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COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness
The year 2020 ended with a significant COVID-19 pandemic, which traumatized almost many countries where the lockdowns were restored, and numerous emotional social protests erupted. According to the World Health Organization, the global epidemiological situation in the first months of 2021 deteriorat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179249/ https://www.ncbi.nlm.nih.gov/pubmed/34108816 http://dx.doi.org/10.1016/j.ssci.2021.105370 |
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author | Varotsos, Costas A. Krapivin, Vladimir F. Xue, Yong Soldatov, Vladimir Voronova, Tatiana |
author_facet | Varotsos, Costas A. Krapivin, Vladimir F. Xue, Yong Soldatov, Vladimir Voronova, Tatiana |
author_sort | Varotsos, Costas A. |
collection | PubMed |
description | The year 2020 ended with a significant COVID-19 pandemic, which traumatized almost many countries where the lockdowns were restored, and numerous emotional social protests erupted. According to the World Health Organization, the global epidemiological situation in the first months of 2021 deteriorated. In this paper, the decision-making supporting system (DMSS) is proposed to be an epidemiological prediction tool. COVID-19 trends in several countries and regions, take into account the big data clouds for important geophysical and socio-ecological characteristics and the expected potentials of the medical service, including vaccination and restrictions on population migration both within the country and international traffic. These parameters for numerical simulations are estimated from officially delivered data that allows the verification of theoretical results. The numerical simulations of the transition and the results of COVID-19 are mainly based on the deterministic approach and the algorithm for processing statistical data based on the instability indicator. DMSS has been shown to help predict the effects of COVID-19 depending on the protection strategies against COVID-19 including vaccination. Numerical simulations have shown that DMSS provides results using accompanying information in the appropriate scenario. |
format | Online Article Text |
id | pubmed-8179249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81792492021-06-05 COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness Varotsos, Costas A. Krapivin, Vladimir F. Xue, Yong Soldatov, Vladimir Voronova, Tatiana Saf Sci Article The year 2020 ended with a significant COVID-19 pandemic, which traumatized almost many countries where the lockdowns were restored, and numerous emotional social protests erupted. According to the World Health Organization, the global epidemiological situation in the first months of 2021 deteriorated. In this paper, the decision-making supporting system (DMSS) is proposed to be an epidemiological prediction tool. COVID-19 trends in several countries and regions, take into account the big data clouds for important geophysical and socio-ecological characteristics and the expected potentials of the medical service, including vaccination and restrictions on population migration both within the country and international traffic. These parameters for numerical simulations are estimated from officially delivered data that allows the verification of theoretical results. The numerical simulations of the transition and the results of COVID-19 are mainly based on the deterministic approach and the algorithm for processing statistical data based on the instability indicator. DMSS has been shown to help predict the effects of COVID-19 depending on the protection strategies against COVID-19 including vaccination. Numerical simulations have shown that DMSS provides results using accompanying information in the appropriate scenario. Elsevier Ltd. 2021-10 2021-06-05 /pmc/articles/PMC8179249/ /pubmed/34108816 http://dx.doi.org/10.1016/j.ssci.2021.105370 Text en © 2021 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 Varotsos, Costas A. Krapivin, Vladimir F. Xue, Yong Soldatov, Vladimir Voronova, Tatiana COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness |
title | COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness |
title_full | COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness |
title_fullStr | COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness |
title_full_unstemmed | COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness |
title_short | COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness |
title_sort | covid-19 pandemic decision support system for a population defense strategy and vaccination effectiveness |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179249/ https://www.ncbi.nlm.nih.gov/pubmed/34108816 http://dx.doi.org/10.1016/j.ssci.2021.105370 |
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