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39 results for “Omicron variant”
Supplementary Data: OpenCOVID model output underlaying Figures 1 and 2 of "Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden"
<p>Supplementary data files <strong>Figure_1.xlsx</strong> and <strong>Figure_2.xlsx</strong> contain the model simulation outcomes for Figures 1 and 2 of <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al</em></a> "<strong>Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden</strong>" (2022)</p> <ul> <li><strong>Figure 1</strong>: Peak daily hospital occupancy (number of beds per 100,000 population over the six-month simulation period) for three variant properties; infectivity (relative to Delta), immune evading capacity (%), and severity (relative to Delta)<br> </li> <li><strong>Figure 2</strong>: Percentage of COVID-19 infections and deaths averted by third-dose vaccines for adults and vaccinating 5-11-year-olds with doses one and two.<br> </li> <li>Open access source-codes of the associated plotting functions are published <a href="http://zenodo.org/record/6532404#.Yqw7cezMKdb">here</a> on Zenodo.<br> </li> <li>Open access source-codes for the OpenCOVID model of all analyses as presented in <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al.</em> (2022)</a> are publicly available at <a href="https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src">https://github.com/SwissTPH/OpenCOVID/tree/manuscript_december_2021/src</a>.<br> </li> <li>Detailed model descriptions and model equations of individual-based transmission model <strong>OpenCOVID</strong> are described in <a href="https://pubmed.ncbi.nlm.nih.gov/34923396/">Shattock <em>et al</em>. (2022)</a> and <a href="https://www.medrxiv.org/content/10.1101/2021.12.12.21267673v2">Le Rutte, Shattock <em>et al.</em> (2022).</a></li> </ul>
Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 06 (SARS-CoV-2 Omicron B.1.1.529; BA.2)
<p>Dataset 06 comprises 164 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Omicron B.1.1.529; BA.2) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 06 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Adsorption of the WT, delta and omicron variants onto hydrophobic and hydrophilic surfaces
<p>Adsorption of the WT, delta and omicron variants onto hydrophobic and hydrophilic surfaces. This contains 3 replicas of each.</p>
Adsorption patterns of the WT, delta and omicron variants onto hydrophobic and hydrophilic surfaces
<p>Adsorption of the WT, delta and omicron variants onto hydrophobic and hydrophilic surfaces. This contains 3 replicas of each.</p>
Neutralizing immunity induced against the Omicron BA.2 variant in vaccinated and unvaccinated individuals infected by Omicron BA.1
<p>This repository contains the pertinent data files used in the manuscript, <em>Neutralizing immunity induced against the Omicron BA.2 variant in vaccinated and unvaccinated individuals infected by Omicron BA.1</em>.</p>
AMTraC-19 (v7.9) Dataset: Persistence of the Omicron variant of SARS-CoV-2 in Australia
<p>The paper describing scenarios which generated this dataset:</p> <p>S. L. Chang, Q. D. Nguyen, A. Martiniuk, V. Sintchenko, T. C. Sorrell, M. Prokopenko, Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing, <em>PLOS Global Public Health</em>, 3(4): e0001427, 2023.</p> <p>The AMTraC-19 source code (v7.9) is released on Zenodo: https://zenodo.org/record/7325675</p>
SARS-CoV-2 vaccine breakthrough infections by Omicron and Delta variants in healthcare workers
<p>An observational prospective cohort study was conducted in vaccinated employees with acute SARS-CoV-2 infection between October 2021 and February 2022. Serological and molecular testing was performed to determine SARS-CoV-2 viral load, lineage, antibody levels, and neutral-ising antibody titres. A total of 571 (9.7%) employees experienced SARS-CoV-2 breakthrough infections during the enrolment period, of which 81 were included. The majority (n=79, 97.5%) was symptomatic and most (n=75, 92.6%) showed Ct-values < 30 in RT-PCR assays. Twenty-four (30%) remained PCR-positive for > 15 days. Neutralizing antibody titres were strongest for the wildtype, intermediate for Delta and lowest for Omicron variants. Omicron infections occurred at higher anti-RBD-IgG serum levels (p= 0.00001) and showed a trend for higher viral loads (p=0.14, median Ct-difference 4.3, 95% CI [-2.5-10.5]). For both variants, viral loads were signifi-cantly higher in participants with lower anti-RBD-IgG serum levels (p=0.02). </p>
Supplementary Data for: "The principles of SARS-CoV-2 inter-variant competition are exemplified in the pre-Omicron era of the Colombian epidemic"
<p>Supplementary data files, including BEAST XMLs and logs from the study "The principles of SARS-CoV-2 inter-variant competition are exemplified in the pre-Omicron era of the Colombian epidemic".</p>
A Study to Evaluate the Immunogenicity and Safety of Omicron Variant Vaccines in Comparison With mRNA-1273 Booster Vaccine for COVID-19
ClinicalTrials.gov study NCT05249829. IPD Sharing: NO. Countries: 1. Publications: 1.
The omicron variant of SARS-CoV-2 drove broadly increased seroprevalence in a public university setting
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Neutralizing immunity in vaccine breakthrough infections from the SARS-CoV-2 Omicron and Delta variants
<p>Scripts and metadata used in the analysis of the manuscript, <em>Neutralizing immunity in vaccine breakthrough infections from the SARS-CoV-2 Omicron and Delta variants.</em></p>
Supplementary Information of Amplicon-based nanopore sequencing of patients with COVID-19 omicron (B.1.1.529) variant from India
<p><strong>We report sequencing of omicron variants from SARS-CoV-2 in 75 patients, using Nanopore long-read sequencing chemistry. We highlight the nature of mutations in spike glycoprotein that are unique and common to other populations.</strong></p> <p> </p>
Twitter Conversations about the COVID-19 Omicron Variant: A Large Scale Dataset of more than 500,000 Tweets
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049</p> <p><strong>Abstract</strong></p> <p>This open-access dataset is one of the salient contributions of the above-mentioned paper. It presents a total of <strong>522,886</strong> Tweet IDs of the same number of <strong>Tweets about the SARS-CoV-2 Omicron Variant</strong> posted on Twitter since the first detected case of this variant on November 24, 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p><strong>Data Description</strong></p> <p>The Tweet IDs are presented in 7 different .txt files based on the timelines of the associated tweets. The data collection followed a keyword-based approach and tweets comprising the "omicron" keyword were filtered, collected, and added to this dataset. The following is the description of these dataset files.</p> <ul> <li>Filename: TweetIDs_November.txt (No. of Tweet IDs: 16471, Date Range of the Tweet IDs: November 24, 2021 to November 30, 2021)</li> <li>Filename: TweetIDs_December.txt (No. of Tweet IDs: 99288, Date Range of the Tweet IDs: December 1, 2021 to December 31, 2021)</li> <li>Filename: TweetIDs_January.txt (No. of Tweet IDs: 92860, Date Range of the Tweet IDs: January 1, 2022 to January 31, 2022)</li> <li>Filename: TweetIDs_February.txt (No. of Tweet IDs: 89080, Date Range of the Tweet IDs: February 1, 2022 to February 28, 2022)</li> <li>Filename: TweetIDs_March.txt (No. of Tweet IDs: 97844, Date Range of the Tweet IDs: March 1, 2022 to March 31, 2022)</li> <li>Filename: TweetIDs_April.txt (No. of Tweet IDs: 91587, Date Range of the Tweet IDs: April 1, 2022 to April 20, 2022)</li> <li>Filename: TweetIDs_May.txt (No. of Tweet IDs: 35756, Date Range of the Tweet IDs: May 1, 2022 to May 12, 2022)</li> </ul> <p>In the above table, the last date for May is May 12 as it was the most recent date at the time of data collection and dataset upload. The dataset would be updated soon to incorporate more recent tweets.</p> <p>The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link to download</a> and a <a href="https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">step-by-step tutorial</a> on how to use Hydrator) may be used.</p>
A Trial Evaluate the Immunogenicity and Safety of Recombinant COVID-19 Omicron-Delta Variant Vaccine (CHO Cell)
ClinicalTrials.gov study NCT05616754. IPD Sharing: NO. Countries: 1. Publications: 1.
Left Atrial Function of COVID-19-recovered Patients During the Surge of Omicron Variants
ClinicalTrials.gov study NCT06170307. IPD Sharing: NO. Countries: 1. Publications: 1.
Immunogenicity and Safety of Booster Immunization of COVID-19 Vaccine (Vero Cell), Inactivated (Omicron Variant) in Healthy People Aged 18 Years and Above
ClinicalTrials.gov study NCT05381350. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Immunogenicity Evaluation of Omicron Variant-based Vaccine and a Trivalent Vaccine in Adults Against COVID-19 in Chile
ClinicalTrials.gov study NCT05593042. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Safety and Immunogenicity of Omicron Variant-Matched Vaccine Booster in Adults
ClinicalTrials.gov study NCT05383560. IPD Sharing: YES. Countries: 1. Publications: 1.
Clinical Trial of COVID-19 Vaccine（SARS-CoV-2 Variant(Omicron BA.5) mRNA Vaccine） in Participants Aged 18 Years and Over
ClinicalTrials.gov study NCT05745545. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Fits and plot data for the paper: "Combined analyses of within-host SARS-CoV-2 viral kinetics and information on past exposures to the virus in a human cohort identifies intrinsic differences of Omicron and Delta variants"
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OpenNeuro
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