Epidemiology and transmission of infectious diseases study using Machine learning - Dr. Kazi Kutubuddin (International Journal of Pathogens, 2025)
Infectious diseases remain a formidable global health challenge, characterized by rapid evolution and complex transmission dynamics that often outpace traditional epidemiological surveillance and response mechanisms. This study investigates the transformative potential of machine learning (ML) methodologies to enhance our understanding and prediction of infectious disease epidemiology and transmission. Leveraging diverse datasets—including clinical records, genomic sequences, environmental factors, social mobility data, and real-time digital footprints—we studies and presented various ML models (e.g., supervised classification, deep learning, and time-series forecasting) tailored for critical epidemiological tasks. Our findings demonstrate significant advancements in several key areas: early outbreak detection through anomaly identification, precise identification of high-risk populations and geographical hotspots, accurate forecasting of disease incidence and transmission trajectories, and optimization of intervention strategies (e.g., vaccination campaigns, resource allocation). The superior predictive accuracy, scalability, and real-time analytical capabilities of these ML models underscore their critical role in building more resilient public health surveillance systems and facilitating a more rapid, precise, and proactive response to emerging pathogenic threats in an increasingly interconnected world.
Keywords: Infection Diseases; Machine Learning; epidemic; epidemiology; supervised classification