Centre for Environmental and Agricultural Informatics, School of Water, Energy and Environment (SWEE), Cranfield University, Cranfield MK43 0AL, UK.
Department of Civil, Environmental and Geomatic Engineering, Faculty of Engineering, UCL, London WC1E 6BT, UK.
Int J Environ Res Public Health. 2021 Mar 26;18(7):3451. doi: 10.3390/ijerph18073451.
Controlling bovine tuberculosis (bTB) disease in cattle farms in England is seen as a challenge for farmers, animal health, environment and policy-makers. The difficulty in diagnosis and controlling bTB comes from a variety of factors: the lack of an accurate diagnostic test which is higher in specificity than the currently available skin test; isolation periods for purchased cattle; and the density of active badgers, especially in high-risk areas. In this paper, to enable the complex evaluation of bTB disease, a dynamic Bayesian network (DBN) is designed with the help of domain experts and available historical data. A significant advantage of this approach is that it represents bTB as a dynamic process that evolves periodically, capturing the actual experience of testing and infection over time. Moreover, the model demonstrates the influence of particular risk factors upon the risk of bTB breakdown in cattle farms.
控制英格兰奶牛场中的牛结核病(bTB)疾病对农民、动物健康、环境和政策制定者来说是一个挑战。诊断和控制 bTB 的困难来自于多种因素:缺乏比当前可用的皮肤测试特异性更高的准确诊断测试;购买牛的隔离期;以及活跃的獾的密度,特别是在高风险地区。在本文中,为了能够对 bTB 疾病进行复杂的评估,借助领域专家和可用的历史数据设计了一个动态贝叶斯网络(DBN)。这种方法的一个显著优势是,它将 bTB 表示为一个周期性演变的动态过程,随着时间的推移捕捉测试和感染的实际经验。此外,该模型还展示了特定风险因素对奶牛场 bTB 爆发风险的影响。