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

In order to study the spread of an epidemic over a region as a function of time, we introduce an entropy ratio U describing the uniformity of infections over various states and their districts, and an entropy concentration coefficient [Formula: see text] The latter is a multiplicative version of the...

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Autor principal: Bandt, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712116/
https://www.ncbi.nlm.nih.gov/pubmed/33287080
http://dx.doi.org/10.3390/e22111315
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author Bandt, Christoph
author_facet Bandt, Christoph
author_sort Bandt, Christoph
collection PubMed
description In order to study the spread of an epidemic over a region as a function of time, we introduce an entropy ratio U describing the uniformity of infections over various states and their districts, and an entropy concentration coefficient [Formula: see text] 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, C 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.
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spelling pubmed-77121162021-02-24 Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic Bandt, Christoph Entropy (Basel) Article In order to study the spread of an epidemic over a region as a function of time, we introduce an entropy ratio U describing the uniformity of infections over various states and their districts, and an entropy concentration coefficient [Formula: see text] 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, C 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. MDPI 2020-11-18 /pmc/articles/PMC7712116/ /pubmed/33287080 http://dx.doi.org/10.3390/e22111315 Text en © 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bandt, Christoph
Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic
title Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic
title_full Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic
title_fullStr Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic
title_full_unstemmed Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic
title_short Entropy Ratio and Entropy Concentration Coefficient, with Application to the COVID-19 Pandemic
title_sort entropy ratio and entropy concentration coefficient, with application to the covid-19 pandemic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712116/
https://www.ncbi.nlm.nih.gov/pubmed/33287080
http://dx.doi.org/10.3390/e22111315
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