Forecasting framework for dominant SARS-CoV-2 strains before clade replacement using phylogeny-informed genetic...

Area of Science:

• Virology
• Genomics
• Epidemiology

Background:

• The emergence of novel Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants drives successive waves of the coronavirus disease 2019 (COVID-19) pandemic.
• Accurate prediction of future variants is crucial for effective public health interventions and timely vaccine updates.

Purpose of the Study:

• To evaluate the evolutionary dynamics of SARS-CoV-2.
• To develop a forecasting framework for predicting emerging variants and potential clade replacement.
• To establish quantifiable molecular criteria for SARS-CoV-2 vaccine updates.

Main Methods:

• Phylogeny-informed genetic distances were calculated using 394 complete SARS-CoV-2 genomes and spike (S) gene sequences.
• A forecasting framework analyzed non-synonymous and synonymous genetic distances from clade roots to predict clade dominance or extinction.
• The framework's predictability was assessed using training and test datasets.

Main Results:

• Quantifiable heterogeneity in genetic distances between dominant and extinct variants was observed.
• The forecasting framework demonstrated high predictability (AUROC > 0.90) in identifying the next clade root before replacement.
• Using only spike gene data yielded accuracy comparable to using complete genomes.

Conclusions:

• The developed framework provides a reliable method for predicting SARS-CoV-2 clade replacement.
• The study establishes quantifiable molecular criteria for proactive SARS-CoV-2 vaccine updates.
• This approach contributes to enhanced global pandemic preparedness.