论文标题
随机流行建模
Stochastic Epidemic Modelling
论文作者
论文摘要
推断流行病将如何进展以及提供有限的信息时要采取的行动对于流行病学家和卫生专业人员至关重要。在现实世界中，流行病学数据可能稀缺或遇到报告错误。在这个项目中，模拟了不同的流行情景，使用隐藏的马尔可夫链，它试图模仿流行病学家会遇到的不完美数据。此外，使用粒子马尔可夫链蒙特卡洛算法对不同种类的隔室模型进行建模，该算法具有自适应大都市 - 悬挂算法的变化，以估计模型基础参数的后部密度。此外，当数据集的变化发生时，研究了这些算法的敏感性。这是通过限制提供的信息，同时使用自适应方法在参数的后协方差来完成的。
Inferring how an epidemic will progress and what actions to take when presented with limited information is of critical importance for epidemiologists and health professionals. In real world settings, epidemiology data can be scarce or subject to reporting errors. In this project there are different epidemic scenarios simulated and, using hidden Markov Chains, it is attempted to mimic the imperfect data an epidemiologist will encounter. Furthermore, different kinds of compartmental models are modelled using the particle Markov Chain Monte Carlo algorithm with a variation of the adaptive Metropolis-Hastings algorithm to estimate the posterior density of the parameters underlying the models. Moreover, the sensitivity of these algorithms is investigated when subjected with changes in the dataset. This is accomplished by limiting the information provided, while using an adaptive approach on the posterior covariance of the parameters.
