Spatial and temporal patterns of chronic wasting disease: fine-scale mapping of a wildlife epidemic in Wisconsin
Spatial and temporal patterns of chronic wasting disease: fine-scale mapping of a wildlife epidemic in Wisconsin
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DOI：
10.1890/08-0578.1
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发表时间：
2009-07-01
影响因子：
5
通讯作者：
Samuel, Michael D.
中科院分区：
文献类型：
--
作者：
Osnas, Erik E.;Heisey, Dennis M.;Samuel, Michael D.
新出现的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非常重要;然而，由于宿主类别、抽样方差、抽样偏差和时空流行过程的异质性，数据变得复杂。忽视这些问题可能会导致错误的结论或模糊数据中的重要模式，例如疾病患病率的空间变化。在这里，我们采用分层贝叶斯疾病映射方法来考虑风险因素，并估计感染的空间和时间模式慢性消耗性疾病（CWD）在白尾鹿（Odocoileus virginianus）的威斯康星州，美国。我们发现由于年龄，性别和空间位置的感染显着异质性。感染概率随着年龄的增长，所有年轻的鹿，随着年龄的增长更快的年轻男性，然后下降一些老年动物，从疾病相关的死亡率和年龄相关的感染风险的变化预期。我们发现，疾病流行聚集在一个中心位置，如预期的一个简单的空间流行病的过程中，疾病流行应随时间和空间扩展。然而，我们无法检测到任何一致的时间或时空趋势慢性消耗病的患病率。时间趋势的估计表明，患病率可能会减少或增加，几乎相等的后验概率，没有时间或时空效应的模型几乎等同于基于偏差信息标准的模型。为了最大限度地解释位置作为疾病风险因素的作用，我们使用了我们开发和描述的直接标准化患病率映射技术。这些映射结果允许疾病管理行动被采用的参考估计的空间分布的疾病和那些主机类的风险最大。未来的野生动物流行病学研究应采用分层贝叶斯方法，以平滑跨空间和时间的估计数量，考虑异质性，然后报告疾病率的基础上适当的标准化。
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, USA. 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.