Malesios Chrisovalantis, Demiris Nikolaos, Kalogeropoulos Konstantinos, Ntzoufras Ioannis
Department of Agricultural Development, Democritus University of Thrace, Orestiada, Greece.
Department of Statistics, Athens University of Economics and Business, Athens, Greece.
Stat Med. 2017 Sep 10;36(20):3216-3230. doi: 10.1002/sim.7364. Epub 2017 Jun 12.
Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein-Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece. Copyright © 2017 John Wiley & Sons, Ltd.
疫情数据通常具有某些特征，例如存在许多零值、疾病传播机制的空间特性、环境噪声、序列相关性以及对时变因素的依赖性。本文通过合适的贝叶斯建模来解决这些问题。在此过程中，我们使用了一类适用于具有过多零值的时空计数数据的通用随机回归模型。所开发的回归框架确实通过奥恩斯坦 - 乌伦贝克过程公式纳入了序列相关性和时变协变量。此外，我们探讨了不同先验的影响，包括默认选项以及 g - 先验混合的变体。研究了疫情模型组件不同距离核的影响。我们通过开发基于分支过程的方法来测试疾病控制方案，从而将传统流行病学模型与随机疫情过程联系起来，这在以政策为重点的决策中很有用。该方法通过应用于希腊埃夫罗斯地区的羊痘数据集进行了说明。版权所有© 2017 约翰·威利父子有限公司。
