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A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19
The understanding of the interaction between disease dynamics and human behavior is an important and essential point to control infectious. Disease outbreak can be influenced by social distancing and vaccination. In this study, we introduce two compartmental models to derive the epidemic curve and a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046797/ https://www.ncbi.nlm.nih.gov/pubmed/33854079 http://dx.doi.org/10.1038/s41598-021-86873-0 |
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author | Dashtbali, Mohammadali Mirzaie, Mehdi |
author_facet | Dashtbali, Mohammadali Mirzaie, Mehdi |
author_sort | Dashtbali, Mohammadali |
collection | PubMed |
description | The understanding of the interaction between disease dynamics and human behavior is an important and essential point to control infectious. Disease outbreak can be influenced by social distancing and vaccination. In this study, we introduce two compartmental models to derive the epidemic curve and analyze the individual’s behavior in spreading and controlling the COVID-19 epidemic. The first model includes Susceptible, Exposed, Infectious, Hospitalized, Recovered and Death compartments and in the second model, we added a new compartment namely, semi-susceptible individuals that are assumed to be more immune than the susceptible. A comparison of the two models shows that the second model provides a better fit to the daily infected cases from Egypt, Belgium, Japan, Nigeria, Italy, and Germany released by WHO. Finally, we added a vaccinated term to the model to predict how vaccination could control the epidemic. The model was applied on the record data from WHO. |
format | Online Article Text |
id | pubmed-8046797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80467972021-04-15 A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19 Dashtbali, Mohammadali Mirzaie, Mehdi Sci Rep Article The understanding of the interaction between disease dynamics and human behavior is an important and essential point to control infectious. Disease outbreak can be influenced by social distancing and vaccination. In this study, we introduce two compartmental models to derive the epidemic curve and analyze the individual’s behavior in spreading and controlling the COVID-19 epidemic. The first model includes Susceptible, Exposed, Infectious, Hospitalized, Recovered and Death compartments and in the second model, we added a new compartment namely, semi-susceptible individuals that are assumed to be more immune than the susceptible. A comparison of the two models shows that the second model provides a better fit to the daily infected cases from Egypt, Belgium, Japan, Nigeria, Italy, and Germany released by WHO. Finally, we added a vaccinated term to the model to predict how vaccination could control the epidemic. The model was applied on the record data from WHO. Nature Publishing Group UK 2021-04-14 /pmc/articles/PMC8046797/ /pubmed/33854079 http://dx.doi.org/10.1038/s41598-021-86873-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Dashtbali, Mohammadali Mirzaie, Mehdi A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19 |
title | A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19 |
title_full | A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19 |
title_fullStr | A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19 |
title_full_unstemmed | A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19 |
title_short | A compartmental model that predicts the effect of social distancing and vaccination on controlling COVID-19 |
title_sort | compartmental model that predicts the effect of social distancing and vaccination on controlling covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046797/ https://www.ncbi.nlm.nih.gov/pubmed/33854079 http://dx.doi.org/10.1038/s41598-021-86873-0 |
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