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

<p>Abstract</p> <p>Background</p> <p>Biosecurity 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.</p> <p>Results</p> <p>Three 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.</p> <p>Conclusion</p> <p>This 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.</p

Application of knowledge-driven spatial modelling approaches and uncertainty management to a study of Rift Valley fever in Africa

BACKGROUND: There are few studies that have investigated uncertainties surrounding the scientific community's knowledge of the geographical distribution of major animal diseases. This is particularly relevant to Rift Valley fever (RVF), a zoonotic disease causing destructive outbreaks in livestock and man, as the geographical range of the disease is widening to involve previously unaffected regions. In the current study we investigate the application of methods developed in the decision sciences: multiple criteria decision making using weighted linear combination and ordered weighted averages, and Dempster-Shafer theory, implemented within the geographical information system IDRISI, to obtain a greater understanding of uncertainty related to the geographical distribution of RVF. The focus is on presenting alternate methods where extensive field data are not available and traditional, model-based approaches to disease mapping are impossible to conduct. RESULTS: Using a compensatory multiple criteria decision making model based on weighted linear combination, most of sub-Saharan Africa was suitable for endemic circulation of RVF. In contrast, areas where rivers and lakes traversed semi-arid regions, such as those bordering the Sahara, were highly suitable for RVF epidemics and wet, tropical areas of central Africa had low suitability. Using a moderately non-compensatory model based on ordered weighted averages, the areas considered suitable for endemic and epidemic RVF were more restricted. Varying the relative weights of the different factors in the models did not affect suitability estimates to a large degree, but variations in model structure had a large impact on our suitability estimates. Our Dempster-Shafer analysis supported the belief that a range of semi-arid areas were suitable for RVF epidemics and the plausibility that many other areas of the continent were suitable. Areas where high levels of uncertainty were highlighted included the Ethiopian Highlands, southwest Kenya and parts of West Africa. CONCLUSION: We have demonstrated the potential of methods developed in the decision sciences to improve our understanding of uncertainties surrounding the geographical distribution of animal diseases, particularly where information is sparse, and encourage wider application of the decision science methodology in the field of animal health

Risk factors associated with Rift Valley fever epidemics in South Africa in 2008-11.

Rift Valley fever (RVF) is a zoonotic and vector-borne disease, mainly present in Africa, which represents a threat to human health, animal health and production. South Africa has experienced three major RVF epidemics (1950-51, 1973-75 and 2008-11). Due to data scarcity, no previous study has quantified risk factors associated with RVF epidemics in animals in South Africa. Using the 2008-11 epidemic datasets, a retrospective longitudinal study was conducted to identify and quantify spatial and temporal environmental factors associated with RVF incidence. Cox regressions with a Besag model to account for the spatial effects were fitted to the data. Coefficients were estimated by Bayesian inference using integrated nested Laplace approximation. An increase in vegetation density was the most important risk factor until 2010. In 2010, increased temperature was the major risk factor. In 2011, after the large 2010 epidemic wave, these associations were reversed, potentially confounded by immunity in animals, probably resulting from earlier infection and vaccination. Both vegetation density and temperature should be considered together in the development of risk management strategies. However, the crucial need for improved access to data on population at risk, animal movements and vaccine use is highlighted to improve model predictions

Social and environmental factors affect tuberculosis related mortality in wild meerkats.

Tuberculosis (TB) is an important and widespread disease of wildlife, livestock and humans world-wide, but long-term empirical datasets describing this condition are rare. A population of meerkats (Suricata suricatta) in South Africa's Kalahari Desert have been diagnosed with Mycobacterium suricattae, a novel strain of TB, causing fatal disease in this group-living species. This study aimed to find characteristics associated with clinical TB in meerkats. These characteristics could subsequently be used to identify 'at-risk' animals within a population, and target these individuals for control measures. We conducted a retrospective study based on a unique, long-term life-history dataset of over 2000 individually identified animals covering a 14-year period after the first confirmatory diagnosis of TB in this population in 2001. Individual- and group-level risk factors were analysed using time-dependent Cox regression to examine their potential influence on the time to development of end-stage TB. Cases of disease involved 144 individuals in 27 of 73 social groups, across 12 of 14 years (an incidence rate of 3·78 cases/100 study years). At the individual level, increasing age had the greatest effect on risk of disease with a hazard ratio of 4·70 (95% CI: 1·92-11·53, P < 0·01) for meerkats aged 24-48 months, and a hazard ratio of 9·36 (3·34-26·25, P < 0·001) for animals aged over 48 months (both age categories compared with animals aged below 24 months). Previous group history of TB increased the hazard by a factor of 4·29 (2·00-9·17, P < 0·01), and an interaction was found between this variable and age. At a group level, immigrations of new group members in the previous year increased hazard by a factor of 3·00 (1·23-7·34, P = 0·016). There was weaker evidence of an environmental effect with a hazard ratio for a low rainfall (<200 mm) year of 2·28 (0·91-5·72, P = 0·079). Our findings identify potential individual characteristics on which to base targeted control measures such as vaccination. Additional data on the dynamics of the infection status of individuals and how this changes over time would complement these findings by enhancing understanding of disease progression and transmission, and thus the implications of potential management measures

BPEX Pig Health Scheme: a useful monitoring system for respiratory disease control in pig farms?

<p>Abstract</p> <p>Background</p> <p>Respiratory diseases account for significant economic losses to the UK pig industry. Lesions indicative of respiratory disease in pig lungs at slaughter e.g. pneumonia and pleuritis are frequently recorded to assess herd health or provide data for epidemiological studies. The BPEX Pig Health Scheme (BPHS) is a monitoring system, which informs producers of gross lesions in their pigs' carcasses at slaughter, enabling farm-level decisions to be made. The aim of the study was to assess whether information provided by the BPHS regarding respiratory lesions was associated with respiratory pathogens in the farm, farm management practices and each other.</p> <p>Results</p> <p>BPHS reports were obtained from a subset of 70 pig farms involved in a cross-sectional study conducted in 2008-09 investigating the epidemiology of post-weaning multi-systemic wasting syndrome. The reports were combined with data regarding the presence/absence of several pathogens in the herd and potential farm-level risk factors for respiratory disease. Principal component analysis (PCA) performed on BPHS reports generated three principal components, explaining 71% of the total variance. Enzootic pneumonia score, severe pleurisy and acute pleuropneumonia had the highest loadings for the principal component which explained the largest percentage of the total variance (35%) (BPHS component 1), it was thought that this component identifies farms with acute disease. Using the factor loadings a score for each farm for BPHS component 1 was obtained. As farms' score for BPHS component 1 increased, average carcass weight at slaughter decreased. In addition, farms positive for H1N2 and porcine reproductive and respiratory disease virus (PRRSV) were more likely to have higher levels of severe and mild pleurisy reported by the BPHS, respectively.</p> <p>Conclusions</p> <p>The study found statistical associations between levels of pleurisy recorded by BPHS at slaughter and the presence H1N2 and PRRSV in the herd. There is also some evidence that farms which submit pigs with these lesions may have reduced productivity. However, more research is needed to fully validate the scheme.</p

Trait-Based Vaccination of Individual Meerkats (Suricata suricatta) against Tuberculosis Provides Evidence to Support Targeted Disease Control.

Individuals vary in their potential to acquire and transmit infections, but this fact is currently underexploited in disease control strategies. We trialled a trait-based vaccination strategy to reduce tuberculosis in free-living meerkats by targeting high-contact meerkats (socially dominant individuals) in one study arm, and high-susceptibility individuals (young subordinates) in a second arm. We monitored infection within vaccinated groups over two years comparing the results with untreated control groups. Being a member of a high-contact group had a protective effect on individuals' survival times (Hazard Ratio = 0.5, 95% Confidence Interval, CI: 0.29-0.88, p = 0.02) compared to control groups. Over the study, odds of testing positive for tuberculosis increased more than five-fold in control groups (Odds Ratio = 5.40, 95% CI = 0.94-30.98, p = 0.058); however, no increases were observed in either of the treatment arms. Targeted disease control approaches, such as the one described in this study, allow for reduced numbers of interventions. Here, trait-based vaccination was associated with reduced infection rates and thus has the potential to offer more efficient alternatives to traditional mass-vaccination policies. Such improvements in efficiency warrant further study and could make infectious disease control more practically achievable in both animal (particularly wildlife) and human populations

Risk mapping for HPAI H5N1 in Africa - Improving surveillance for virulent bird flu: Final report and maps

More than 85 percent of households in rural Africa raise poultry for food, income, or both, and many people live in close contact with their birds. The possibility of an epidemic of highly pathogenic avian influenza (HPAI) H5N1 is therefore a major concern. Since 2006 bird fl u has been introduced into at least 11 countries in Africa, and over 600 outbreaks reported. Vigilance is key to limiting the disease but animal health personnel cannot monitor everywhere at once. This risk-mapping project was designed to help prioritize their efforts by showing in which places outbreaks are more likely to occur. A risk map is a complex, computer-generated image that shows the spatial distribution of the predicted risk of a disease. It is based on the spatial distribution of “risk factors” associated with an increased risk of disease, and the relative importance of each of these factors. In the case of virulent bird fl u, risk factors include major transport routes, markets where poultry may be traded, and wetlands with the possibility of contact between poultry and wild birds. Researchers in this project have prepared risk maps for bird fl u in Africa using multi-criteria decision modeling (MCDM). In this way they have integrated data and information from such diverse sources as published scientific literature, maps available in the public domain, field surveys and expert consultations

Amélioration de la surveillance de l’influenza aviaire de type H5N1 - Cartographie du risque d’influenza aviaire de type H5N1 en Afrique: Rapport final et cartes de risqué d’influenza aviaire

Plus de 85% des ménages ruraux en Afrique élèvent la volaille aux fins d’alimentation, de revenu ou les deux, et de nombreuses personnes vivent en contact étroit avec leurs oiseaux. La possibilité d’une épidémie de l’influenza aviaire hautement pathogène (IAHP) de type H5N1 est donc une grande préoccupation. Depuis 2006, la grippe aviaire est apparue dans au moins 11 pays africains et plus de 600 foyers d’épidémie ont été signalés. La vigilance est essentielle en vue de limiter la maladie mais le personnel de santé animale ne peut faire un suivi partout à la fois. Ce projet de cartographie de facteurs de risques a été conçu en vue d’aider à prioriser leurs efforts en indiquant les lieux où il existe un risque très élevé de flambées de la maladie. La cartographie des risques est une image complexe générée par ordinateur qui montre la répartition spatiale des facteurs de risques prévus d’une maladie. Elle est fondée sur la répartition spatiale des « facteurs de risques » associés au risque accru de maladie et à l’importance relative de chacun de ces facteurs. Dans le cas d’une grippe aviaire de type H5N1, les facteurs de risques sont les principales voies de transport, les marchés de volailles et les points d’eau avec possibilité de contact entre les oiseaux domestiques et sauvages. Pour ce projet, les chercheurs ont préparé des cartes de risques de grippe aviaire en Afrique en utilisant la modélisation de décision multicritères (MCDM). De cette façon, ils ont intégré les données et les informations de diverses sources telles que les publications scientifi ques, les cartes disponibles dans le domaine public, les études de terrain et les consultations d’expert