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Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection
Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844193/ https://www.ncbi.nlm.nih.gov/pubmed/36650230 http://dx.doi.org/10.1038/s41598-023-27951-3 |
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author | Rufino, Jesús Baquero, Carlos Frey, Davide Glorioso, Christin A. Ortega, Antonio Reščič, Nina Roberts, Julian Charles Lillo, Rosa E. Menezes, Raquel Champati, Jaya Prakash Fernández Anta, Antonio |
author_facet | Rufino, Jesús Baquero, Carlos Frey, Davide Glorioso, Christin A. Ortega, Antonio Reščič, Nina Roberts, Julian Charles Lillo, Rosa E. Menezes, Raquel Champati, Jaya Prakash Fernández Anta, Antonio |
author_sort | Rufino, Jesús |
collection | PubMed |
description | Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a ground-truth subset of cases with known diagnostic test status. This classifier was compared with other competing classifiers and shown to exhibit an increased performance with respect to the ground-truth data. Using the Random Forest classifier, and knowing the vaccination status of the subjects, we then proceeded to analyse the evolution of vaccine efficacy towards infection during different periods, geographies and dominant variants. In South Africa, where the first significant wave of Omicron occurred, a significant reduction of vaccine efficacy is observed from August-September 2021 to December 2021. For instance, the efficacy drops from 0.81 to 0.30 for those vaccinated with 2 doses (of Pfizer/BioNTech), and from 0.51 to 0.09 for those vaccinated with one dose (of Pfizer/BioNTech or Johnson & Johnson). We also extended the study to other countries in which Omicron has been detected, comparing the situation in October 2021 (before Omicron) with that of December 2021. While the reduction measured is smaller than in South Africa, we still found, for instance, an average drop in vaccine efficacy from 0.53 to 0.45 among those vaccinated with two doses. Moreover, we found a significant negative (Pearson) correlation of around − 0.6 between the measured prevalence of Omicron in several countries and the vaccine efficacy in those same countries. This prediction, in January of 2022, of the decreased vaccine efficacy towards Omicron is in line with the subsequent increase of Omicron infections in the first half of 2022. |
format | Online Article Text |
id | pubmed-9844193 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98441932023-01-18 Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection Rufino, Jesús Baquero, Carlos Frey, Davide Glorioso, Christin A. Ortega, Antonio Reščič, Nina Roberts, Julian Charles Lillo, Rosa E. Menezes, Raquel Champati, Jaya Prakash Fernández Anta, Antonio Sci Rep Article Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a ground-truth subset of cases with known diagnostic test status. This classifier was compared with other competing classifiers and shown to exhibit an increased performance with respect to the ground-truth data. Using the Random Forest classifier, and knowing the vaccination status of the subjects, we then proceeded to analyse the evolution of vaccine efficacy towards infection during different periods, geographies and dominant variants. In South Africa, where the first significant wave of Omicron occurred, a significant reduction of vaccine efficacy is observed from August-September 2021 to December 2021. For instance, the efficacy drops from 0.81 to 0.30 for those vaccinated with 2 doses (of Pfizer/BioNTech), and from 0.51 to 0.09 for those vaccinated with one dose (of Pfizer/BioNTech or Johnson & Johnson). We also extended the study to other countries in which Omicron has been detected, comparing the situation in October 2021 (before Omicron) with that of December 2021. While the reduction measured is smaller than in South Africa, we still found, for instance, an average drop in vaccine efficacy from 0.53 to 0.45 among those vaccinated with two doses. Moreover, we found a significant negative (Pearson) correlation of around − 0.6 between the measured prevalence of Omicron in several countries and the vaccine efficacy in those same countries. This prediction, in January of 2022, of the decreased vaccine efficacy towards Omicron is in line with the subsequent increase of Omicron infections in the first half of 2022. Nature Publishing Group UK 2023-01-17 /pmc/articles/PMC9844193/ /pubmed/36650230 http://dx.doi.org/10.1038/s41598-023-27951-3 Text en © The Author(s) 2023, corrected publication 2023 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 Rufino, Jesús Baquero, Carlos Frey, Davide Glorioso, Christin A. Ortega, Antonio Reščič, Nina Roberts, Julian Charles Lillo, Rosa E. Menezes, Raquel Champati, Jaya Prakash Fernández Anta, Antonio Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection |
title | Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection |
title_full | Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection |
title_fullStr | Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection |
title_full_unstemmed | Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection |
title_short | Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection |
title_sort | using survey data to estimate the impact of the omicron variant on vaccine efficacy against covid-19 infection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844193/ https://www.ncbi.nlm.nih.gov/pubmed/36650230 http://dx.doi.org/10.1038/s41598-023-27951-3 |
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