论文标题
通过统一的千古块采样的随机流行模型的精确推断
Exact Inference for Stochastic Epidemic Models via Uniformly Ergodic Block Sampling
论文作者
论文摘要
随机流行模型对疾病通过人群的传播提供了可解释的概率描述。然而，由于许多经典模型的可能性很难，将这些模型拟合到部分观察到的数据是一项艰巨的任务。为了解决这个问题，本文在随机的SIR模型下介绍了一种新型的数据增强的MCMC算法，用于精确的贝叶斯推断，只有离散观察到感染计数。在大都市束缚的步骤中，从仔细设计的替代过程中共同提出了潜在数据，该过程与SIR模型非常相似，我们可以从中有效地生成与观察到的数据一致的流行病。这产生了一种方法，可以有效探索高维的潜在空间，并与成千上万个个体的爆发相扩展到爆发。我们表明，该算法的马尔可夫链是统一的，并通过彻底的仿真实验和2013 - 2015年西非埃博拉血出血爆发的案例研究来验证其性能。
Stochastic epidemic models provide an interpretable probabilistic description of the spread of a disease through a population. Yet, fitting these models to partially observed data is a notoriously difficult task due to intractability of the likelihood for many classical models. To remedy this issue, this article introduces a novel data-augmented MCMC algorithm for exact Bayesian inference under the stochastic SIR model, given only discretely observed counts of infection. In a Metropolis-Hastings step, the latent data are jointly proposed from a surrogate process carefully designed to closely resemble the SIR model, from which we can efficiently generate epidemics consistent with the observed data. This yields a method that explores the high-dimensional latent space efficiently, and scales to outbreaks with hundreds of thousands of individuals. We show that the Markov chain underlying the algorithm is uniformly ergodic, and validate its performance via thorough simulation experiments and a case study on the 2013-2015 outbreak of Ebola Haemorrhagic Fever in Western Africa.
