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通过建模 COVID-19 的传播动态来预测疫情趋势。

Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19.

机构信息

Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

出版信息

Int J Environ Res Public Health. 2022 Apr 9;19(8):4541. doi: 10.3390/ijerph19084541.

Abstract

The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public health officials and the global community to rein in further spread of this and other virulent diseases. Classic epidemiological modeling based on dynamical systems are powerful tools used for modeling and understanding diseases, but often necessitate modifications to the classic compartmental models to reflect empirical observations. In this paper, we present a collection of extensions to the classic SIRS model to support public health decisions associated with viral pandemics. Specifically, we present models that reflect different levels of disease severity among infected individuals, capture the effect of vaccination on different population groups, capture the effect of different vaccines with different levels of effectiveness, and model the impact of a vaccine with varying number of doses. Further, our mathematical models support the investigation of a pandemic's trend under the emergence of new variants and the associated reduction in vaccine effectiveness. Our models are supported through numerical simulations, which we use to illustrate phenomena that have been observed in the COVID-19 pandemic. Our findings also confirm observations that the mild infectious group accounts for the majority of infected individuals, and that prompt immunization results in weaker pandemic waves across all levels of infection as well as a lower number of disease-caused deaths. Finally, using our models, we demonstrate that, when dealing with a single variant and having access to a highly effective vaccine, a three-dose vaccine has a strong ability to reduce the infectious population. However, when a new variant with higher transmissibility and lower vaccine efficiency emerges, it becomes the dominant circulating variant, as was observed in the recent emergence of the Omicron variant.

摘要

COVID-19 大流行的进程在各种人群规模上引发了许多疾病趋势,从局部到全球。了解这些趋势以及导致疾病进展相关动态变化的流行病学现象,对于公共卫生官员和全球社会来说,是控制这种和其他烈性疾病进一步传播的关键。基于动力系统的经典流行病学模型是用于对疾病进行建模和理解的强大工具,但通常需要对经典的隔室模型进行修改,以反映经验观察结果。在本文中,我们提出了一系列对经典 SIRS 模型的扩展,以支持与病毒大流行相关的公共卫生决策。具体来说,我们提出了反映感染个体不同疾病严重程度的模型,捕获疫苗对不同人群的影响,捕获不同有效性水平的不同疫苗的影响,以及建模具有不同剂量数的疫苗的影响。此外,我们的数学模型支持在新变体出现和疫苗有效性降低的情况下,对大流行趋势进行研究。我们的模型通过数值模拟得到支持,我们使用这些模拟来说明在 COVID-19 大流行中观察到的现象。我们的研究结果还证实了这样的观察结果,即轻度感染组占感染个体的大多数,及时免疫会导致所有感染水平的大流行波较弱,以及因疾病导致的死亡人数较少。最后,使用我们的模型,我们证明了当处理单一变体且可获得高效疫苗时,三剂疫苗具有降低感染人群的强大能力。然而,当一种具有更高传染性和较低疫苗效率的新变体出现时,它就会成为主要的传播变体,就像最近奥密克戎变体的出现那样。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7709/9032231/7994a9bf6b79/ijerph-19-04541-g001.jpg

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