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      Alpha to Omicron: Disease Severity and Clinical Outcomes of Major SARS-CoV-2 Variants

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          Abstract

          Background

          Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited.

          Method

          Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes.

          Results

          In total 2779 patients identified with either Alpha (n = 1153), Gamma (n = 122), Delta (n = 808), or Omicron variants (n = 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (χ 2 = 12.8, P < .001; χ 2 = 21.6, P < .002; χ 2 = 9.6, P = .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9%] and 513/696 [73.7%], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2.

          Conclusions

          Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

          Abstract

          Four SARS-CoV-2 variants predominated in the United States since 2021. Disease severity associated with Alpha, Gamma, and Delta variants were comparable while Omicron infections are significantly less severe. Additionally, no differences in Omicron subvariant severity (BA1 vs BA2) were identified.

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          Most cited references31

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          Is Open Access

          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

            Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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              The REDCap consortium: Building an international community of software platform partners

              The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.
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                Author and article information

                Contributors
                Journal
                J Infect Dis
                J Infect Dis
                jid
                The Journal of Infectious Diseases
                Oxford University Press
                0022-1899
                1537-6613
                10 October 2022
                10 October 2022
                : jiac411
                Affiliations
                Center for Pediatric Infectious Disease, Cleveland Clinic Children’s , Cleveland, Ohio, USA
                Department of Computer and Data Sciences, Case Western Reserve University , Cleveland, Ohio, USA
                Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic , Cleveland, Ohio, USA
                Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic , Cleveland, Ohio, USA
                Center for Pediatric Infectious Disease, Cleveland Clinic Children’s , Cleveland, Ohio, USA
                Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic , Cleveland, Ohio, USA
                Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic , Cleveland, Ohio, USA
                Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic , Cleveland, Ohio, USA
                American Board of Pathology , Tampa, Florida, USA
                Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic , Cleveland, Ohio, USA
                Lerner Research Institute, Cleveland Clinic , Cleveland, Ohio, USA
                Department of Computer and Data Sciences, Case Western Reserve University , Cleveland, Ohio, USA
                Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic , Cleveland, Ohio, USA
                Author notes
                Correspondence: F. Esper, MD, Center for Pediatric Infectious Diseases, Cleveland Clinic Children's, 9500 Euclid Avenue, Cleveland, OH 44195 ( esperf@ 123456ccf.org ).

                Potential conflicts of interest. D. D. R. performs collaborative research that is sponsored by industry collaborators BD, bioMerieux, Cepheid, Cleveland Diagnostics, Hologic, Luminex, Q-Linea, Qiagen, Roche, Specific Diagnostics, Thermo Fisher, and Vela; and is or has been on advisory boards for Luminex, Talis Biomedical, and Thermo Fisher. F. E. serves as consultant to Proctor and Gamble. All other authors report no potential conflicts.

                Author information
                https://orcid.org/0000-0003-0102-7830
                https://orcid.org/0000-0002-7636-5191
                Article
                jiac411
                10.1093/infdis/jiac411
                9619650
                36214810
                2fc3b430-d447-44e9-8a9e-0f1864afdeda
                © The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

                This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                : 09 July 2022
                : 06 October 2022
                : 03 October 2022
                : 27 October 2022
                Page count
                Pages: 9
                Funding
                Funded by: National Science Foundation, doi 10.13039/100000001;
                Award ID: IIS-2027667
                Award ID: CCF-2006780
                Award ID: CCF-1815139
                Award ID: NS097719
                Funded by: Robert J. Tomsich Pathology and Laboratory Medicine Institute;
                Categories
                Major Article
                AcademicSubjects/MED00290
                Custom metadata
                corrected-proof
                PAP

                Infectious disease & Microbiology
                covid-19,sars-cov-2,clinical severity,delta,omicron,variant
                Infectious disease & Microbiology
                covid-19, sars-cov-2, clinical severity, delta, omicron, variant

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