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Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule
The COVID-19 pandemic has world-widely motivated numerous attempts to properly adjust classical epidemiological models, namely those of the SEIR-type, to the spreading characteristics of the novel Corona virus. In this context, the fundamental structure of the differential equations making up the SE...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7261113/ https://www.ncbi.nlm.nih.gov/pubmed/32508398 http://dx.doi.org/10.1016/j.chaos.2020.109891 |
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author | Scheiner, Stefan Ukaj, Niketa Hellmich, Christian |
author_facet | Scheiner, Stefan Ukaj, Niketa Hellmich, Christian |
author_sort | Scheiner, Stefan |
collection | PubMed |
description | The COVID-19 pandemic has world-widely motivated numerous attempts to properly adjust classical epidemiological models, namely those of the SEIR-type, to the spreading characteristics of the novel Corona virus. In this context, the fundamental structure of the differential equations making up the SEIR models has remained largely unaltered—presuming that COVID-19 may be just “another epidemic”. We here take an alternative approach, by investigating the relevance of one key ingredient of the SEIR models, namely the death kinetics law. The latter is compared to an alternative approach, which we call infection-to-death delay rule. For that purpose, we check how well these two mathematical formulations are able to represent the publicly available country-specific data on recorded fatalities, across a selection of 57 different nations. Thereby, we consider that the model-governing parameters—namely, the death transmission coefficient for the death kinetics model, as well as the apparent fatality-to-case fraction and the characteristic fatal illness period for the infection-to-death delay rule—are time-invariant. For 55 out of the 57 countries, the infection-to-death delay rule turns out to represent the actual situation significantly more precisely than the classical death kinetics rule. We regard this as an important step towards making SEIR-approaches more fit for the COVID-19 spreading prediction challenge. |
format | Online Article Text |
id | pubmed-7261113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72611132020-06-01 Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule Scheiner, Stefan Ukaj, Niketa Hellmich, Christian Chaos Solitons Fractals Article The COVID-19 pandemic has world-widely motivated numerous attempts to properly adjust classical epidemiological models, namely those of the SEIR-type, to the spreading characteristics of the novel Corona virus. In this context, the fundamental structure of the differential equations making up the SEIR models has remained largely unaltered—presuming that COVID-19 may be just “another epidemic”. We here take an alternative approach, by investigating the relevance of one key ingredient of the SEIR models, namely the death kinetics law. The latter is compared to an alternative approach, which we call infection-to-death delay rule. For that purpose, we check how well these two mathematical formulations are able to represent the publicly available country-specific data on recorded fatalities, across a selection of 57 different nations. Thereby, we consider that the model-governing parameters—namely, the death transmission coefficient for the death kinetics model, as well as the apparent fatality-to-case fraction and the characteristic fatal illness period for the infection-to-death delay rule—are time-invariant. For 55 out of the 57 countries, the infection-to-death delay rule turns out to represent the actual situation significantly more precisely than the classical death kinetics rule. We regard this as an important step towards making SEIR-approaches more fit for the COVID-19 spreading prediction challenge. Elsevier Ltd. 2020-07 2020-05-30 /pmc/articles/PMC7261113/ /pubmed/32508398 http://dx.doi.org/10.1016/j.chaos.2020.109891 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Scheiner, Stefan Ukaj, Niketa Hellmich, Christian Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule |
title | Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule |
title_full | Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule |
title_fullStr | Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule |
title_full_unstemmed | Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule |
title_short | Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule |
title_sort | mathematical modeling of covid-19 fatality trends: death kinetics law versus infection-to-death delay rule |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7261113/ https://www.ncbi.nlm.nih.gov/pubmed/32508398 http://dx.doi.org/10.1016/j.chaos.2020.109891 |
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