论文标题
通过多项式近似的随机流行模型中的推断
Inference in Stochastic Epidemic Models via Multinomial Approximations
论文作者
论文摘要
我们介绍了一种在随机流行模型中推断的新方法，该方法使用递归多项式近似值来整合未观察到的变量，从而避免了可能性的棘手性。该方法适用于具有部分，随机报告或缺失计数观测值的一类离散时间，有限的室内室模型。与最新的替代方案（例如近似贝叶斯计算技术）相反，不需要对模型进行正向模拟，也没有调谐参数。通过计算简单的过滤递归来评估模型参数的近似边际可能性。通过使用1995年刚果民主共和国的埃博拉疫情模型对真实和模拟数据的分析来证明近似的准确性。我们展示了如何将该方法嵌入到连续的蒙特卡洛方法中，以估计中国武汉（Wuhan）的COVID-19的时变繁殖数，该数量最近由Kucharski等人发表。 2020。
We introduce a new method for inference in stochastic epidemic models which uses recursive multinomial approximations to integrate over unobserved variables and thus circumvent likelihood intractability. The method is applicable to a class of discrete-time, finite-population compartmental models with partial, randomly under-reported or missing count observations. In contrast to state-of-the-art alternatives such as Approximate Bayesian Computation techniques, no forward simulation of the model is required and there are no tuning parameters. Evaluating the approximate marginal likelihood of model parameters is achieved through a computationally simple filtering recursion. The accuracy of the approximation is demonstrated through analysis of real and simulated data using a model of the 1995 Ebola outbreak in the Democratic Republic of Congo. We show how the method can be embedded within a Sequential Monte Carlo approach to estimating the time-varying reproduction number of COVID-19 in Wuhan, China, recently published by Kucharski et al. 2020.
