Spatiotemporal analyses illuminate the competitive advantage of a SARS-CoV-2 variant of concern over a variant of interest
Russell, A.; O'Connor, C.; Lasek-Nesselquist, E.; Plitnick, J.; Kelly, J. P.; Lamson, D. M.; St George, K.
Show abstract
The emergence of novel SARS-CoV-2 variants in late 2020 and early 2021 raised alarm worldwide and prompted reassessment of the management, surveillance, and projected future of COVID-19. Mutations that confer competitive advantages by increasing transmissibility or immune evasion have been associated with the localized dominance of single variants. Thus, elucidating the evolutionary and epidemiological dynamics among novel variants is essential for understanding the trajectory of the COVID-19 pandemic. Here we show the interplay between B.1.1.7 (Alpha) and B.1.526 (Iota) in New York (NY) from December 2020 to April 2021 through phylogeographic analyses, space-time scan statistics, and cartographic visualization. Our results indicate that B.1.526 likely evolved in the Bronx in late 2020, providing opportunity for an initial foothold in the heavily interconnected New York City (NYC) region, as evidenced by numerous exportations to surrounding locations. In contrast, B.1.1.7 became dominant in regions of upstate NY where B.1.526 had limited presence, suggesting that B.1.1.7 was able to spread more efficiently in the absence of B.1.526. Clusters discovered from the spatial-time scan analysis supported the role of competition between B.1.526 and B.1.1.7 in NYC in March 2021 and the outsized presence of B.1.1.7 in upstate NY in April 2021. Although B.1.526 likely delayed the rise of B.1.1.7 in NYC, B.1.1.7 became the dominant variant in the Metro region by the end of the study period. These results reveal the advantages endemicity may grant to a variant (founder effect), despite the higher fitness of an introduced lineage. Our research highlights the dynamics of inter-variant competition at a time when B.1.617.2 (Delta) is overtaking B.1.1.7 as the dominant lineage worldwide. We believe our combined spatiotemporal methodologies can disentangle the complexities of shifting SARS-CoV-2 variant landscapes at a time when the evolution of variants with additional fitness advantages is impending.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Genomic Epidemiology of SARS-CoV-2 in Norfolk, UK, March 2020 - December 2022 94%
- Phylogenomics and population genomics of SARS-CoV-2 in Mexico reveals variants of interest (VOI) and a mutation in the Nucleocapsid protein associated with symptomatic versus asymptomatic carriers 94%
- ‘Vivaldi’: An amplicon-based whole genome sequencing method for the four seasonal human coronaviruses 229E, NL63, OC43 & HKU1, alongside SARS-CoV-2’ 93%
Similar papers in this journal
- Large-scale genomic analysis of SARS-CoV-2 Omicron BA.5 emergence in the United States 95%
- Epidemiology of the early COVID-19 epidemic in Orange County, California: comparison of predictors of test positivity, mortality, and seropositivity 92%
- Respiratory shedding of infectious SARS-CoV-2 Omicron XBB.1.41.1 lineage with increased evolutionary rate among captive white-tailed deer 92%
Similar papers in this journal
- White-tailed deer (Odocoileus virginianus) may serve as a wildlife reservoir for nearly extinct SARS-CoV-2 variants of concern 96%
- Multiple spillovers and onward transmission of SARS-Cov-2 in free-living and captive White-tailed deer 95%
- Broad Host Range of SARS-CoV-2 Predicted by Comparative and Structural Analysis of ACE2 in Vertebrates 93%
Similar papers in this journal
- Phylogenetic estimates of SARS-CoV-2 introductions into Washington State 95%
- Lives saved and hospitalizations averted by COVID-19 vaccination in New York City 90%
- A molecular surveillance-guided vector control response to concurrent dengue and West Nile virus outbreaks in a COVID-19 hotspot of Florida 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.