论文标题
机械流行模型中的不确定性定量通过跨凝结近似贝叶斯计算
Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation
论文作者
论文摘要
本文提出了一个数据驱动的近似贝叶斯计算框架，用于参数估计和流行病模型的不确定性量化，其中结合了两个新颖性：（i）使用与观察数据兼容的合理动态状态来识别初始条件； （ii）通过跨凝结方法学习模型参数的信息性先前分布。新方法的有效性在巴西里约热内卢市的Covid-19流行病的实际数据中进行了说明，该数据采用了一个普通的基于微分方程的模型，该模型具有广义的SEIR机械结构，其中包括时间依赖时间的传播率，无度经意和住院。提出了两个成本术语（住院和死亡人数）的最小化问题，并确定了十二个参数。校准模型提供了对可用数据的一致描述，能够在几周内推断预测，从而使所提出的方法对实时流行性建模非常有吸引力。
This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using plausible dynamic states that are compatible with observational data; (ii) learning of an informative prior distribution for the model parameters via the cross-entropy method. The new methodology's effectiveness is illustrated with the aid of actual data from the COVID-19 epidemic in Rio de Janeiro city in Brazil, employing an ordinary differential equation-based model with a generalized SEIR mechanistic structure that includes time-dependent transmission rate, asymptomatics, and hospitalizations. A minimization problem with two cost terms (number of hospitalizations and deaths) is formulated, and twelve parameters are identified. The calibrated model provides a consistent description of the available data, able to extrapolate forecasts over a few weeks, making the proposed methodology very appealing for real-time epidemic modeling.
