Stochastic state-transition models of infectious disease transmission can be used to deduce relevant drivers of transmission when fitted to data using statistically principled methods. Fitting this individual-level data requires inference on individuals' unobserved disease statuses over time, which form a high-dimensional and highly correlated state space. We introduce a novel Bayesian (data-augmentation Markov chain Monte Carlo) algorithm for jointly estimating the model parameters and unobserved disease statuses, which we call the Rippler algorithm. This is a non-centred method that can be applied to any individual-based state-transition model. We compare the Rippler algorithm to the state-of-the-art inference methods for individual-based stochastic epidemic models and find that it performs better than these methods as the number of disease states in the model increases.

翻译：传染病传播的随机状态转移模型可通过统计原理方法与数据拟合，从而推断传播的关键驱动因素。拟合此类个体水平数据需对个体随时间变化的未观测疾病状态进行推断，这些状态构成高维且高度相关的状态空间。本文提出一种新颖的贝叶斯（数据增强马尔可夫链蒙特卡洛）算法，用于联合估计模型参数与未观测疾病状态，我们称之为Rippler算法。这是一种非中心化方法，可应用于任何基于个体的状态转移模型。我们将Rippler算法与基于个体的随机流行病模型最先进的推断方法进行比较，发现随着模型中疾病状态数量的增加，该算法性能优于现有方法。