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熵比与熵浓度系数及其在新冠疫情中的应用

Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic.

作者信息

Bandt Christoph

机构信息

Institute of Mathematics, University of Greifswald, 17487 Greifswald, Germany.

出版信息

Entropy (Basel). 2020 Nov 18;22(11):1315. doi: 10.3390/e22111315.

Abstract

In order to study the spread of an epidemic over a region as a function of time, we introduce an entropy ratio describing the uniformity of infections over various states and their districts, and an entropy concentration coefficient C=1-U. The latter is a multiplicative version of the Kullback-Leibler distance, with values between 0 and 1. For product measures and self-similar phenomena, it does not depend on the measurement level. Hence, is an alternative to Gini's concentration coefficient for measures with variation on different levels. Simple examples concern population density and gross domestic product. Application to time series patterns is indicated with a Markov chain. For the Covid-19 pandemic, entropy ratios indicate a homogeneous distribution of infections and the potential of local action when compared to measures for a whole region.

摘要

为了研究流行病在一个地区随时间的传播情况,我们引入了一个熵比来描述不同状态及其区域内感染的均匀性,以及一个熵集中系数(C = 1 - U)。后者是库尔贝克 - 莱布勒距离的乘法形式,取值范围在(0)到(1)之间。对于乘积测度和自相似现象,它不依赖于测量水平。因此,对于不同水平存在变化的测度,它是基尼集中系数的一种替代。简单的例子涉及人口密度和国内生产总值。通过马尔可夫链表明了其在时间序列模式中的应用。对于新冠疫情,与整个地区的测量指标相比,熵比表明感染分布均匀以及地方行动的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bd9/7712116/a524e741de2e/entropy-22-01315-g001.jpg

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