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Consecutive Rate Model for Covid Infections and Deaths and Prediction of Level-Off Time
[Image: see text] Covid-19 infection and death rates are predicted based on a simple two-step consecutive reaction rate model. The infection rate is analogous to the first step of a consecutive reaction that results in an intermediate, and the death rate is analogous to the second step of the consec...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762421/ https://www.ncbi.nlm.nih.gov/pubmed/36569213 http://dx.doi.org/10.1021/acsomega.2c06006 |
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author | Madiligama, Amila Vandervort, Zachary Khan, Arshad |
author_facet | Madiligama, Amila Vandervort, Zachary Khan, Arshad |
author_sort | Madiligama, Amila |
collection | PubMed |
description | [Image: see text] Covid-19 infection and death rates are predicted based on a simple two-step consecutive reaction rate model. The infection rate is analogous to the first step of a consecutive reaction that results in an intermediate, and the death rate is analogous to the second step of the consecutive reaction in which a small fraction of the intermediate terminates in a product formation irreversibly. The model has been thoroughly tested, especially with infection data from different countries and two of the USA states California and New York, and predicts a linear infection–time relationship in the early stage of Covid infection. That is, the number of infections in 6 days is double the number of infections in 3 days, and infections in 9 days is 3 times the number of infections in 3 days, etc. In the later stage, the infection curve deviates from the linear relationship and follows a first-order constant “half-life” relationship. In the time interval of one half-life, the infection rises to 50% of the level-off value (maximum); during the second half-life, it rises by another 25% (50/2); and in the third half-life, it rises by another 12.5% (25/2), etc. That is, the infection curve reaches 50% (one “half-life”), 75% (two half-lives), 87.5% (three half-lives), etc. of the level off value after the time interval of one to three half-lives. Available data support our predictions. |
format | Online Article Text |
id | pubmed-9762421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-97624212022-12-19 Consecutive Rate Model for Covid Infections and Deaths and Prediction of Level-Off Time Madiligama, Amila Vandervort, Zachary Khan, Arshad ACS Omega [Image: see text] Covid-19 infection and death rates are predicted based on a simple two-step consecutive reaction rate model. The infection rate is analogous to the first step of a consecutive reaction that results in an intermediate, and the death rate is analogous to the second step of the consecutive reaction in which a small fraction of the intermediate terminates in a product formation irreversibly. The model has been thoroughly tested, especially with infection data from different countries and two of the USA states California and New York, and predicts a linear infection–time relationship in the early stage of Covid infection. That is, the number of infections in 6 days is double the number of infections in 3 days, and infections in 9 days is 3 times the number of infections in 3 days, etc. In the later stage, the infection curve deviates from the linear relationship and follows a first-order constant “half-life” relationship. In the time interval of one half-life, the infection rises to 50% of the level-off value (maximum); during the second half-life, it rises by another 25% (50/2); and in the third half-life, it rises by another 12.5% (25/2), etc. That is, the infection curve reaches 50% (one “half-life”), 75% (two half-lives), 87.5% (three half-lives), etc. of the level off value after the time interval of one to three half-lives. Available data support our predictions. American Chemical Society 2022-12-14 /pmc/articles/PMC9762421/ /pubmed/36569213 http://dx.doi.org/10.1021/acsomega.2c06006 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Madiligama, Amila Vandervort, Zachary Khan, Arshad Consecutive Rate Model for Covid Infections and Deaths and Prediction of Level-Off Time |
title | Consecutive Rate Model for Covid Infections and Deaths
and Prediction of Level-Off Time |
title_full | Consecutive Rate Model for Covid Infections and Deaths
and Prediction of Level-Off Time |
title_fullStr | Consecutive Rate Model for Covid Infections and Deaths
and Prediction of Level-Off Time |
title_full_unstemmed | Consecutive Rate Model for Covid Infections and Deaths
and Prediction of Level-Off Time |
title_short | Consecutive Rate Model for Covid Infections and Deaths
and Prediction of Level-Off Time |
title_sort | consecutive rate model for covid infections and deaths
and prediction of level-off time |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762421/ https://www.ncbi.nlm.nih.gov/pubmed/36569213 http://dx.doi.org/10.1021/acsomega.2c06006 |
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