Bayesian Nonparametrics for Stochastic Epidemic Models
Bayesian Nonparametrics for Stochastic Epidemic Models
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DOI：
10.1214/17-sts617
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发表时间：
2018-02-01
影响因子：
5.7
通讯作者：
O'Neill, Philip D.
中科院分区：
文献类型：
--
作者：
Kypraios, Theodore;O'Neill, Philip D.
绝大多数传染病传播模型本质上是参数模型，涉及有关疾病如何在人群中传播的基本假设。在这篇文章中，我们考虑使用贝叶斯非参数方法来分析疾病暴发的数据。具体地说，在假设感染过程具有显式的时间依赖性的假设下，我们重点研究了在简单模型中估计感染过程的方法。
The vast majority of models for the spread of communicable diseases are parametric in nature and involve underlying assumptions about how the disease spreads through a population. In this article, we consider the use of Bayesian nonparametric approaches to analysing data from disease outbreaks. Specifically we focus on methods for estimating the infection process in simple models under the assumption that this process has an explicit time-dependence.