Osnas Erik E, Heisey Dennis M, Rolley Robert E, Samuel Michael D
Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, Wisconsin 53706, USA.
Ecol Appl. 2009 Jul;19(5):1311-22. doi: 10.1890/08-0578.1.
Emerging infectious diseases threaten wildlife populations and human health. Understanding the spatial distributions of these new diseases is important for disease management and policy makers; however, the data are complicated by heterogeneities across host classes, sampling variance, sampling biases, and the space-time epidemic process. Ignoring these issues can lead to false conclusions or obscure important patterns in the data, such as spatial variation in disease prevalence. Here, we applied hierarchical Bayesian disease mapping methods to account for risk factors and to estimate spatial and temporal patterns of infection by chronic wasting disease (CWD) in white-tailed deer (Odocoileus virginianus) of Wisconsin, U.S.A. We found significant heterogeneities for infection due to age, sex, and spatial location. Infection probability increased with age for all young deer, increased with age faster for young males, and then declined for some older animals, as expected from disease-associated mortality and age-related changes in infection risk. We found that disease prevalence was clustered in a central location, as expected under a simple spatial epidemic process where disease prevalence should increase with time and expand spatially. However, we could not detect any consistent temporal or spatiotemporal trends in CWD prevalence. Estimates of the temporal trend indicated that prevalence may have decreased or increased with nearly equal posterior probability, and the model without temporal or spatiotemporal effects was nearly equivalent to models with these effects based on deviance information criteria. For maximum interpretability of the role of location as a disease risk factor, we used the technique of direct standardization for prevalence mapping, which we develop and describe. These mapping results allow disease management actions to be employed with reference to the estimated spatial distribution of the disease and to those host classes most at risk. Future wildlife epidemiology studies should employ hierarchical Bayesian methods to smooth estimated quantities across space and time, account for heterogeneities, and then report disease rates based on an appropriate standardization.
新发传染病威胁着野生动物种群和人类健康。了解这些新疾病的空间分布对于疾病管理和政策制定者来说至关重要；然而，数据因宿主类别间的异质性、抽样方差、抽样偏差以及时空流行过程而变得复杂。忽视这些问题可能会导致错误结论或掩盖数据中的重要模式，比如疾病患病率的空间变异。在此，我们应用分层贝叶斯疾病映射方法来考虑风险因素，并估计美国威斯康星州白尾鹿（弗吉尼亚鹿）慢性消耗病（CWD）感染的时空模式。我们发现，感染情况因年龄、性别和空间位置存在显著异质性。所有幼鹿的感染概率随年龄增加，幼龄雄性鹿的感染概率随年龄增加得更快，然后一些老龄动物的感染概率下降，这与疾病相关死亡率和感染风险的年龄相关变化预期一致。我们发现疾病患病率集中在一个中心位置，这符合简单空间流行过程的预期，即疾病患病率应随时间增加并在空间上扩展。然而，我们未能检测到CWD患病率有任何一致的时间或时空趋势。时间趋势估计表明，患病率下降或上升的后验概率几乎相等，并且基于偏差信息准则，无时间或时空效应的模型与有这些效应的模型几乎等效。为了最大程度地解释位置作为疾病风险因素的作用，我们使用直接标准化技术进行患病率映射，并对其进行了开发和描述。这些映射结果有助于根据疾病估计的空间分布以及最具风险的宿主类别来采取疾病管理行动。未来的野生动物流行病学研究应采用分层贝叶斯方法来平滑估计量在空间和时间上的分布，考虑异质性，然后基于适当的标准化报告疾病发生率。
