Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future...

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Measles is a highly contagious disease posing global public health risks despite vaccination.
• Computational approaches are crucial for monitoring, predicting, and controlling measles outbreaks.
• Artificial intelligence (AI) is emerging as a significant tool in infectious disease modeling.

Purpose of the Study:

• To review classical and contemporary computational methods for measles monitoring, prediction, and control.
• To evaluate the role and impact of artificial intelligence (AI) and machine learning (ML) in measles research.
• To identify challenges and future directions for computational approaches in measles elimination.

Main Methods:

• Narrative review of 46 studies (31 on measles, 15 on related diseases) from major scientific databases and preprint servers.
• Analysis of traditional models (compartmental, statistical, seroepidemiological) and advanced AI/ML techniques (supervised, deep learning, hybrid).
• Synthesis of findings on data integration, predictive performance, and application in surveillance and policy.

Main Results:

• Classical models (SIR, SEIR) offer transparent frameworks for transmission dynamics and intervention simulation.
• AI/ML methods demonstrate enhanced predictive performance by integrating diverse data sources (epidemiological, demographic, mobility).
• AI excels in high-dimensional risk prediction and diagnostics, but widespread adoption for policy is limited by data quality, generalizability, and infrastructure issues.

Conclusions:

• Classical and AI/ML models offer complementary strengths for measles control and elimination strategies.
• Significant challenges remain in data quality, model generalizability, and equitable deployment of computational tools.
• Future directions include explainable AI, federated learning, workforce training, and integrated data systems for enhanced measles surveillance and policy.