Handbook of Infectious Disease Data Analysis
Handbook of Infectious Disease Data Analysis
复制标题
DOI：
10.1201/9781315222912
复制
发表时间：
2019-10
期刊：
影响因子：
--
通讯作者：
L. Held;N. Hens;P. O’Neill;J. Wallinga
中科院分区：
文献类型：
--
作者：
L. Held;N. Hens;P. O’Neill;J. Wallinga
在这一章中，我们考虑监视计数数据的时空分析。这些数据无处不在，人们提出了许多方法来分析它们。在回顾和批评一些常见模型之前，我们首先描述了监视努力的目标。我们关注的是将时间离散到所研究疾病的潜伏期和传染期的时间尺度的模型。我们特别关注[15]中最初描述的时间序列SIR （TSIR）模型和[22]中首次提出的流行病/地方性模型。我们在Stan软件中实现了这两种模型，并通过分析a / s期间收集的麻疹数据来说明它们的性能
In this chapter we consider space-time analysis of surveillance count data. Such data are ubiquitous and a number of approaches have been proposed for their analysis. We first describe the aims of a surveillance endeavor, before reviewing and critiquing a number of common models. We focus on models in which time is discretized to the time scale of the latent and infectious periods of the disease under study. In particular, we focus on the time series SIR (TSIR) models originally described in [15] and the epidemic/endemic models first proposed in [22]. We implement both of these models in the Stan software, and illustrate their performance via analyses of measles data collected over a