We consider a mixture-theoretic continuum model of the spread of COVID-19 in Texas. The model consists of multiple coupled partial differential reaction–diffusion equations governing the evolution of susceptible, exposed, infectious, recovered, and deceased fractions of the total population in a given region. We consider the problem of model calibration, validation, and prediction following a Bayesian learning approach implemented in OPAL (the Occam Plausibility Algorithm). Our goal is to incorporate COVID-19 data to calibrate the model in real-time and make meaningful predictions and specify the confidence level in the prediction by quantifying the uncertainty in key quantities of interests. Our results show smaller mortality rates in Texas than what is reported in the literature. We predict 7003 deceased cases by September 1, 2020 in Texas with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\%$$\end{document}95% CI 6802–7204. The model is validated for the total deceased cases, however, is found to be invalid for the total infected cases. We discuss possible improvements of the model.

中文翻译：

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使用多物种混合理论连续体模型基于贝叶斯预测德克萨斯州的 COVID-19 演变


我们考虑使用混合理论连续统模型来描述 COVID-19 在德克萨斯州的传播。该模型由多个耦合偏微分反应扩散方程组成，控制给定区域内总人口中易感者、暴露者、感染者、康复者和死亡者比例的演变。我们考虑遵循 OPAL（奥卡姆合理性算法）中实施的贝叶斯学习方法的模型校准、验证和预测问题。我们的目标是结合 COVID-19 数据来实时校准模型，做出有意义的预测，并通过量化关键兴趣量的不确定性来指定预测的置信度。我们的结果显示德克萨斯州的死亡率比文献报道的要低。我们预测到 2020 年 9 月 1 日，德克萨斯州将有 7003 例死亡病例， \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs } \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{文档}$$95\%$$\end{文档}95% CI 6802–7204。该模型针对死亡病例总数进行了验证，但发现对于感染病例总数无效。我们讨论模型的可能改进。