Centre for Environmental Mathematics, Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Penryn TR10 9FE, UK.
Department of Mathematics, College of Science, Najran University, Najran 11001, Saudi Arabia.
Sensors (Basel). 2023 May 13;23(10):4734. doi: 10.3390/s23104734.
The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the accuracy of the predictions for complex compartmental epidemiological models. A new approach for estimating the measurement noise covariance from real COVID-19 pandemic data has been presented based on the marginal likelihood (Bayesian evidence) for Bayesian model selection of the stochastic part of the Extended Kalman filter (EKF), with a sixth-order nonlinear epidemic model, known as the SEIQRD (Susceptible-Exposed-Infected-Quarantined-Recovered-Dead) compartmental model. This study presents a method for testing the noise covariance in cases of dependence or independence between the infected and death errors, to better understand their impact on the predictive accuracy and reliability of EKF statistical models. The proposed approach is able to reduce the error in the quantity of interest compared to the arbitrarily chosen values in the EKF estimation.
基于复杂噪声数据的冠状病毒疾病 (COVID-19) 模型预测存在认知不确定性，这极大地影响了大流行趋势和状态估计的准确性。需要量化由不同未观察到的隐藏变量引起的 COVID-19 趋势的不确定性，以评估复杂隔室流行病学模型预测的准确性。本文提出了一种从真实 COVID-19 大流行数据中估计测量噪声协方差的新方法，该方法基于随机部分的扩展卡尔曼滤波器 (EKF) 的贝叶斯模型选择的边际似然 (贝叶斯证据)，具有第六阶非线性传染病模型，称为 SEIQRD（易感-暴露-感染-检疫-恢复-死亡）隔室模型。本研究提出了一种在感染和死亡误差之间存在相关性或独立性情况下测试噪声协方差的方法，以更好地了解它们对 EKF 统计模型预测准确性和可靠性的影响。与 EKF 估计中任意选择的值相比，所提出的方法能够降低感兴趣数量的误差。
