Anderson D P, Gormley A M, Bosson M, Livingstone P G, Nugent G
Manaaki Whenua Landcare Research, Wildlife Ecology and Management, P.O. Box 69040, Lincoln 7640, New Zealand.
Manaaki Whenua Landcare Research, Wildlife Ecology and Management, P.O. Box 69040, Lincoln 7640, New Zealand.
Prev Vet Med. 2017 Dec 1;148:106-114. doi: 10.1016/j.prevetmed.2017.10.015. Epub 2017 Oct 28.
A central question to address in managing wildlife diseases is how much effort and resources are required to reduce infection prevalence to below a requisite threshold? This requires surveillance for infection in at least one species involved in the infection cycle, a process that is often expensive and time-consuming but one which could be enhanced using additional sources of readily-obtainable surveillance data. We demonstrate how surveillance data from ruminant livestock monitored for bovine tuberculosis (bTB) in New Zealand can be employed in spatially-explicit modelling to help predict the probability of freedom from Mycobacterium bovis infection in a sympatric wildlife reservoir species, the brushtail possum (Trichosurus vulpecula). We apply the model to a case study and compare resulting probabilities of freedom when utilizing (1) livestock data only, (2) wildlife data only, and (3) combined livestock-plus-wildlife surveillance data. Results indicated that the greatest probability of M. bovis eradication was achieved using wildlife monitoring data supplemented with livestock surveillance data. This combined approach lessened the time required for a confident (95% probability) declaration of regional eradication. However, the combined model was sensitive to the precision of the input parameters, and we describe ways to account for this. In a broad sense, this modelling approach is flexible in that any spatial arrangement of wildlife habitat and farms can be analysed, provided infection is readily detectable in both the wild and domestic animal(s) of interest. It is applicable to monitoring any communicable wildlife disease that affects regularly-tested livestock. The potential benefits to wildlife disease management include reduced surveillance costs and more rapid achievement of targeted reductions in disease prevalence.
在管理野生动物疾病时需要解决的一个核心问题是，需要投入多少精力和资源才能将感染率降低到规定阈值以下？这需要对感染循环中至少一个物种的感染情况进行监测，这一过程通常既昂贵又耗时，但可以利用其他易于获取的监测数据来源加以改进。我们展示了如何将新西兰对反刍家畜进行牛结核病（bTB）监测得到的监测数据用于空间明确建模，以帮助预测同域野生宿主物种帚尾袋貂（Trichosurus vulpecula）无牛分枝杆菌感染的概率。我们将该模型应用于一个案例研究，并比较了在使用（1）仅家畜数据、（2）仅野生动物数据以及（3）家畜加野生动物监测数据组合时得出的无感染概率。结果表明，使用补充了家畜监测数据的野生动物监测数据，实现牛分枝杆菌根除的概率最大。这种组合方法减少了有信心（95%概率）宣布区域根除所需的时间。然而，组合模型对输入参数的精度很敏感，我们描述了应对这一问题的方法。从广义上讲，这种建模方法具有灵活性，因为只要在所关注的野生动物和家畜中感染易于检测，就可以分析野生动物栖息地和农场的任何空间布局。它适用于监测任何影响定期检测家畜的传染性野生动物疾病。对野生动物疾病管理的潜在好处包括降低监测成本以及更快实现疾病流行率的目标降低。
