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

Within-holding prevalence of sheep classical scrapie in Great Britain

Abstract Background Data from the Compulsory Scrapie Flocks Scheme (CSFS), part of the compulsory eradication measures for the control of scrapie in the EU, have been used to estimate the within-holding prevalence of classical scrapie in Great Britain (GB). Specifically data from one of the testing routes within the CSFS have been used; the initial cull (IC), whereby two options can be applied: the whole flock cull option by which the entire flock is depopulated, and the genotyping and cull of certain genotypes. Results Between April 2005 and September 2007, 25,316 suitable samples, submitted from 411 flocks in 213 scrapie-affected holdings in Great Britain, were tested for scrapie. The predicted within-holding prevalence for the initial cull was 0.65% (95% CI: 0.55–0.75). For the whole cull option was 0.47% (95% CI: 0.32–0.68) and for the genotype and cull or mixed option (both options applied in different flocks of the same holding), the predicted within-holding prevalence was 0.7% (95% CI: 0.6–0.83). There were no significant differences in the within-flock prevalence between countries (England, Scotland and Wales) or between CSFS holdings by the surveillance stream that detected the index case. The number of CSFS flocks on a holding did not affect the overall within-holding prevalence of classical scrapie. Conclusion These estimates are important in the discussion of the epidemiological implications of the current EU testing programme of scrapie-affected flocks and to inform epidemiological and mathematical models. Furthermore, these estimates may provide baseline data to assist the design of future surveillance activities and control policies with the aim to increase their efficiency.</p