论文标题
泊松区数据的时空模型，并应用于里约热内卢的艾滋病流行
Spatiotemporal models for Poisson areal data with an application to the AIDS epidemic in Rio de Janeiro
论文作者
论文摘要
我们提供了一类时空模型，用于用于分析新兴传染病的泊松数据。这些模型假设通过链接方程与潜在的随机场过程相关的泊松观测值。这种潜在的随机场过程随着适当的高斯马尔可夫随机场卷积而演变。我们的方法自然地适应了柔性结构，例如每个区域的独特但相互作用的时间趋势以及邻近地区之间的跨时间污染。我们使用基于模拟的程序开发贝叶斯分析方法：具体来说，我们基于广义的扩展卡尔曼滤波器构建了马尔可夫链蒙特卡洛算法，以从近似后部分布中获取样品。最后，为了比较泊松时空模型，我们开发了基于模拟的条件贝叶斯因子。我们说明了泊松时空框架的实用性和灵活性，并在1982 - 2007年期间在里约热内卢的期间应用了获得的免疫缺陷综合征（AIDS）病例。
We present a class of spatiotemporal models for Poisson areal data suitable for the analysis of emerging infectious diseases. These models assume Poisson observations related through a link equation to a latent random field process. This latent random field process evolves through time with proper Gaussian Markov random field convolutions. Our approach naturally accommodates flexible structures such as distinct but interacting temporal trends for each region and across-time contamination among neighboring regions. We develop a Bayesian analysis approach with a simulation-based procedure: specifically, we construct a Markov chain Monte Carlo algorithm based on the generalized extended Kalman filter to obtain samples from an approximate posterior distribution. Finally, for the comparison of Poisson spatiotemporal models, we develop a simulation-based conditional Bayes factor. We illustrate the utility and flexibility of our Poisson spatiotemporal framework with an application to the number of acquired immunodeficiency syndrome (AIDS) cases during the period 1982-2007 in Rio de Janeiro.
