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.

翻译：本文提出了一种数据驱动的近似贝叶斯计算框架，用于传染病模型的参数估计与不确定性量化。该框架包含两项创新：（i）利用与观测数据兼容的合理动态状态来识别初始条件；（ii）通过交叉熵方法学习模型参数的信息性先验分布。以巴西里约热内卢市COVID-19疫情的实际数据为例，采用基于常微分方程的广义SEIR力学结构模型（包含时变传播率、无症状感染者及住院病例）验证了新方法的有效性。通过构建包含两个成本项（住院人数与死亡人数）的最小化问题，识别出十二个参数。校准后的模型能一致地描述现有数据，并可外推数周的预测，使得所提出的方法在实时传染病建模中极具应用价值。