标题： 具有模型比较的流行病超级传播贝叶斯建模框架 显示英文标题

标题： A Bayesian Modelling Framework with Model Comparison for Epidemics with Super-Spreading

摘要： 传染病的传播动态很少是同质的。 超级传播事件和超级传播者是两种异质传播能力的类型。 对超级传播的推断通常基于次级病例数据，其预期分布称为后代分布。 然而，这种数据很少可用。 在这里，我们引入了一个多模型框架，适用于发病率时间序列数据，这种数据更容易获得。 该框架包括五个离散时间、随机、分支过程模型，这些模型描述了在易感人群中传播的疫情。 该框架包括一个同质传播的基本模型，一个单峰和一个双峰模型用于超级传播事件，以及一个单峰和一个双峰模型用于超级传播者。 使用贝叶斯统计方法，通过马尔可夫链蒙特卡洛来推断模型参数。 通过计算贝叶斯因子进行模型比较，使用重要性抽样来估计每个模型的边缘似然。 选择这个估计器是因为它与其他方法相比具有更高的一致性和更低的方差。 对每个模型的模拟数据的应用确定了大多数模拟中正确的模型，并准确地推断了真实参数，如基本繁殖数。 我们还将我们的方法应用于2003年SARS爆发和新冠状病毒大流行的发病率数据。 模型选择在使用不同时间序列时，对于某种疾病始终识别出相同的模型和机制。 我们的估计与基于次级病例数据的先前研究结果一致。 量化超级传播在疾病传播中的贡献对传染病管理和控制有重要意义。 我们的建模框架与疾病无关，并作为R包实现，有望成为公共卫生的重要工具。 显示英文摘要

摘要： The transmission dynamics of an epidemic are rarely homogeneous. Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility. Inference of super-spreading is commonly carried out on secondary case data, the expected distribution of which is known as the offspring distribution. However, this data is seldom available. Here we introduce a multi-model framework fit to incidence time-series, data that is much more readily available. The framework consists of five discrete-time, stochastic, branching-process models of epidemics spread through a susceptible population. The framework includes a baseline model of homogeneous transmission, a unimodal and a bimodal model for super-spreading events, as well as a unimodal and a bimodal model for super-spreading individuals. Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo. Model comparison is conducted by computing Bayes factors, with importance sampling used to estimate the marginal likelihood of each model. This estimator is selected for its consistency and lower variance compared to alternatives. Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters, such as the basic reproduction number. We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic. Model selection consistently identifies the same model and mechanism for a given disease, even when using different time series. Our estimates are consistent with previous studies based on secondary case data. Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control. Our modelling framework is disease-agnostic and implemented as an R package, with potential to be a valuable tool for public health.