PDF(Pubmed)
Abstract：
In late 2019, the emergence of COVID-19 in Wuhan, China, led to the implementation of stringent measures forming the zero-COVID policy aimed at eliminating transmission. Zero-COVID policy basically aimed at completely eliminating the transmission of COVID-19. However, the relaxation of this policy in late 2022 reportedly resulted in a rapid surge of COVID-19 cases. The aim of this work is to investigate the factors contributing to this outbreak using a new SEIR-type epidemic model with time-dependent level of immunity. Our model incorporates a time-dependent level of immunity considering vaccine doses administered and time-post-vaccination dependent vaccine efficacy. We find that vaccine efficacy plays a significant role in determining the outbreak size and maximum number of daily infected. Additionally, our model considers under-reporting in daily cases and deaths, revealing their combined effects on the outbreak magnitude. We also introduce a novel Physics Informed Neural Networks (PINNs) approach which is extremely useful in estimating critical parameters and helps in evaluating the predictive capability of our model.
摘要：
2019年末，COVID-19在武汉的出现，中国,导致实施了旨在消除传播的零COVID政策的严格措施。零COVID政策基本上旨在彻底消除COVID-19的传播。然而,据报道，2022年末这项政策的放松导致COVID-19病例迅速激增。这项工作的目的是使用具有时间依赖性免疫水平的新的SEIR型流行病模型来调查导致这次爆发的因素。我们的模型结合了时间依赖性的免疫水平，考虑了施用的疫苗剂量和疫苗接种后的时间依赖性疫苗功效。我们发现疫苗效力在确定爆发规模和每日感染的最大数量方面起着重要作用。此外,我们的模型考虑了日常病例和死亡的漏报，揭示了它们对疫情规模的综合影响。我们还介绍了一种新颖的物理知情神经网络（PINN）方法，该方法在估计关键参数方面非常有用，并有助于评估我们模型的预测能力。
