Infectious Disease Modelling in R

Understanding how infectious diseases spread across populations is essential for evaluating outbreak scenarios and public health strategies. This text-based course guides you through mathematical epidemiology and statistical programming, translating biological concepts into clear, reproducible R scripts. What you'll learn: - Understand foundational epidemiological concepts, including transmission rates, herd immunity thresholds, and the reproduction number - Build classic compartmental frameworks such as SIR and SEIR models from first principles - Implement differential equation solvers in R to simulate infection trajectories over time - Apply modern data manipulation and visualization workflows to interpret epidemic curves - Calibrate model parameters by fitting simulations to observed incidence data - Assess the quantitative impact of vaccination, quarantine, and non-pharmaceutical interventions You will begin with essential terminology and the mathematical logic behind disease transmission before progressing to code-based simulations and practical parameter estimation. This course is designed for beginners interested in quantitative public health and data science, requiring no prior background in epidemic modeling. Start reading today to build your understanding of disease transmission mechanics.