Integrated Analysis of the 2022 SARS-CoV-2 Omicron Lineage Replacement Dynamics in Connecticut, US
In 2022, consecutive sweeps of highly transmissible SARS-CoV-2 Omicron-derived lineages (B.1.1.529*) maintained viral transmission despite extensive antigen exposure from both vaccinations and infections. To better understand Omicron variant emergence in the context of the dynamic fitness landscape...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1999-4915/17/7/1020 |
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| author | Nicholas F. G. Chen Kien Pham Chrispin Chaguza Rafael Lopes Fayette Klaassen Chaney C. Kalinich Yale SARS-CoV-2 Genomic Surveillance Initiative Nicholas Kerantzas Sameer Pandya David Ferguson Wade Schulz Daniel M. Weinberger Virginia E. Pitzer Joshua L. Warren Nathan D. Grubaugh Anne M. Hahn |
| author_facet | Nicholas F. G. Chen Kien Pham Chrispin Chaguza Rafael Lopes Fayette Klaassen Chaney C. Kalinich Yale SARS-CoV-2 Genomic Surveillance Initiative Nicholas Kerantzas Sameer Pandya David Ferguson Wade Schulz Daniel M. Weinberger Virginia E. Pitzer Joshua L. Warren Nathan D. Grubaugh Anne M. Hahn |
| author_sort | Nicholas F. G. Chen |
| collection | DOAJ |
| description | In 2022, consecutive sweeps of highly transmissible SARS-CoV-2 Omicron-derived lineages (B.1.1.529*) maintained viral transmission despite extensive antigen exposure from both vaccinations and infections. To better understand Omicron variant emergence in the context of the dynamic fitness landscape of 2022, we aimed to explore putative drivers behind SARS-CoV-2 lineage replacements. Variant fitness is determined through its ability to either outrun previously dominant lineages or more efficiently circumvent host immune responses to previous infections and vaccinations. By analyzing data collected through our local genomic surveillance program from Connecticut, USA, we compared emerging Omicron lineages’ growth rates, estimated infections, effective reproductive rates, average viral copy numbers, and likelihood for causing infections in recently vaccinated individuals. We find that newly emerging Omicron lineages outcompeted dominant lineages through a combination of enhanced viral shedding or advanced immune escape depending on the population-level exposure state. This analysis integrates individual-level sequencing data with demographic, vaccination, laboratory, and epidemiological data and provides further insights into host–pathogen dynamics beyond public aggregate data. |
| format | Article |
| id | doaj-art-af5e82790f2147f69f243e759fc1d18e |
| institution | Kabale University |
| issn | 1999-4915 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj-art-af5e82790f2147f69f243e759fc1d18e2025-08-20T03:56:46ZengMDPI AGViruses1999-49152025-07-01177102010.3390/v17071020Integrated Analysis of the 2022 SARS-CoV-2 Omicron Lineage Replacement Dynamics in Connecticut, USNicholas F. G. Chen0Kien Pham1Chrispin Chaguza2Rafael Lopes3Fayette Klaassen4Chaney C. Kalinich5Yale SARS-CoV-2 Genomic Surveillance InitiativeNicholas Kerantzas6Sameer Pandya7David Ferguson8Wade Schulz9Daniel M. Weinberger10Virginia E. Pitzer11Joshua L. Warren12Nathan D. Grubaugh13Anne M. Hahn14Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USADepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USADepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USADepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USADepartment of Global Health & Population, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USADepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USADepartment of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06510, USADepartment of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06510, USADepartment of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06510, USADepartment of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06510, USADepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USADepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USAPublic Health Modeling Unit, Yale School of Public Health, New Haven, CT 06510, USADepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USADepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USAIn 2022, consecutive sweeps of highly transmissible SARS-CoV-2 Omicron-derived lineages (B.1.1.529*) maintained viral transmission despite extensive antigen exposure from both vaccinations and infections. To better understand Omicron variant emergence in the context of the dynamic fitness landscape of 2022, we aimed to explore putative drivers behind SARS-CoV-2 lineage replacements. Variant fitness is determined through its ability to either outrun previously dominant lineages or more efficiently circumvent host immune responses to previous infections and vaccinations. By analyzing data collected through our local genomic surveillance program from Connecticut, USA, we compared emerging Omicron lineages’ growth rates, estimated infections, effective reproductive rates, average viral copy numbers, and likelihood for causing infections in recently vaccinated individuals. We find that newly emerging Omicron lineages outcompeted dominant lineages through a combination of enhanced viral shedding or advanced immune escape depending on the population-level exposure state. This analysis integrates individual-level sequencing data with demographic, vaccination, laboratory, and epidemiological data and provides further insights into host–pathogen dynamics beyond public aggregate data.https://www.mdpi.com/1999-4915/17/7/1020variant fitnessGenomic surveillanceviral evolution |
| spellingShingle | Nicholas F. G. Chen Kien Pham Chrispin Chaguza Rafael Lopes Fayette Klaassen Chaney C. Kalinich Yale SARS-CoV-2 Genomic Surveillance Initiative Nicholas Kerantzas Sameer Pandya David Ferguson Wade Schulz Daniel M. Weinberger Virginia E. Pitzer Joshua L. Warren Nathan D. Grubaugh Anne M. Hahn Integrated Analysis of the 2022 SARS-CoV-2 Omicron Lineage Replacement Dynamics in Connecticut, US Viruses variant fitness Genomic surveillance viral evolution |
| title | Integrated Analysis of the 2022 SARS-CoV-2 Omicron Lineage Replacement Dynamics in Connecticut, US |
| title_full | Integrated Analysis of the 2022 SARS-CoV-2 Omicron Lineage Replacement Dynamics in Connecticut, US |
| title_fullStr | Integrated Analysis of the 2022 SARS-CoV-2 Omicron Lineage Replacement Dynamics in Connecticut, US |
| title_full_unstemmed | Integrated Analysis of the 2022 SARS-CoV-2 Omicron Lineage Replacement Dynamics in Connecticut, US |
| title_short | Integrated Analysis of the 2022 SARS-CoV-2 Omicron Lineage Replacement Dynamics in Connecticut, US |
| title_sort | integrated analysis of the 2022 sars cov 2 omicron lineage replacement dynamics in connecticut us |
| topic | variant fitness Genomic surveillance viral evolution |
| url | https://www.mdpi.com/1999-4915/17/7/1020 |
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