Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.
Center for Geospatial Analytics, North Carolina State University, Raleigh, North Carolina, USA.
Transbound Emerg Dis. 2022 Sep;69(5):e2757-e2768. doi: 10.1111/tbed.14627. Epub 2022 Jun 25.
Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.
大多数动物疾病监测系统集中精力阻断传播途径和追溯感染接触者，而不考虑将动物运输到疾病风险升高的地区的风险。在这里，我们使用一系列空间统计和社交网络分析来描述具有不同疾病循环风险的地区之间的动物运动，最终增强监测活动。我们的模型利用了马传染性贫血病毒 (EIAV) 爆发、农场间马匹运动以及 2015 年至 2017 年的空间景观数据。我们将 EIAV 的发生和农场间马匹的运动与促进局部疾病传播的气候变量联系起来。然后，我们构建了一个空间显式模型，允许气候变量对 EIAV 发生的影响在空间上发生变化（即非平稳）。我们的结果确定了一些重要的区域，在这些区域中，进入的运动更有可能导致 EIAV 感染和疾病传播。然后将直辖市分为高风险 56 个（11.3%）、中风险 48 个（9.66%）和低风险 393 个（79.1%）。大多数运动是在低风险地区之间进行的，总共占所有动物运动的 68.68%。与此同时，9.48%发生在高风险地区，6.20%发生在中风险地区。只有 5.37%进入低风险地区的动物来自高风险地区。另一方面，4.91%来自高风险地区的动物来自低风险和中风险地区。我们的结果表明，动物运动和空间风险图可以在发放动物运动许可证之前做出明智的决策，从而降低将感染重新引入低风险地区的机会。
