Infectious Disease Modelling with R: Applied Methods

Understanding how infectious diseases spread is critical for public health decision-making and epidemiological research. This text-based course guides you through the foundational principles of mathematical epidemiology and shows you how to translate biological concepts into practical computational models using R. You will begin with essential terminology and core mathematical concepts before writing code to simulate outbreaks and assess the impact of interventions. What you'll learn: - Understand foundational epidemiological terminology, transmission dynamics, and core compartmental frameworks such as SIR and SEIR models. - Implement ordinary differential equations in R to simulate disease progression and outbreak trajectories. - Calculate and interpret key metrics, including the basic reproduction number (R0) and effective reproduction number over time. - Model public health control strategies, such as vaccination programs, quarantine measures, and contact reductions. - Apply modern R packages and tidy data practices to analyze and visualize simulation outputs cleanly. - Practice basic parameter calibration and model fitting techniques against real-world outbreak data. The course starts with essential definitions and theoretical foundations before advancing through guided code examples, practical scenarios, and structured exercises. Designed specifically for beginners, public health students, and data analysts, no prior background in mathematical modeling or advanced R programming is required. Start reading to master the fundamentals of infectious disease modeling in R.