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Identification of the first COVID-19 infections in the US using a retrospective analysis
Accurate detection of early COVID-19 cases is crucial to drastically reduce infection, hospitalization, and death rates. However, it remains a challenge and methods for identifying initial COVID-19 cases are urgently needed. Here, we used the results from a seroprevalence study in 50 US states to ap...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312904/ https://www.ncbi.nlm.nih.gov/pubmed/34312619 http://dx.doi.org/10.21203/rs.3.rs-707353/v1 |
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author | García-García, David Morales, Enrique de la Fuente-Nunez, Cesar Vigo, Isabel Fonfría, Eva S. Bordehore, Cesar |
author_facet | García-García, David Morales, Enrique de la Fuente-Nunez, Cesar Vigo, Isabel Fonfría, Eva S. Bordehore, Cesar |
author_sort | García-García, David |
collection | PubMed |
description | Accurate detection of early COVID-19 cases is crucial to drastically reduce infection, hospitalization, and death rates. However, it remains a challenge and methods for identifying initial COVID-19 cases are urgently needed. Here, we used the results from a seroprevalence study in 50 US states to apply our Retrospective Methodology to Estimate Daily Infections from Deaths (REMEDID) with the aim of analyzing the initial stages and spread of SARS-CoV-2 infections across the United States (US). Our retrospective data analysis revealed that the virus likely entered the country through California on December 28, 2019, which corresponds to 16 days before the officially recognized entry date established by the CDC. Thus, REMEDID provides evidence that SARS-CoV-2 entered the U.S. earlier than previously reflected in official data. Collectively, our mathematical modeling more accurately estimates the initial COVID-19 cases in the US, may be extrapolated to other countries, and may be used to retrospectively track the progress of the pandemic. Approaches such as REMEDID may enable health authorities to accelerate preventative measures aimed at controlling pandemics within weeks of their onset. |
format | Online Article Text |
id | pubmed-8312904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-83129042021-07-27 Identification of the first COVID-19 infections in the US using a retrospective analysis García-García, David Morales, Enrique de la Fuente-Nunez, Cesar Vigo, Isabel Fonfría, Eva S. Bordehore, Cesar Res Sq Article Accurate detection of early COVID-19 cases is crucial to drastically reduce infection, hospitalization, and death rates. However, it remains a challenge and methods for identifying initial COVID-19 cases are urgently needed. Here, we used the results from a seroprevalence study in 50 US states to apply our Retrospective Methodology to Estimate Daily Infections from Deaths (REMEDID) with the aim of analyzing the initial stages and spread of SARS-CoV-2 infections across the United States (US). Our retrospective data analysis revealed that the virus likely entered the country through California on December 28, 2019, which corresponds to 16 days before the officially recognized entry date established by the CDC. Thus, REMEDID provides evidence that SARS-CoV-2 entered the U.S. earlier than previously reflected in official data. Collectively, our mathematical modeling more accurately estimates the initial COVID-19 cases in the US, may be extrapolated to other countries, and may be used to retrospectively track the progress of the pandemic. Approaches such as REMEDID may enable health authorities to accelerate preventative measures aimed at controlling pandemics within weeks of their onset. American Journal Experts 2021-07-24 /pmc/articles/PMC8312904/ /pubmed/34312619 http://dx.doi.org/10.21203/rs.3.rs-707353/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article García-García, David Morales, Enrique de la Fuente-Nunez, Cesar Vigo, Isabel Fonfría, Eva S. Bordehore, Cesar Identification of the first COVID-19 infections in the US using a retrospective analysis |
title | Identification of the first COVID-19 infections in the US using a retrospective analysis |
title_full | Identification of the first COVID-19 infections in the US using a retrospective analysis |
title_fullStr | Identification of the first COVID-19 infections in the US using a retrospective analysis |
title_full_unstemmed | Identification of the first COVID-19 infections in the US using a retrospective analysis |
title_short | Identification of the first COVID-19 infections in the US using a retrospective analysis |
title_sort | identification of the first covid-19 infections in the us using a retrospective analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312904/ https://www.ncbi.nlm.nih.gov/pubmed/34312619 http://dx.doi.org/10.21203/rs.3.rs-707353/v1 |
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