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Infectious Diseases

Application of Statistical Learning to Identify Omicron Mutations in SARS-CoV-2 Viral Genome Sequence Data From Populations in Africa and the United States

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To identify the key insights or developments described in this article

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JAMA Network Open

Published Online: September 7, 2022

Original Investigation

Article Information and Disclosures

Lue Ping Zhao, PhD¹;

Terry P. Lybrand, PhD^2,3;

Peter Gilbert, PhD⁴;

Margaret Madeleine, PhD¹;

Thomas H. Payne, MD⁵;

Seth Cohen, MD^4,5;

Daniel E. Geraghty, PhD⁶;

Keith R. Jerome, MD, PhD^4,5;

Lawrence Corey, MD⁴

Author Affiliations

¹Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
²Quintepa Computing LLC, Nashville, Tennessee
³Department of Chemistry, Vanderbilt University, Nashville, Tennessee
⁴Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
⁵Department of Medicine, University of Washington School of Medicine, Seattle
⁶Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington

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Key Points

Question Could the SARS-CoV-2 Omicron variant have been detected earlier with existing surveillance data and a state-of-the-art statistical learning strategy?

Findings In this case series of 2698 Omicron cases in Africa and 12 141 Omicron cases in the United States, a statistical learning strategy found that Omicron was dynamically expanding in Africa and the United States with trackable expansion over time. The results indicated that Omicron could have been detected 20 days earlier in Africa; similarly, 8 Omicron cases were detected in the United States by November 25, 2021, prior to the official US Centers for Disease Control and Prevention declaration.

Meaning These findings suggest that novel data analytics such as statistical learning strategy may have applications for surveillance of SARS-CoV-2 variants.

Abstract

Importance With timely collection of SARS-CoV-2 viral genome sequences, it is important to apply efficient data analytics to detect emerging variants at the earliest time.

Objective To evaluate the application of a statistical learning strategy (SLS) to improve early detection of novel SARS-CoV-2 variants using viral sequence data from global surveillance.

Design, Setting, and Participants This case series applied an SLS to viral genomic sequence data collected from 63 686 individuals in Africa and 531 827 individuals in the United States with SARS-CoV-2. Data were collected from January 1, 2020, to December 28, 2021.

Main Outcomes and Measures The outcome was an indicator of Omicron variant derived from viral sequences. Centering on a temporally collected outcome, the SLS used the generalized additive model to estimate locally averaged Omicron caseload percentages (OCPs) over time to characterize Omicron expansion and to estimate when OCP exceeded 10%, 25%, 50%, and 75% of the caseload. Additionally, an unsupervised learning technique was applied to visualize Omicron expansions, and temporal and spatial distributions of Omicron cases were investigated.

Results In total, there were 2698 cases of Omicron in Africa and 12 141 in the United States. The SLS found that Omicron was detectable in South Africa as early as December 31, 2020. With 10% OCP as a threshold, it may have been possible to declare Omicron a variant of concern as early as November 4, 2021, in South Africa. In the United States, the application of SLS suggested that the first case was detectable on November 21, 2021.

Conclusions and Relevance The application of SLS demonstrates how the Omicron variant may have emerged and expanded in Africa and the United States. Earlier detection could help the global effort in disease prevention and control. To optimize early detection, efficient data analytics, such as SLS, could assist in the rapid identification of new variants as soon as they emerge, with or without lineages designated, using viral sequence data from global surveillance.

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Article Information

Accepted for Publication: July 21, 2022.

Published: September 7, 2022. doi:10.1001/jamanetworkopen.2022.30293

Correction: This article was corrected on October 6, 2022, to fix errors in Figure 2.

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Zhao LP et al. JAMA Network Open.

Corresponding Author: Lue Ping Zhao, PhD, Public Health Sciences Division (lzhao@fredhutch.org), and Lawrence Corey, MD, Vaccine and Infectious Disease Division (lcorey@fredhutch.org), Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109.

Author Contributions: Dr Zhao had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Zhao, Lybrand, Payne, Corey.

Acquisition, analysis, or interpretation of data: Zhao, Lybrand, Gilbert, Madeleine, Cohen, Geraghty, Jerome.

Drafting of the manuscript: Zhao, Lybrand, Payne.

Critical revision of the manuscript for important intellectual content: Zhao, Lybrand, Gilbert, Madeleine, Cohen, Geraghty, Jerome, Corey.

Statistical analysis: Zhao, Lybrand.

Obtained funding: Gilbert, Geraghty, Jerome, Corey.

Administrative, technical, or material support: Geraghty, Jerome.

Supervision: Payne, Cohen, Geraghty, Jerome, Corey.

Conflict of Interest Disclosures: Dr Gilbert reported grants from the National Institutes of Health National Institute of Allergy and Infectious Diseases for statistical work on COVID-19 vaccine efficacy trials during the conduct of the study. No other disclosures were reported.

Funding/Support: This research was funded by grants UM1 AI68614 and UM1 AI068635 from the National Institutes of Health National Institute of Allergy and Infectious Diseases.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Information: All sequence data analyzed here are publicly available at GISAID (https://www.gisaid.org/).

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