Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, NSW 2570, Australia.
Prev Vet Med. 2013 May 15;110(1):37-44. doi: 10.1016/j.prevetmed.2013.02.004. Epub 2013 Mar 13.
On-farm biosecurity practices have been promoted in many animal industries to protect animal populations from infections. Current approaches based on regression modelling techniques for assessing biosecurity perceptions and practices are limited for analysis of the interrelationships between multivariate data. A suitable approach, which does not require background knowledge of relationships, is provided by Bayesian network modelling. Here we apply such an approach to explore the complex interrelationships between the variables representing horse managers' perceptions of effectiveness of on-farm biosecurity practices. The dataset was derived from interviews conducted with 200 horse managers in Australia after the 2007 equine influenza outbreak. Using established computationally intensive techniques, an optimal graphical statistical model was identified whose structure was objectively determined, directly from the observed data. This methodology is directly analogous to multivariate regression (i.e. multiple response variables). First, an optimal model structure was identified using an exact (exhaustive) search algorithm, followed by pruning the selected model for over-fitting by the parametric bootstrapping approach. Perceptions about effectiveness of movement restrictions and access control were linked but were generally segregated from the perceptions about effectiveness of personal and equipment hygiene. Horse managers believing in the effectiveness of complying with movement restrictions in stopping equine influenza spread onto their premises were also more likely to believe in the effectiveness of reducing their own contact with other horses and curtailing professional visits. Similarly, the variables representing the effectiveness of disinfecting vehicles, using a disinfectant footbath, changing into clean clothes on arrival at the premises and washing hands before contact with managed horses were clustered together. In contrast, horse managers believing in the effectiveness of disinfecting vehicles (hygiene measure) were less likely to believe in the effectiveness of controlling who has access to managed horses (access control). The findings of this analysis provide new insights into the relationships between perceptions of effectiveness of different biosecurity measures. Different extension education strategies might be required for horse managers believing more strongly in the effectiveness of access control or hygiene measures.
农场生物安全实践已在许多动物产业中得到推广，以保护动物种群免受感染。当前基于回归建模技术评估生物安全感知和实践的方法在分析多变量数据之间的相互关系方面存在局限性。贝叶斯网络建模提供了一种合适的方法，它不需要对关系的背景知识。在这里，我们应用这种方法来探索代表马管理者对农场生物安全实践有效性的感知的变量之间的复杂相互关系。该数据集是从澳大利亚 200 名马管理者在 2007 年马流感爆发后的访谈中得出的。使用已建立的计算密集型技术，确定了一个最优的图形统计模型，其结构是从观察到的数据中直接客观确定的。这种方法与多元回归（即多个响应变量）直接相似。首先，使用精确（详尽）搜索算法确定最优模型结构，然后通过参数引导方法修剪所选模型以避免过度拟合。关于移动限制和访问控制的有效性的感知是相互关联的，但通常与个人和设备卫生的有效性的感知分开。相信遵守移动限制可阻止马流感传播到其场所的有效性的马管理者也更有可能相信减少与其他马接触和减少专业访问的有效性。同样，代表车辆消毒、使用消毒剂洗脚池、到达场所时更换干净衣服和接触管理马前洗手的有效性的变量也聚集在一起。相比之下，相信车辆消毒（卫生措施）有效性的马管理者不太可能相信控制谁可以接触管理马（访问控制）的有效性。这项分析的结果提供了对不同生物安全措施有效性感知之间关系的新见解。对于更相信访问控制或卫生措施有效性的马管理者，可能需要不同的推广教育策略。
