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利用意识对具有过度暴露的SEIR模型进行Z控制:对新冠疫情的见解

Using awareness to Z-control a SEIR model with overexposure: Insights on Covid-19 pandemic.

作者信息

Lacitignola Deborah, Diele Fasma

机构信息

Dipartimento di Ingegneria Elettrica e dell'Informazione, Università di Cassino e del Lazio Meridionale, via Di Biasio, Cassino I-03043, Italy.

Istituto per le Applicazioni del Calcolo M. Picone,CNR, Via Amendola 122, Bari I-70126, Italy.

出版信息

Chaos Solitons Fractals. 2021 Sep;150:111063. doi: 10.1016/j.chaos.2021.111063. Epub 2021 May 24.

Abstract

In this paper, we use the Z-control approach to get further insight on the role of awareness in the management of epidemics that, just like Covid-19, display a high rate of overexposure because of the large number of asymptomatic people. We focus on a SEIR model including a overexposure mechanism and consider awareness as a time-dependent variable whose dynamics is not assigned a priori. Exploiting the potential of awareness to produce social distancing and self-isolation among susceptibles, we use it as an indirect control on the class of infective individuals and apply the Z-control approach to detect what trend must awareness display over time in order to eradicate the disease. To this aim, we generalize the Z-control procedure to appropriately treat an uncontrolled model with more than two governing equations. Analytical and numerical investigations on the resulting Z-controlled system show its capability in controlling some representative dynamics within both the backward and the forward scenarios. The awareness variable is qualitatively compared to Google Trends data on Covid-19 that are discussed in the perspective of the Z-control approach, inferring qualitative indications in view of the disease control. The cases of Italy and New Zealand in the first phase of the pandemic are analyzed in detail. The theoretical framework of the Z-control approach can hence offer the chance to reflect on the use of Google Trends as a possible indicator of good management of the epidemic.

摘要

在本文中,我们使用Z控制方法,以进一步深入了解认知在流行病管理中的作用。像新冠疫情这样的流行病,由于大量无症状感染者的存在,呈现出高暴露率。我们关注一个包含过度暴露机制的SEIR模型,并将认知视为一个随时间变化的变量,其动态变化并非预先设定。利用认知在易感人群中产生社交距离和自我隔离的潜力,我们将其作为对感染个体类别的间接控制,并应用Z控制方法来检测认知随时间必须呈现何种趋势才能根除疾病。为此,我们对Z控制程序进行了推广,以适当地处理具有两个以上控制方程的非受控模型。对所得Z受控系统的分析和数值研究表明,它有能力在向后和向前的情景中控制一些具有代表性的动态变化。将认知变量与关于新冠疫情的谷歌趋势数据进行定性比较,并从Z控制方法的角度对这些数据进行讨论,从而根据疾病控制推断出定性指标。详细分析了疫情第一阶段意大利和新西兰的情况。因此,Z控制方法的理论框架能够为思考将谷歌趋势用作疫情良好管理的可能指标提供契机。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ee2/8142850/b924cb80df33/gr1_lrg.jpg

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