  Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to
become a dominating variant in late 2021 and was replaced by more transmissible
Omicron BA.2 in March 2022. An important question is which new variants will
dominate in the future. Topology-based deep learning models have had tremendous
success in forecasting emerging variants in the past. However, topology is
insensitive to homotopic shape variations in virus-human protein-protein
binding, which are crucial to viral evolution and transmission. This challenge
is tackled with persistent Laplacian, which is able to capture both the
topology and shape of data. Persistent Laplacian-based deep learning models are
developed to systematically evaluate variant infectivity. Our comparative
analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1,
BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11,
BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular,
BA.4 and BA.5 are about 36\% more infectious than BA.2 and are projected to
become new dominating variants by natural selection. Moreover, the proposed
models outperform the state-of-the-art methods on three major benchmark
datasets for mutation-induced protein-protein binding free energy changes.
