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SIR模型中流行病峰值时间的显式公式。该使用哪种近似值?

Explicit formulae for the peak time of an epidemic from the SIR model. Which approximant to use?

作者信息

Kröger Martin, Turkyilmazoglu Mustafa, Schlickeiser Reinhard

机构信息

Polymer Physics, Department of Materials, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093 Zurich, Switzerland.

Department of Mathematics, Hacettepe University, Beytepe, 06532, Ankara, Turkey.

出版信息

Physica D. 2021 Nov;425:132981. doi: 10.1016/j.physd.2021.132981. Epub 2021 Jun 24.

Abstract

An analytic evaluation of the peak time of a disease allows for the installment of effective epidemic precautions. Recently, an explicit analytic, approximate expression (MT) for the peak time of the fraction of infected persons during an outbreak within the susceptible-infectious-recovered/removed (SIR) model had been presented and discussed (Turkyilmazoglu, 2021). There are three existing approximate solutions (SK-I, SK-II, and CG) of the semi-time SIR model in its reduced formulation that allow one to come up with different explicit expressions for the peak time of the infected compartment (Schlickeiser and Kröger, 2021; Carvalho and Gonçalves, 2021). Here we compare the four expressions for any choice of SIR model parameters and find that SK-I, SK-II and CG are more accurate than MT as long as the amount of population to which the SIR model is applied exceeds hundred by far (countries, ss, cities). For small populations with less than hundreds of individuals (families, small towns), however, the approximant MT outperforms the other approximants. To be able to compare the various approaches, we clarify the equivalence between the four-parametric dimensional SIR equations and their two-dimensional dimensionless analogue. Using Covid-19 data from various countries and sources we identify the relevant regime within the parameter space of the SIR model.

摘要

对疾病高峰时间进行分析评估有助于制定有效的疫情预防措施。最近,有人提出并讨论了易感-感染-康复/清除(SIR)模型中疫情爆发期间感染人群比例高峰时间的一个明确的解析近似表达式(MT)(图尔基尔马佐格鲁,2021年)。简化形式的半时间SIR模型有三种现有的近似解(SK-I、SK-II和CG),这使得人们可以为感染部分的高峰时间得出不同的明确表达式(施利克塞泽和克罗格,2021年;卡瓦略和贡萨尔维斯,2021年)。在这里,我们针对SIR模型参数的任何选择比较这四个表达式,发现只要应用SIR模型的人口数量远远超过100(国家、州、城市),SK-I、SK-II和CG比MT更准确。然而,对于人口少于数百人的小群体(家庭、小镇),近似式MT优于其他近似式。为了能够比较各种方法,我们阐明了四参数维SIR方程与其二维无量纲类似方程之间的等价性。利用来自不同国家和来源的新冠疫情数据,我们确定了SIR模型参数空间内的相关区域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e89/8225312/d983bd949111/gr1_lrg.jpg

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