The Role of Artificial Intelligence and Machine Learning in Predictive Virology: Forecasting, Tracking, and Combating...

Area of Science:

• Virology and Epidemiology
• Computational Biology
• Public Health Surveillance

Background:

• The COVID-19 pandemic highlighted limitations of reactive virology against evolving pathogens.
• There is a critical need for advanced methods to anticipate and mitigate viral threats.

Purpose of the Study:

• Introduce predictive virology (PV) as an AI-driven discipline for forecasting viral threats.
• Examine AI applications in anticipating viral emergence, evolution, and designing countermeasures.
• Provide a framework for transitioning virology to a predictive science.

Main Methods:

• Systematic review of AI-driven approaches in virology, including machine learning and deep learning.
• Analysis of AI applications in genomic data integration, multimodal surveillance, and transmission modeling.
• Evaluation of AI tools for predicting viral mutations, antigenic escape, and designing antivirals/vaccines.

Main Results:

• AI-powered tools demonstrate performance comparable to or exceeding traditional methods in retrospective benchmarks.
• Core PV pillars include AI for mutation prediction, early-warning systems, transmission modeling, and drug/vaccine design.
• Significant challenges remain, including data bias, overfitting, and limited prospective validation.

Conclusions:

• Predictive virology offers a paradigm shift towards proactive pandemic preparedness.
• Addressing challenges in data, validation, interpretability, and equity is crucial for responsible deployment.
• Transitioning to PV strengthens global health security by anticipating and managing viral threats.