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Guesstimating the COVID-19 burden: what is the best model?
There has been significant underreporting in countries that employ only a symptom-based algorithmic testing approach for COVID-19, focusing exclusively on health-conscious people who present to facilities (volunteer bias). Mass-level, population-based serologic testing has demonstrated groundbreakin...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9212247/ http://dx.doi.org/10.1016/B978-0-323-99277-0.00027-9 |
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author | Javed, Wajiha Farooq, Wajiha Jaffari, Anzal Abbas |
author_facet | Javed, Wajiha Farooq, Wajiha Jaffari, Anzal Abbas |
author_sort | Javed, Wajiha |
collection | PubMed |
description | There has been significant underreporting in countries that employ only a symptom-based algorithmic testing approach for COVID-19, focusing exclusively on health-conscious people who present to facilities (volunteer bias). Mass-level, population-based serologic testing has demonstrated groundbreaking results in assessing the true prevalence of COVID-19, as opposed to PCR-based positivity rates used by most governments to report official figures (which fail to capture the proportion of asymptomatic yet positive cases within the general population). Seroprevalence findings from a large-scale census in Pakistan between April and July indicated 17.7 times higher prevalence as compared to traditional PCR government testing within the same timeframe. Emerging research on COVID-19 transmission illustrates how asymptomatic infections within a country may be manyfold higher than the number of PCR reported cases. In contrast to PCR tests, serologic tests are based on the qualitative, as well as titers of IgM and IgG, generated by the body in response to a SARS-CoV-2 infection. Serologic tests can detect asymptomatic carriers and assess past exposure, whereas PCR has a high false-negative rate, especially when the viral load is low, giving it a false assurance while continuing to unknowingly spread the infection. As research demonstrates that the extent of silent transmission of COVID-19 in a population may not be captured by an exclusively PCR-focused testing methodology, the most effective way to conduct massive level testing is through serologic tests as they minimize the need for hospital settings, reduce the pressure on an already overwhelmed health system, and assess the true prevalence of the disease. |
format | Online Article Text |
id | pubmed-9212247 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-92122472022-06-22 Guesstimating the COVID-19 burden: what is the best model? Javed, Wajiha Farooq, Wajiha Jaffari, Anzal Abbas Pandemic Risk, Response, and Resilience Article There has been significant underreporting in countries that employ only a symptom-based algorithmic testing approach for COVID-19, focusing exclusively on health-conscious people who present to facilities (volunteer bias). Mass-level, population-based serologic testing has demonstrated groundbreaking results in assessing the true prevalence of COVID-19, as opposed to PCR-based positivity rates used by most governments to report official figures (which fail to capture the proportion of asymptomatic yet positive cases within the general population). Seroprevalence findings from a large-scale census in Pakistan between April and July indicated 17.7 times higher prevalence as compared to traditional PCR government testing within the same timeframe. Emerging research on COVID-19 transmission illustrates how asymptomatic infections within a country may be manyfold higher than the number of PCR reported cases. In contrast to PCR tests, serologic tests are based on the qualitative, as well as titers of IgM and IgG, generated by the body in response to a SARS-CoV-2 infection. Serologic tests can detect asymptomatic carriers and assess past exposure, whereas PCR has a high false-negative rate, especially when the viral load is low, giving it a false assurance while continuing to unknowingly spread the infection. As research demonstrates that the extent of silent transmission of COVID-19 in a population may not be captured by an exclusively PCR-focused testing methodology, the most effective way to conduct massive level testing is through serologic tests as they minimize the need for hospital settings, reduce the pressure on an already overwhelmed health system, and assess the true prevalence of the disease. 2022 2022-06-17 /pmc/articles/PMC9212247/ http://dx.doi.org/10.1016/B978-0-323-99277-0.00027-9 Text en Copyright © 2022 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Javed, Wajiha Farooq, Wajiha Jaffari, Anzal Abbas Guesstimating the COVID-19 burden: what is the best model? |
title | Guesstimating the COVID-19 burden: what is the best model? |
title_full | Guesstimating the COVID-19 burden: what is the best model? |
title_fullStr | Guesstimating the COVID-19 burden: what is the best model? |
title_full_unstemmed | Guesstimating the COVID-19 burden: what is the best model? |
title_short | Guesstimating the COVID-19 burden: what is the best model? |
title_sort | guesstimating the covid-19 burden: what is the best model? |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9212247/ http://dx.doi.org/10.1016/B978-0-323-99277-0.00027-9 |
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