Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the US
Rafael Lopes,
Kien Pham,
Fayette Klaassen,
Melanie H. Chitwood,
Anne M. Hahn,
Seth Redmond,
Nicole A. Swartwood,
Joshua A. Salomon,
Nicolas A. Menzies,
Ted Cohen,
Nathan D. Grubaugh
Affiliations
Rafael Lopes
Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA; Corresponding author
Kien Pham
Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
Fayette Klaassen
Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Melanie H. Chitwood
Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
Anne M. Hahn
Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
Seth Redmond
Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
Nicole A. Swartwood
Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Joshua A. Salomon
Department of Health Policy, Stanford University School of Medicine, Stanford, CA, USA
Nicolas A. Menzies
Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Ted Cohen
Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA; Corresponding author
Nathan D. Grubaugh
Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA; Corresponding author
Summary: Omicron surged as a variant of concern in late 2021. Several distinct Omicron variants appeared and overtook each other. We combined variant frequencies and infection estimates from a nowcasting model for each US state to estimate variant-specific infections, attack rates, and effective reproduction numbers (Rt). BA.1 rapidly emerged, and we estimate that it infected 47.7% of the US population before it was replaced by BA.2. We estimate that BA.5 infected 35.7% of the US population, persisting in circulation for nearly 6 months. Other variants—BA.2, BA.4, and XBB—together infected 30.7% of the US population. We found a positive correlation between the state-level BA.1 attack rate and social vulnerability and a negative correlation between the BA.1 and BA.2 attack rates. Our findings illustrate the complex interplay between viral evolution, population susceptibility, and social factors during the Omicron emergence in the US.
