Predictive and Analytical Methods for Determining Viral Variation: Evolution and Frontier Breakthroughs

Abstract

The continuous mutation of viruses poses a persistent and severe challenge to global public health security, disease prevention and control, and biomedical research and development. The accurate prediction and in-depth analysis of viral mutation trends and their potential impacts are of critical strategic importance for constructing effective epidemic early warning systems and guiding the targeted development of vaccines and drugs. In recent years, the rapid development of artificial intelligence and high-throughput sequencing technologies has significantly enhanced our ability to monitor and infer viral evolutionary paths. In particular, the application of machine learning models in predicting mutation hotspots and functional impacts has opened new avenues for research. Moreover, interdisciplinary approaches combining genomics, structural biology, and computational modeling provide deeper insights into the mechanisms driving viral evolution. This paper aims to systematically review the evolution of methods for viral mutation prediction and analysis, delve into frontier technological breakthroughs in the field, objectively analyze the advantages and limitations of existing approaches, and provide an outlook on future directions. Furthermore, the integration of real-time surveillance data with predictive models is emphasized as a key factor in improving the timeliness and accuracy of mutation alerts. The goal is to offer a comprehensive and valuable reference for scientific research and practical applications in related fields, thereby assisting human society in better responding to the complex problems arising from viral variation.