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Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance
COVID19 has aptly revealed that airborne viruses such as SARS-CoV-2 with the ability to rapidly mutate combined with high rates of transmission and fatality can cause a deadly worldwide pandemic in a matter of weeks (Plato et al., 2021). Apart from vaccines and post-infection treatment options, stra...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894583/ https://www.ncbi.nlm.nih.gov/pubmed/36655992 http://dx.doi.org/10.7554/eLife.82980 |
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author | Najar, Fares Z Linde, Evan Murphy, Chelsea L Borin, Veniamin A Wang, Huan Haider, Shozeb Agarwal, Pratul K |
author_facet | Najar, Fares Z Linde, Evan Murphy, Chelsea L Borin, Veniamin A Wang, Huan Haider, Shozeb Agarwal, Pratul K |
author_sort | Najar, Fares Z |
collection | PubMed |
description | COVID19 has aptly revealed that airborne viruses such as SARS-CoV-2 with the ability to rapidly mutate combined with high rates of transmission and fatality can cause a deadly worldwide pandemic in a matter of weeks (Plato et al., 2021). Apart from vaccines and post-infection treatment options, strategies for preparedness will be vital in responding to the current and future pandemics. Therefore, there is wide interest in approaches that allow predictions of increase in infections (‘surges’) before they occur. We describe here real-time genomic surveillance particularly based on mutation analysis, of viral proteins as a methodology for a priori determination of surge in number of infection cases. The full results are available for SARS-CoV-2 at http://pandemics.okstate.edu/covid19/, and are updated daily as new virus sequences become available. This approach is generic and will also be applicable to other pathogens. |
format | Online Article Text |
id | pubmed-9894583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-98945832023-02-03 Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance Najar, Fares Z Linde, Evan Murphy, Chelsea L Borin, Veniamin A Wang, Huan Haider, Shozeb Agarwal, Pratul K eLife Epidemiology and Global Health COVID19 has aptly revealed that airborne viruses such as SARS-CoV-2 with the ability to rapidly mutate combined with high rates of transmission and fatality can cause a deadly worldwide pandemic in a matter of weeks (Plato et al., 2021). Apart from vaccines and post-infection treatment options, strategies for preparedness will be vital in responding to the current and future pandemics. Therefore, there is wide interest in approaches that allow predictions of increase in infections (‘surges’) before they occur. We describe here real-time genomic surveillance particularly based on mutation analysis, of viral proteins as a methodology for a priori determination of surge in number of infection cases. The full results are available for SARS-CoV-2 at http://pandemics.okstate.edu/covid19/, and are updated daily as new virus sequences become available. This approach is generic and will also be applicable to other pathogens. eLife Sciences Publications, Ltd 2023-01-19 /pmc/articles/PMC9894583/ /pubmed/36655992 http://dx.doi.org/10.7554/eLife.82980 Text en © 2023, Najar et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Epidemiology and Global Health Najar, Fares Z Linde, Evan Murphy, Chelsea L Borin, Veniamin A Wang, Huan Haider, Shozeb Agarwal, Pratul K Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance |
title | Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance |
title_full | Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance |
title_fullStr | Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance |
title_full_unstemmed | Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance |
title_short | Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance |
title_sort | future covid19 surges prediction based on sars-cov-2 mutations surveillance |
topic | Epidemiology and Global Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894583/ https://www.ncbi.nlm.nih.gov/pubmed/36655992 http://dx.doi.org/10.7554/eLife.82980 |
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