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Entropy and Epidemiology

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (15 December 2020) | Viewed by 7593

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Copenhagen Business College, Rønne Alle 1, st., 2860 Søborg, Denmark
Interests: cause and effect; entropy; exponential families; graphical models; information divergence; minimum description length; quantum information; statistical mechanics
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Special Issue Information

Dear Colleagues,

Some aspects of an epidemic are quite predictable, but there are other aspects that involve randomness and uncertainty. Entropy has proven to be one of the most robust measures of uncertainty, so it may provide a useful tool to analyze certain aspects of epidemics. The most obvious aspects of an epidemic that involve uncertainty are:

  • The evolution of a disease via mutations.
  • The spreading of the disease in a population.
  • Monitoring an epidemic via sampling and testing.
  • Modelling cause and effect, latent variables, confounders, etc. for predicting who will get infected and which infected individuals will develop the disease.

Researchers that have novel results on the use of entropy and related concepts in modelling and handling epidemics are welcome to submit their research to this Special Issue.

Dr. Peter Harremoës
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

20 pages, 498 KiB  
Article
Analytical Parameter Estimation of the SIR Epidemic Model. Applications to the COVID-19 Pandemic
by Dimiter Prodanov
Entropy 2021, 23(1), 59; https://doi.org/10.3390/e23010059 - 31 Dec 2020
Cited by 26 | Viewed by 4591
Abstract
The SIR (Susceptible-Infected-Removed) model is a simple mathematical model of epidemic outbreaks, yet for decades it evaded the efforts of the mathematical community to derive an explicit solution. The present paper reports novel analytical results and numerical algorithms suitable for parametric estimation of [...] Read more.
The SIR (Susceptible-Infected-Removed) model is a simple mathematical model of epidemic outbreaks, yet for decades it evaded the efforts of the mathematical community to derive an explicit solution. The present paper reports novel analytical results and numerical algorithms suitable for parametric estimation of the SIR model. Notably, a series solution of the incidence variable of the model is derived. It is proven that the explicit solution of the model requires the introduction of a new transcendental special function, describing the incidence, which is a solution of a non-elementary integral equation. The paper introduces iterative algorithms approximating the incidence variable, which allows for estimation of the model parameters from the numbers of observed cases. The approach is applied to the case study of the ongoing coronavirus disease 2019 (COVID-19) pandemic in five European countries: Belgium, Bulgaria, Germany, Italy and the Netherlands. Incidence and case fatality data obtained from the European Centre for Disease Prevention and Control (ECDC) are analysed and the model parameters are estimated and compared for the period Jan-Dec 2020. Full article
(This article belongs to the Special Issue Entropy and Epidemiology)
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13 pages, 322 KiB  
Article
An Ultrametric Random Walk Model for Disease Spread Taking into Account Social Clustering of the Population
by Andrei Khrennikov and Klaudia Oleschko
Entropy 2020, 22(9), 931; https://doi.org/10.3390/e22090931 - 25 Aug 2020
Cited by 11 | Viewed by 2465
Abstract
We present a mathematical model of disease (say a virus) spread that takes into account the hierarchic structure of social clusters in a population. It describes the dependence of epidemic’s dynamics on the strength of barriers between clusters. These barriers are established by [...] Read more.
We present a mathematical model of disease (say a virus) spread that takes into account the hierarchic structure of social clusters in a population. It describes the dependence of epidemic’s dynamics on the strength of barriers between clusters. These barriers are established by authorities as preventative measures; partially they are based on existing socio-economic conditions. We applied the theory of random walk on the energy landscapes represented by ultrametric spaces (having tree-like geometry). This is a part of statistical physics with applications to spin glasses and protein dynamics. To move from one social cluster (valley) to another, a virus (its carrier) should cross a social barrier between them. The magnitude of a barrier depends on the number of social hierarchy levels composing this barrier. Infection spreads rather easily inside a social cluster (say a working collective), but jumps to other clusters are constrained by social barriers. The model implies the power law, 1ta, for approaching herd immunity, where the parameter a is proportional to inverse of one-step barrier Δ. We consider linearly increasing barriers (with respect to hierarchy), i.e., the m-step barrier Δm=mΔ. We also introduce a quantity characterizing the process of infection distribution from one level of social hierarchy to the nearest lower levels, spreading entropy E. The parameter a is proportional to E. Full article
(This article belongs to the Special Issue Entropy and Epidemiology)
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