Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models
Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models
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DOI：
10.1007/s00466-020-01889-z
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发表时间：
2020-07-31
影响因子：
4.1
通讯作者：
Oden, J. Tinsley
中科院分区：
文献类型：
--
作者：
Jha, Prashant K.;Cao, Lianghao;Oden, J. Tinsley
我们考虑了COVID-19在德克萨斯州传播的混合理论连续模型。该模型由多个耦合偏微分反应扩散方程的发展，在一个给定的区域中的总人口的易感，暴露，传染，恢复和死亡的分数。我们考虑的问题，模型的校准，验证和预测的贝叶斯学习方法在OPAL（奥卡姆似然算法）。我们的目标是纳入COVID-19数据以实时校准模型，并作出有意义的预测，并通过量化关键关注量的不确定性来指定预测的置信水平。我们的研究结果表明，在得克萨斯州的死亡率低于文献报道。我们预测到2020年9月1日，德克萨斯州将有7003例死亡病例，95% CI为6802-7204。该模型对死亡病例总数有效，但对感染病例总数无效。我们讨论了模型的可能改进。
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 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.