Track 4: AI

Overview

Applications of machine learning in infectious disease modelling.

Prerequisites

• Basic understanding of epidemiology and infectious disease concepts
• Undergraduate-level statistics (descriptive statistics, regression analysis)
• Basic programming experience (preferably one of Python, R or Julia)
• Some sort of exposure to computational statistics or computational mathematical modelling or machine learning

Course Content

This course explores the intersection of artificial intelligence and infectious disease modeling, equipping participants with practical skills to leverage AI in epidemiology, tropical medicine, and public health contexts. Participants will learn to work with diverse epidemiological data types (text, images, audio) and master data engineering fundamentals essential for building robust AI systems. Through hands-on sessions using Orange and supplementary Python scripts, students will develop predictive models for classification and regression tasks, as well as diagnostic models for disease detection and risk assessment. The curriculum progresses from simple machine learning algorithms to more complex deep learning architectures, with a focus on practical implementation and responsible deployment. Students will gain proficiency in evaluating model performance using key metrics specific to infectious disease applications. Additional modules will cover citizen science approaches, and the ethical considerations essential to AI deployment in public health contexts. By the course’s conclusion, participants will possess the technical skills and ethical framework necessary to apply AI solutions to pressing infectious disease challenges

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