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Early detection of SARS-CoV-2 variants through dynamic co-mutation network surveillance
Qiang Huang1, Huining Qiu2, Paul W Bible3
1Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Frontiers in Public Health
|February 9, 2023
Summary
A new co-mutation network framework enhances SARS-CoV-2 variant tracking by analyzing weekly genomic data. This method provides timely and accurate surveillance of COVID-19 variants, improving public health responses.
Area of Science:
- Genomics
- Epidemiology
- Bioinformatics
Background:
- Current SARS-CoV-2 variant tracking faces challenges due to the overwhelming volume of viral genome sequences, impacting timeliness and accuracy.
- Existing methods struggle to keep pace with the rapid evolution and spread of COVID-19 variants.
Purpose of the Study:
- To develop an efficient genomic surveillance framework for tracking SARS-CoV-2 variants.
- To address the limitations of current variant tracking methods in handling large-scale genomic data.
Main Methods:
- A novel co-mutation network framework was developed to dynamically analyze nucleotide covarying patterns in weekly sequence data.
- Community detection algorithms were applied to co-occurring genomic alteration networks to identify variant markers.
- A dynamic, community-based variant dictionary tree was created to represent SARS-CoV-2 evolution and track variants weekly.
Main Results:
- 235 co-mutation communities were identified over 120 weeks, accurately reflecting SARS-CoV-2 evolution and GISAID clades.
- The framework demonstrated high efficiency in detecting pandemic variants and showed earlier identification of major variants compared to Pangolin.
- Time-varying prevalence of identified communities correlated well with the emergence and circulation of specific variants.
Conclusions:
- The developed genomic surveillance framework effectively overcomes data flood challenges for early and continuous SARS-CoV-2 variant investigation.
- This approach aids in public health and clinical interventions for the COVID-19 pandemic through improved variant tracking.
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