Global estimates of the fitness advantage of SARS-CoV-2 variant Omicron

Abstract

New variants of SARS-CoV-2 show remarkable heterogeneity in their relative fitness over both time and space. In this paper we extend the tools available for estimating the selection strength for new SARS-CoV-2 variants to a hierarchical, mixed-effects, renewal equation model. This formulation allows us to estimate selection effects at the global level while incorporating both measured and unmeasured heterogeneity among countries. Applying this model to the spread of Omicron in forty countries, we find evidence for very strong but very heterogeneous selection effects. To test whether this heterogeneity is explained by differences in the immune landscape, we considered several measures of vaccination rates and recent population-level infection as covariates, finding marginally significant effects. We also found a significant positive correlation between the selection advantage of Delta and Omicron at the country level, suggesting that other region-specific explanatory variables of fitness differences do exist. Our method is implemented in the Stan programming language, can be run on standard consumer-grade computing resources, and will be straightforward to apply to future variants.

Comparative epidemic expansion of SARS-CoV-2 variants Delta and Omicron in the Brazilian State of Amazonas

“…The most recent COVID-19 wave in Amazonas, however, displayed some unique features. The Omicron BA.1 wave displayed a higher peak of SARS-CoV-2 cases and shorter duration than previous waves, as observed in other Western countries 18,48,49 . We speculate that the weak mitigation measures implemented in Amazonas during the dissemination of Omicron BA.1 may have resulted in a high and sharp peak with a rapid decline, while the curve of previous variants may have been "flattened" and prolonged by more stringent control measures implemented earlier (Fig.…”

“…Delta in Amazonas (~3) was roughly comparable to that estimated in other settings with different infection and vaccination histories like South Africa, Denmark, the UK, the United States of America, and India (~2-5) 27,[29][30][31] . The Omicron BA.1 outbreak in Amazonas also had a similar duration from the onset of case growth to their peaks (~30 days) than that observed in other populations with different immune landscapes 18,48,49 . These data support that immunologicalrelated covariates alone could not explain heterogeneity in Omicron's fitness across countries 5,49 .…”

“…The Omicron BA.1 outbreak in Amazonas also had a similar duration from the onset of case growth to their peaks (~30 days) than that observed in other populations with different immune landscapes 18,48,49 . These data support that immunologicalrelated covariates alone could not explain heterogeneity in Omicron's fitness across countries 5,49 .…”

Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the United States

“…We found that Omicron variants were responsible for approximately 404 million (95% CrI = 221-617 million) infections across the US from December 2021 to March 2023, including approximately 169 million during the BA.1* wave. The transmission dynamics of variants differed markedly: BA.1* emerged as a genetically distinct (3,(23)(24)(25) variant which caused large rapid epidemics, especially in states with a higher degree of social vulnerability. Subsequent Omicron variants, while able to both co-circulate and eventually outcompete extant strains, spread at lower levels and often for longer durations, exhibiting much weaker association with social vulnerability measures than the BA.1* variant.…”

On the Evolutionary Trajectory of SARS-CoV-2: Host Immunity as a Driver of Adaptation in RNA Viruses

“…The SARS-CoV-2 genome shows signs of continued adaptation to human immunity after transmission from the non-human reservoir, much like other zoonotic RNA viruses Middle Eastern Respiratory Syndome Coronavirus (MERS-CoV) and influenza [ 29 , 31 ]. Furthermore, the relative fitness of different SARS-CoV-2 variants is heterogenous across different geographic regions, likely reflecting a changing immunological landscape influenced by genetics, as well as infection and vaccination rates [ 30 , 32 ].…”