Study suggests the behavior of an emergent SARS-CoV-2 variant may be sensitive to the immunologic and demographic context of its location

In a recent study posted to bioRxiv*, researchers characterized the heterogeneity in the speed, magnitude, and timing of 13 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant transitions/waves.

Background

SARS-CoV-2 continues to evolve, and global transitions to newly emerging variants can cause new transmission waves. The selective advantage of new variants over existing variants can stem from increased infectivity (through enhanced binding to the host cell receptor) and resistance to neutralizing antibodies.

Previous infections with different SARS-CoV-2 variants can confer varying levels of protection against new variants. The authors speculate that the history of infecting variants and vaccination rates might impact the rate at which a new variant can outcompete extant variants and become the dominant variant.

About the study

In the present study, researchers tested the specified hypothesis and characterized the differences in the global timing, magnitude, and speed of variant transitions. They performed a retrospective analysis of genome sequences of SARS-CoV-2 submitted to the Global Initiative on Sharing All Influenza Data (GISAID) database between October 2020 and October 2022.

Data for more than 12.8 million SARS-CoV-2 genome sequences were obtained from the GISAID repository through the coronavirus disease 2019 (COVID-19) viral genome analysis pipeline. The sequences were stratified by variant according to the Pango nomenclature. Data on daily confirmed COVID-19 cases and deaths were obtained from the Johns Hopkins Center for Systems Science and Engineering.

Age, population density, and information for each location (country) were accessed from WorldPop. The researchers proposed a model for variant proportions over time for each location, accounting for multiple competing variants since the variant landscapes remain dynamic in a given population. For the primary analysis, up to 13 Pango lineage categories were considered for each of the 215 geographic locations (countries and sub-country regions).

The primary analysis did not consider emergent Omicron Pango lineage groupings due to insufficient data. In a sub-analysis of special emergent variants, the researchers characterized existing data for SARS-CoV-2 Omicron BA.2.75, BQ.1, and XBB/XBB.1 variants. A hierarchical clustering analysis was performed to characterize the location similarities across Omicron waves for 155 locations only.

The researchers obtained location attributes such as demographics, COVID-19 clinical landscapes, and associated public policy when the proportion of sequences of a given variant first reached 5% in any location. They identified two proxies for variant competition when a new variant emerged in each location: 1) the number of variants co-circulating with a minimum prevalence of 5% and 2) the competition ratio, the maximum percent increase in the prevalence of existing variants.

Findings

SARS-CoV-2 Beta, Epsilon, Iota, Gamma, and Mu variants were associated with lower prevalence and transition speeds, except for the Beta transition in Southern Africa and the Gamma transition in South America. The Delta and Omicron (BA.1, BA.1.1, BA.2, and BA.5) variants had fast transitions, albeit the variability in the prevalence and transition speeds of Omicron BA.1 and BA.1.1 worldwide.

The Alpha variant had a small and slow transition in South America and South Africa due to variant competition. The Omicron BA.1.1 variant attained a strong presence in the Americas. Contrastingly, there was little heterogeneity in the prevalence and transition speeds for SARS-CoV-2 Delta, which exhibited a complete and rapid transition in most locations.

The Omicron BA.4 and BA.5 variants had different trajectories in terms of maximum transition slopes, relative time to transition, and maximum prevalence, suggesting a selective advantage of the BA.5 variant over BA.4. The transition slopes of the new Omicron sub-variants (BA.2.75, BQ.1, and XBB/XBB.1) were at par with earlier Omicron sub-variants.

Hierarchical clustering analysis yielded seven clusters and indicated that SARS-CoV-2 Omicron variant transitions were likely to be more similar between some geographic location pairs than others, suggesting a link between transition dynamics and geographic location attributes. The researchers investigated the association between the number of co-circulating variants (when each variant had a 5% prevalence) and the maximum transition slope.

There was a significant association between a higher number of co-circulating variants and lower transition speeds for many SARS-CoV-2 variants, including the Epsilon, Gamma, Delta, and Omicron (BA.1, BA.2, and BA.5) variants. Higher vaccination rates were associated with slower and later global spread of variants before the Delta and Mu variants emerged. However, there was a weak association between vaccination rates and speed/timing of transitions for the Omicron variant.

Vaccination rates were significantly associated with variant transition dynamics before Delta/Mu emergence, even after adjusting for location attributes and multiple testing. A shorter time since the last wave peak, a higher prior COVID-19 case rate, and lower population density were associated with later transitions. Regions with more people aged 65 or older were likely to have a higher peak variant prevalence.

Conclusions

In summary, the researchers illustrated associations of an emergent SARS-CoV-2 variant’s behavior with the number of co-circulating variants, prior COVID-19 case rate, and vaccination rates. There was a strong association between higher vaccination rates and variant transition dynamics before the Delta/Mu variant transitions.

Overall, the findings indicate substantial heterogeneity in how a variant competes with co-circulating variants across geographic locations, suggesting that a location’s contemporary immunologic landscape may contribute to these interactions. These data on heterogeneity and historical variant transitions may be leveraged in future works to predict the behavior of emergent variants.

*Important notice

bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information

Written by

Tarun Sai Lomte

Tarun is a writer based in Hyderabad, India. He has a Master’s degree in Biotechnology from the University of Hyderabad and is enthusiastic about scientific research. He enjoys reading research papers and literature reviews and is passionate about writing.