Speaker
Description
The COVID-19 pandemic saw successive emergence and spread of novel viral variants, exhibiting enhanced transmissibility or evasion from vaccine- and infection-acquired immunity. While the genotypic and phenotypic basis of SARS-CoV-2 variants have been extensively characterized, the evolutionary factors governing their patterns of emergence are less well understood. In this study we investigated how variant phenotype (increased transmissibility or immune evasion), source (local evolution vs importation), the timing of introduction, the distribution of population susceptibility, and the contact network structure interact to determine the probability of variant invasion and the speed of spread.
We developed a simplified stochastic SIR-type model of a resident and variant strain and characterized variant invasion dynamics by the probability an introduced strain avoids stochastic extinction, the time to reach detection and dominance, and the speed of invasion. We find that strains with a transmission advantage are more likely to emerge earlier in an epidemic, and will rapidly and predictably dominate the viral population. Whereas, we find that immune-escape variants can invade at any point in the epidemic, but tend to linger at a low prevalence for extended time periods until a critical amount of immunity has built up. This implies that immune escape variants detected later in an epidemic may have arisen much earlier and avoided detection. We examined variant invasion in more realistic human population structures that included heterogeneity in contact patterns and clustering of prior immunity. We observed that both types of variants were more likely to go extinct upon introduction, but when they successfully established, they swept through the population more quickly.
This work enhances our understanding of when different variant types are likely to be detected in an epidemic. In addition to providing insight into the past dynamics of SARS-CoV-2 variants, it can help define planning scenarios for future epidemic modeling efforts.
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