Forecasting framework of dominant SARS-CoV-2 strains before clade replacement using phylogeny-informed genetic distances

Kyuyoung Lee¹Atanas V. Demirev¹Sangyi Lee¹Seunghye Cho¹Hyunbeen Kim¹Junhyung Cho²Jeong-Sun Yang²Kyung-Chang Kim²Joo-Yeon Lee²Woojin Shin¹Soyoung Lee¹Sejik Park¹Philippe Lemey³Man-Seong Park¹Jin Il Kim^1*

• ¹Korea University College of Medicin, Seoul, Republic of Korea
• ²Korea National Institute of Health, Osong, Republic of Korea
• ³KU Leuven, Leuven, Belgium

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the global COVID-19 pandemic and continues to drive successive waves of infection through the emergence of novel variants. Consequently, accurate prediction of next clade roots by global surveillance is crucial for effective prevention, control, and timely vaccine antigen updates. This study evaluated the evolutionary dynamics of SARS-CoV-2 using phylogeny-informed genetic distances based on 394 complete genome and spike gene sequences. Furthermore, we presented a forecasting framework to estimate the potential of emerging variants leading to clade replacement by analyzing nonsynonymous and synonymous genetic distances from clade roots, which reflect global herd immune pressure. Nonsynonymous and synonymous genetic distances from both Wuhan and clade root strains were assessed to predict whether a clade would become dominant or extinct within three months before clade replacement. Through five observed clade replacements up to January 2024, we captured the quantifiable heterogeneity in nonsynonymous and synonymous genetic distances of Spike gene from clade roots between dominant and extinct variants by the extent of novelty, whether through gradual or drastic change. Our framework demonstrated high predictability for identifying the next clade root before replacement in both training and test datasets (AUROC > 0.90) by incorporating differential weighting of nonsynonymous and synonymous genetic distances. Additionally, the framework solely using spike gene data showed comparable accuracy to those using the complete genome. Overall, our approach establishes quantifiable molecular criteria for identifying potential SARS-CoV-2 vaccine updates, contributing to proactive pandemic preparedness.