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用于预测SARS-CoV-2周期性行为的延迟建模方法。

Delayed Modeling Approach to Forecast the Periodic Behavior of SARS-2.

作者信息

Yu Zhenhua, Sohail Ayesha, Nutini Alessandro, Arif Robia

机构信息

Institute of Systems Security and Control, College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, China.

Department of Mathematics, Comsats University Islamabad, Lahore, Pakistan.

出版信息

Front Mol Biosci. 2021 Apr 1;7:585245. doi: 10.3389/fmolb.2020.585245. eCollection 2020.

Abstract

The ongoing threat of Coronavirus is alarming. The key players of this virus are modeled mathematically during this research. The transmission rates are hypothesized, with the aid of epidemiological concepts and recent findings. The model reported is extended, by taking into account the delayed dynamics. Time delay reflects the fact that the dynamic behavior of transmission of the disease, at time depends not only on the state at time but also on the state in some period τ before time . The research presented in this manuscript will not only help in understanding the current threat of pandemic (SARS-2), but will also contribute in making precautionary measures and developing control strategies.

摘要

新型冠状病毒持续构成的威胁令人担忧。在这项研究中,对该病毒的关键因素进行了数学建模。借助流行病学概念和最新研究结果,对传播率进行了假设。所报告的模型通过考虑延迟动态进行了扩展。时间延迟反映了这样一个事实,即疾病传播的动态行为在时刻 不仅取决于时刻 的状态,还取决于时刻 之前某个时间段 τ 内的状态。本手稿中提出的研究不仅将有助于理解当前大流行(SARS-2)的威胁,还将有助于制定预防措施和制定控制策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bd2/8047460/2d9e6aaaba89/fmolb-07-585245-g0001.jpg

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