Chen Jiahui, Wang Rui, Hozumi Yuta, Liu Gengzhuo, Qiu Yuchi, Wei Xiaoqi, Wei Guo-Wei
Department of Mathematics, Michigan State University, MI 48824, USA.
Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA.
ArXiv. 2022 Oct 18:arXiv:2210.09485v1.
Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron (BA.1), BA.2, and BA.4/BA.5 were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. Based on newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BA.2.10.4, BA.2.75, BQ.1.1, and particularly, BA.2.75+R346T, have high potential to become new dominant variants to drive the next surge.
准确可靠地预测新兴的主要严重急性呼吸综合征冠状病毒2(SARS-CoV-2)变种,能够使政策制定者和疫苗制造商为未来的感染浪潮做好准备。由主要变种奥密克戎(BA.1)、BA.2以及BA.4/BA.5引发的最近三轮SARS-CoV-2感染,都被我们基于生物物理学、病毒基因组基因分型、实验数据、代数拓扑学和深度学习构建的人工智能(AI)模型准确预测到了。基于最新可得的实验数据,我们分析了所有可能的病毒刺突(S)蛋白受体结合域(RBD)突变对SARS-CoV-2传染性的影响。我们的分析揭示了病毒的进化机制,即通过增强传染性和抗体抗性进行自然选择。我们预测,BA.2.10.4、BA.2.75、BQ.1.1,特别是BA.2.75+R346T,极有可能成为推动下一波疫情高峰的新的主要变种。