HAI: A Haplotype-Based AI System for Predicting SARS-CoV-2 Variants

The HAI (Haplotype-based Artificial Intelligence) system developed by Fred Hutch Cancer Research Center integrates haplotype analysis and machine learning techniques to automatically predict new SARS-CoV-2 variants, providing early warning capabilities for epidemic surveillance. The project has been under continuous development since 2022, and its source code is hosted on GitHub (link: https://github.com/FredHutch/HAI).

The SARS-CoV-2 virus continues to evolve, generating numerous mutations during replication. Some mutations may increase transmissibility, evade immunity, or enhance virulence. WHO and CDC classify variants carrying concerning mutations into Variants Being Monitored (VBM), Variants of Concern (VOC), or Variants of High Consequence (VOHC). Timely identification is crucial for public health responses.

Complexity of Variant Generation: Includes recombinant (recombination of different variants), cumulative (accumulation of mutations in existing variants), and novel (independent mutation combinations), which traditional methods struggle to capture comprehensively.

HAI System Architecture: Integrates multiple modules: data processing (cleaning and standardizing sequences), temporal modeling (temporal evolution of mutations), unsupervised learning (discovering potential patterns), haplotype analysis (identifying co-inherited mutation combinations), Bayesian probability calculation (quantifying occurrence likelihood), and post-prediction processing (screening and validating results).

HAI primarily uses viral sequences and metadata from GISAID (Global Initiative on Sharing All Influenza Data). Usage must comply with GISAID rules: obtaining access rights, agreeing to terms of use, correctly citing sources, and respecting the contributions of data providers.

Input Options: Supports GISAID ID lists, GISAID metadata files, and can also process custom data in similar formats (the "AA.Substitutions" column must be consistent).

Output Results: Predictions of new variants, including possible mutation combinations, estimated occurrence probabilities, and relationship analysis with known variants.

Early Warning Capability: Can identify signals of new variants before official confirmation, helping to prepare medical resources in advance, adjust vaccine strategies, formulate public health policies, and optimize surveillance networks.

Research Contributions: The achievements have been published (Zhao et al., 2022), providing methodological references for the field of viral evolution prediction.

Current Limitations: Relies on the timeliness and coverage of GISAID data; prediction accuracy is affected by the quality of training data; professional bioinformatics knowledge is required to interpret results.

Future Directions: Integrate more data sources (e.g., wastewater surveillance), introduce deep learning to improve accuracy, develop a user-friendly UI, and expand to other pathogens.

Public Health Implications: Demonstrates the potential of combining AI and bioinformatics to solve epidemic surveillance problems, emphasizing the importance of interdisciplinary collaboration and open data sharing (e.g., GISAID).