Integrated Analysis of the 2022 SARS-CoV-2 Omicron Lineage Replacement Dynamics in Connecticut, US

Abstract

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.

Keywords: Genomic surveillance; variant fitness; viral evolution.

Figure 1

Genomic Epidemiology of SARS-CoV-2 describes the COVID-19 epidemic in Connecticut, US. (A) Frequencies of SARS-CoV-2 variants of concern from January 2021 to January 2023, based on sequences deposited on GISAID. (B) Reported COVID-19 infections from January 2021 to 2023 with highlights of the dominant periods of different Omicron lineages, data from the Connecticut Department of Public Health (C) Number of estimated infections based on the covidestim model for 2022 where testing and reporting widely changed after the first wave of Omicron BA.1 (D) Cumulative estimated cases for each Omicron variant up until January 2023.

Figure 2

Comparison of key epidemiological parameters for each Omicron lineage in 2022. (A) Frequencies of the incoming Omicron lineage during its emergence period highlight the time interval between being detected at 5% and 50% of all sequences (based on submissions to GISAID for Connecticut). (B) Comparison of growth rates between the different Omicron lineages and the average slope of the growth curve during emergence periods (C) Variant-specific R_t numbers for each of the Omicron lineage over time derived from the modeled overall R_t from the covidestim model (D) Based on (C), the R_t ratios for each of the emergence periods were calculated to estimate the average advantage of each incoming lineage compared to the previously dominating one.

Figure 3

Comparing average qPCR Ct values as a proxy for variant intrinsic transmissibility. (A) Summary of all samples processed in our genomic surveillance program from 2021 to January 2023 (B) Overview about the numbers and distribution of Omicron samples (C) Ct values from Omicron samples plotted together with modeled average over time (D) Based on (C), summary values for each of the Omicron lineages with emergence periods highlighted (E) Statistical analysis of Ct values retrieved from samples collected during the emergence periods. * p < 0.05, ** p < 0.01.

Figure 4

Influence of vaccine uptake and community-immunity levels on Omicron lineage emergence. (A) Vaccination coverage of Connecticut as reported by the US CDC for 2021 and 2022 (B) Estimated antigen exposure based on estimates from covidestim model according to either total (vaccination and/or infection) (purple) and hybrid exposure (infection and/or vaccination) (orange) (C) Logistic regression model to compare the ratios of infections caused by the incoming or previously dominant variant (Delta vs. pre-Delta variants (orange), BA.1 vs. Delta (green), BA.2 vs. BA.1 (pink), BA.4/5 vs. BA.2 (blue), XBB.1 vs. BA5 (dark red)) based on vaccination status during the emergence windows. BA.4 and BA.5 were analyzed together due to the similarity in the Spike region.

Figure 5

Summary of selective fitness advantages of Omicron lineage emergence in a dynamic host immunity landscape. (A) Sliding-window analysis of cumulative estimated infections 60, 90 or 120 days prior to each date on the plot with highlights when emerging Omicron lineages reached 5% of total samples (B) Frequency of estimated proportion (in%) of the population to be protected against infection over time inferred by vaccine and infection data according to the covidestim model (C) Conceptual visualization of selective advantages of incoming Omicron lineages over the year (D) SIR-based conceptual transmission model highlighting different fitness advantages of incoming Omicron lineages depending on the status of the host population. Shades of blue depict different levels of susceptibility within the first antigenic space (BA.1-BA.2) depending on infection and vaccination status where darker colors depict higher protection. Shades of green depict different levels of susceptibility within the second antigenic space (BA.5-XBB) depending on infection and vaccination status, darker colors depict higher levels of protection. Arrows in yellow, orange and red represent the different combinations of advantageous fitness traits as outlined in the table in the right corner. Shades of yellow, orange and red represent the strength of the transmission advantage effect applying to each transition.