Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, based on a discrete-time spatio-temporal susceptible/exposed/infected/recovered/deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 Covid-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.

中文翻译：

---

一种用于建模和估计 Covid-19 传播的随机元种群状态空间方法

数学模型被广泛认为是分析和理解传染病暴发动态、预测其未来趋势、评估疾病控制和消除公共卫生干预措施的重要工具。我们基于离散时间时空易感/暴露/感染/恢复/死亡（SEIRD）模型，提出了一种用于 COVID-19 传播的新型随机种群状态空间模型。所提出的框架允许通过应用无迹卡尔曼滤波 (UKF)、最大似然自适应滤波和元启发式优化，从报告的流行病学数据的嘈杂、不完整的时间序列中估计隐藏的 SEIRD 状态和未知的传输参数。