Linking process to pattern: estimating spatiotemporal dynamics of a wildlife epidemic from cross-sectional data
Linking process to pattern: estimating spatiotemporal dynamics of a wildlife epidemic from cross-sectional data
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DOI：
10.1890/09-0052.1
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发表时间：
2010-05-01
影响因子：
6.1
通讯作者：
Miller, Michael W.
中科院分区：
文献类型：
--
作者：
Heisey, Dennis M.;Osnas, Erik E.;Miller, Michael W.
在连续时间内展开的潜在动态事件过程会产生有时只能在几个离散时间观察到的时空模式。这样的事件过程可以在多个空间（例如，纬度和经度）和时间（例如，年龄、日历时间和群组）维度上同时调制。生态挑战是理解动态潜在过程，这些过程在多个维度（空间和时间）上整合以产生观察到的模式：所谓的逆问题。此类问题的一个例子是根据空间参考的特定年龄患病率数据来表征野生动物疾病（例如慢性消耗性疾病（CWD））的流行病学速率过程。对于特定年龄的患病率数据，无法观察到确切的感染时间，这使得直接估计发病率变得复杂。然而，观察到的数据和未观察到的速率变量之间的关系可以用似然方程来描述。通常，对于具有多个时间尺度的问题，似然度是没有封闭形式的积分方程。可能性的复杂性常常使得传统的最大似然方法站不住脚。这里使用了威斯康星州白尾鹿 (Odocoileus virginianus) 的 CWD 流行病七年的狩猎收获流行率数据。在美国，我们开发并探索了一种贝叶斯方法，可以详细检查影响空间、年龄和时间感染率的因素及其相互作用。我们的方法依赖于贝叶斯从空间和时间上的邻居借用力量的能力。综合事件时间分析的多个领域（当前状态数据、年龄/时期/队列模型、贝叶斯空间共享脆弱模型），我们的总体框架具有非常广泛的生态适用性，超越疾病流行数据到许多重要的生态事件时间分析，包括现有方法有限的多时间维度的一般生存研究。我们观察到感染率与年龄、性别和地点密切相关。感染率似乎随着时间的推移而增加。我们无法检测到增长热点。或位置与时间的相互作用，这表明感染率的空间变化主要取决于疾病到达当地的时间，而不是它的增长速度。我们强调假设及其违反的潜在后果。
Underlying dynamic event processes unfolding in continuous time give rise to spatiotemporal patterns that are sometimes observable at only a few discrete times. Such event processes may he modulated simultaneously over several spatial (e.g., latitude and longitude) and temporal (e.g., age, calendar time, and cohort) dimensions. The ecological challenge is to understand the dynamic latent processes that were integrated over several dimensions (space and time) to produce the observed pattern: a so-called inverse problem. An example of such a problem is characterizing epidemiological rate processes from spatially referenced age-specific prevalence data for a wildlife disease such as chronic wasting disease (CWD). With age-specific prevalence data, the exact infection times are not observed, which complicates the direct estimation of rates. However, the relationship between the observed data and the unobserved rate variables can he described with likelihood equations. Typically, for problems with multiple timescales, the likelihoods are integral equations without closed forms. The complexity of the likelihoods often makes traditional maximum-likelihood approaches untenable. Here, using seven years of hunter-harvest prevalence data From the CWD epidemic in white-tailed deer (Odocoileus virginianus) in Wisconsin. USA, we develop and explore a Bayesian approach that allows for a detailed examination of factors modulating the infection rates over space, age, and time, and their interactions. Our approach relies on the Bayesian ability to borrow strength from neighbors in both space and time. Synthesizing a number of areas of event time analysis (current-status data, age/period/cohort models. Bayesian spatial shared frailty models), our general framework has very broad ecological applicability beyond disease prevalence data to a number of important ecological event time analyses, including general survival studies with multiple time dimensions for which existing methodology is limited. We observed strong associations of infection rates with age, gender, and location. The infection rate appears to be increasing with time. We could not detect growth hotspots. or location by time interactions, which suggests that spatial variation in infection rates is determined primarily by when the disease arrives locally, rather than how fast it grows. We emphasize assumptions and the potential consequences of their violations.