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A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis
This study aims to provide both a model by using cumulative cases and cumulative death toll for SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) outbreak in four countries, China, Italy, South Korea, and Turkey, starting from the first diagnosis and to compare associated indicators. The...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251623/ https://www.ncbi.nlm.nih.gov/pubmed/32450712 http://dx.doi.org/10.1177/1010539520928180 |
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author | Ankarali, Handan Ankarali, Seyit Caskurlu, Hulya Cag, Yasemin Arslan, Ferhat Erdem, Hakan Vahaboglu, Haluk |
author_facet | Ankarali, Handan Ankarali, Seyit Caskurlu, Hulya Cag, Yasemin Arslan, Ferhat Erdem, Hakan Vahaboglu, Haluk |
author_sort | Ankarali, Handan |
collection | PubMed |
description | This study aims to provide both a model by using cumulative cases and cumulative death toll for SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) outbreak in four countries, China, Italy, South Korea, and Turkey, starting from the first diagnosis and to compare associated indicators. The most successful estimation was obtained from the cubic model with natural logarithm for China, Italy, South Korea, and Turkey. The success of the models was around 99%. However, differences began to emerge in China, Italy, and South Korea after the second week. Although the highest number of new cases per 1 million people in China was 9.8 on February 28, 2020; it was 108.4 on March 21, 2020, in Italy; and this was 16.6 on March 5, 2020, in South Korea. On the other hand, the number of new cases was 24.6 per 1 million people on March 27, 2020, in Turkey. The log-cubic model proposed in this study has been set forth to obtain successful results for aforementioned countries, as well as to estimate the course of the COVID-19 outbreak. Other factors such as climacteric factors and genetic differences, which may have an impact on viral spreading and transmission, would also have strengthened the model prediction capacity. |
format | Online Article Text |
id | pubmed-7251623 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-72516232020-05-27 A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis Ankarali, Handan Ankarali, Seyit Caskurlu, Hulya Cag, Yasemin Arslan, Ferhat Erdem, Hakan Vahaboglu, Haluk Asia Pac J Public Health COVID Articles This study aims to provide both a model by using cumulative cases and cumulative death toll for SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) outbreak in four countries, China, Italy, South Korea, and Turkey, starting from the first diagnosis and to compare associated indicators. The most successful estimation was obtained from the cubic model with natural logarithm for China, Italy, South Korea, and Turkey. The success of the models was around 99%. However, differences began to emerge in China, Italy, and South Korea after the second week. Although the highest number of new cases per 1 million people in China was 9.8 on February 28, 2020; it was 108.4 on March 21, 2020, in Italy; and this was 16.6 on March 5, 2020, in South Korea. On the other hand, the number of new cases was 24.6 per 1 million people on March 27, 2020, in Turkey. The log-cubic model proposed in this study has been set forth to obtain successful results for aforementioned countries, as well as to estimate the course of the COVID-19 outbreak. Other factors such as climacteric factors and genetic differences, which may have an impact on viral spreading and transmission, would also have strengthened the model prediction capacity. SAGE Publications 2020-05-25 2020-05 /pmc/articles/PMC7251623/ /pubmed/32450712 http://dx.doi.org/10.1177/1010539520928180 Text en © 2020 APJPH https://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | COVID Articles Ankarali, Handan Ankarali, Seyit Caskurlu, Hulya Cag, Yasemin Arslan, Ferhat Erdem, Hakan Vahaboglu, Haluk A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis |
title | A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis |
title_full | A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis |
title_fullStr | A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis |
title_full_unstemmed | A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis |
title_short | A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis |
title_sort | statistical modeling of the course of covid-19 (sars-cov-2) outbreak: a comparative analysis |
topic | COVID Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251623/ https://www.ncbi.nlm.nih.gov/pubmed/32450712 http://dx.doi.org/10.1177/1010539520928180 |
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