Schuler Krysten L, Hollingshead Nicholas A, Heerkens Steven, Kelly James D, Hurst Jeremy E, Abbott Rachel C, Hanley Brenda J, Collins Eireann, Hynes Kevin P
Cornell Wildlife Health Lab, Public and Ecosystem Health, College of Veterinary Medicine, Animal Health Diagnostic Center, Cornell University,, 240 Farrier Road, Ithaca, NY 14850, USA.
Cornell Wildlife Health Lab, Public and Ecosystem Health, College of Veterinary Medicine, Animal Health Diagnostic Center, Cornell University,, 240 Farrier Road, Ithaca, NY 14850, USA.
Prev Vet Med. 2025 Jun 20;243:106599. doi: 10.1016/j.prevetmed.2025.106599.
Surveillance for emerging diseases can be enhanced through incorporation of risks and hazards to identify areas on the landscape with higher likelihoods of disease introduction and spread while increasing confidence that samples are collected from locations and animals with the highest probability of disease detection. A primary example of this situation is wildlife surveillance programs for chronic wasting disease (CWD) in free-ranging white-tailed deer (Odocoileus virginianus) in jurisdictions where it is not yet known to exist. But knowledge gaps in areas that lack sufficient disease testing and the nonexistence of data depicting disease introduction risks have impeded the ability to detect disease at the earliest intrusion into wild herds. We developed a novel method to conduct wildlife disease surveillance by considering how disease introduction likelihood may increase in the presence of risk factors, such as certain human activities and dense deer populations. In the absence of empirical risk data, we solicited perceptions from subject matter experts to develop a risk assessment (survey) characterizing the likelihood of disease introduction from anthropogenic activities. We overlaid these summarized perceptions with independent harvest data on the demographic attributes of wild cervid herds. We further incorporated previously published surveillance weights representing the differential disease information gained by testing each age/sex segment of deer. We applied the resulting surveillance design ('Hazard Model') in New York during the 2013-2014 hunting season and in Tennessee during the 2018-2019 hunting season. In both states, the Hazard Model suggested that counties with large deer populations, high-risk cervid businesses, or those in close proximity to infections in neighboring states were at greatest risk for introduction of CWD and therefore should be sampled with the greatest intensity. After a brief outbreak of CWD in New York in 2005, wildlife officials in New York did not re-discover CWD in their state, while officials in Tennessee discovered their first case of CWD within four months. The Hazard Model was developed with logistics and constraints as primary considerations, so implementation is sufficiently flexible to accommodate specific operational needs of the wildlife agency.
通过纳入风险和危害因素来加强对新发疾病的监测,可识别景观中疾病传入和传播可能性较高的区域,同时提高从疾病检测概率最高的地点和动物采集样本的可信度。这种情况的一个主要例子是在尚未发现慢性消耗病(CWD)的辖区内,对自由放养的白尾鹿(弗吉尼亚鹿)开展的野生动物监测计划。但在缺乏充分疾病检测的地区存在知识空白,且不存在描述疾病传入风险的数据,这阻碍了在疾病最早侵入野生鹿群时进行检测的能力。我们开发了一种新方法来进行野生动物疾病监测,该方法考虑了在存在某些人类活动和鹿群密度大等风险因素的情况下,疾病传入可能性如何增加。在缺乏实证风险数据的情况下,我们征求了主题专家的意见,以制定一项风险评估(调查),描述人为活动导致疾病传入的可能性。我们将这些汇总的意见与关于野生鹿群人口统计学特征的独立狩猎数据叠加在一起。我们还纳入了先前公布的监测权重,这些权重代表了通过检测鹿的每个年龄/性别段获得的不同疾病信息。我们在2013 - 2014年狩猎季节将由此产生的监测设计(“危害模型”)应用于纽约,在2018 - 2019年狩猎季节应用于田纳西。在这两个州,危害模型表明,鹿群数量大、高风险鹿业或与邻州感染地区相邻的县,引入CWD的风险最大,因此应以最高强度进行采样。2005年纽约曾短暂爆发CWD后,纽约的野生动物官员在该州未再次发现CWD,而田纳西的官员在四个月内发现了他们的首例CWD病例。危害模型的开发以后勤和限制因素为主要考虑因素,因此实施足够灵活,能够适应野生动物机构的特定业务需求。