseases, such as BSE, highly pathogenic avian influenza and foot and mouth disease, occurred in Japan in recent years. These diseases severely damaged livestock industries and also increased social concerns about food safety and public health. With increasing the importance of animal health in the general public as well as the livestock industries, the role of “epidemiology” has been expected as a useful tool to understand and analyze disease situations and to support decision making for control and prevention of animal diseases. To meet these expectations, the veterinary epidemiology training course for local and national veterinary officials in working for animal health is held annually. The training course is sponsored by the Ministry of Agriculture, Forestry and Fisheries, and managed by the National Institute of Animal Health. The aim of this training course is to provide basic principles and skills of veterinary epidemiology applicable to animal disease control activities. During the training course, trainees firstly learn basic methods to analyze and interpret field data, to evaluate diagnostic tests, and to design observational studies and surveys using sampling methods. Then, they learn how to apply basic epidemiological methods to problems through practical exercises. These include outbreak investigations and epidemiological studies of diseases in animal populations. This training course provides good opportunities to brush up and develop appropriate knowledge and skills of epidemiology for veterinarians in the animal health field.

Use of data mining techniques to investigate disease risk classification as a proxy for compromised biosecurity of cattle herds in Wales

BackgroundBiosecurity is at the forefront of the fight against infectious diseases in animal populations. Few research studies have attempted to identify and quantify the effectiveness of biosecurity against disease introduction or presence in cattle farms and, when done, they have relied on the collection of on-farm data. Data on environmental, animal movement, demographic/husbandry systems and density disease determinants can be collated without requiring additional specific on-farm data collection activities, since they have already been collected for some other purposes. The aim of this study was to classify cattle herds according to their risk of disease presence as a proxy for compromised biosecurity in the cattle population of Wales in 2004 for risk-based surveillance purposes.ResultsThree data mining methods have been applied: logistic regression, classification trees and factor analysis. Using the cattle holding population in Wales, a holding was considered positive if at least bovine TB or one of the ten most frequently diagnosed infectious or transmissible non-notifiable diseases in England and Wales, according to the Veterinary Investigation Surveillance Report (VIDA) had been diagnosed in 2004. High-risk holdings can be described as open large cattle herds located in high-density cattle areas with frequent movements off to many locations within Wales. Additional risks are associated with the holding being a dairy enterprise and with a large farming area.ConclusionThis work has demonstrated the potential of mining various livestock-relevant databases to obtain generic criteria for individual cattle herd biosecurity risk classification. Despite the data and analytical constraints the described risk profiles are highly specific and present variable sensitivity depending on the model specifications. Risk profiling of farms provides a tool for designing targeted surveillance activities for endemic or emerging diseases, regardless of the prior amount of information available on biosecurity at farm level. As the delivery of practical evidence-based information and advice is one of the priorities of Defra's new Animal Health and Welfare Strategy (AHWS), data-driven models, derived from existing databases, need to be developed that can then be used to inform activities during outbreaks of endemic diseases and to help design surveillance activities.