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Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19?
SIMPLE SUMMARY: Nowadays, enhancing development of mathematical models is very important to help in the prediction of coronavirus disease 2019 (COVID1-19). However, the vast majority of published model-based predictions do not cover people who left the epidemic COVID-19 positive (alive) and they mus...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923266/ https://www.ncbi.nlm.nih.gov/pubmed/33669895 http://dx.doi.org/10.3390/biology10020165 |
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author | Boselli, Pietro M. Basagni, Massimo Soriano, Jose M. |
author_facet | Boselli, Pietro M. Basagni, Massimo Soriano, Jose M. |
author_sort | Boselli, Pietro M. |
collection | PubMed |
description | SIMPLE SUMMARY: Nowadays, enhancing development of mathematical models is very important to help in the prediction of coronavirus disease 2019 (COVID1-19). However, the vast majority of published model-based predictions do not cover people who left the epidemic COVID-19 positive (alive) and they must be included in studies to guarantee a more accurate model for application in public health. The epidemic development phenomenon can be obtained with a modelling framework. ABSTRACT: On 11 March 2020, coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO). As of 12.44 GMT on 15 January 2021, it has produced 93,640,296 cases and 2,004,984 deaths. The use of mathematical modelling was applied in Italy, Spain, and UK to help in the prediction of this pandemic. We used equations from general and reduced logistic models to describe the epidemic development phenomenon and the trend over time. We extracted this information from the Italian Ministry of Health, the Spanish Ministry of Health, Consumer Affairs, and Social Welfare, and the UK Statistics Authority from 3 February to 30 April 2020. We estimated that, from the seriousness of the phenomenon, the consequent pathology, and the lethal outcomes, the COVID-19 trend relate to the same classic laws that govern epidemics and their evolution. The curve d(t) helps to obtain information on the duration of the epidemic phenomenon, as its evolution is related to the efficiency and timeliness of the system, control, diagnosis, and treatment. In fact, the analysis of this curve, after acquiring the data of the first three weeks, also favors the advantage to formulate forecast hypotheses on the progress of the epidemic. |
format | Online Article Text |
id | pubmed-7923266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79232662021-03-03 Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19? Boselli, Pietro M. Basagni, Massimo Soriano, Jose M. Biology (Basel) Article SIMPLE SUMMARY: Nowadays, enhancing development of mathematical models is very important to help in the prediction of coronavirus disease 2019 (COVID1-19). However, the vast majority of published model-based predictions do not cover people who left the epidemic COVID-19 positive (alive) and they must be included in studies to guarantee a more accurate model for application in public health. The epidemic development phenomenon can be obtained with a modelling framework. ABSTRACT: On 11 March 2020, coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO). As of 12.44 GMT on 15 January 2021, it has produced 93,640,296 cases and 2,004,984 deaths. The use of mathematical modelling was applied in Italy, Spain, and UK to help in the prediction of this pandemic. We used equations from general and reduced logistic models to describe the epidemic development phenomenon and the trend over time. We extracted this information from the Italian Ministry of Health, the Spanish Ministry of Health, Consumer Affairs, and Social Welfare, and the UK Statistics Authority from 3 February to 30 April 2020. We estimated that, from the seriousness of the phenomenon, the consequent pathology, and the lethal outcomes, the COVID-19 trend relate to the same classic laws that govern epidemics and their evolution. The curve d(t) helps to obtain information on the duration of the epidemic phenomenon, as its evolution is related to the efficiency and timeliness of the system, control, diagnosis, and treatment. In fact, the analysis of this curve, after acquiring the data of the first three weeks, also favors the advantage to formulate forecast hypotheses on the progress of the epidemic. MDPI 2021-02-19 /pmc/articles/PMC7923266/ /pubmed/33669895 http://dx.doi.org/10.3390/biology10020165 Text en © 2021 by the authors. 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 Boselli, Pietro M. Basagni, Massimo Soriano, Jose M. Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19? |
title | Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19? |
title_full | Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19? |
title_fullStr | Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19? |
title_full_unstemmed | Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19? |
title_short | Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19? |
title_sort | are there any parameters missing in the mathematical models applied in the process of spreading covid-19? |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923266/ https://www.ncbi.nlm.nih.gov/pubmed/33669895 http://dx.doi.org/10.3390/biology10020165 |
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