Foundations of Infectious Disease Modelling

Understanding how infectious diseases spread is crucial for designing effective public health policies and interventions. This text-based course introduces you to the core mathematical and computational frameworks used to simulate and predict epidemic trajectories. You will transition from understanding basic biological transmission to constructing and analyzing your own mathematical models. By studying foundational concepts and exploring written code examples, you will gain the skills needed to interpret public health data and evaluate the impact of control measures like vaccination and social distancing. What you'll learn: - Understand the core concepts of epidemiological modeling, including the basic reproduction number (R0) and herd immunity thresholds. - Build deterministic compartment models, such as SIR and SEIR, using differential equations. - Analyze how interventions like vaccination, quarantine, and treatment alter disease dynamics. - Explore modern computational approaches, including basic stochastic simulations and parameter estimation from real-world data. - Interpret model outputs to make informed recommendations for public health decision-making. The course begins with fundamental biological and mathematical definitions before guiding you through step-by-step model construction and code-based analysis. You will work through practical written scenarios and code snippets to solidify your understanding of epidemic forecasting. This course is designed for beginners in public health, biology, mathematics, or data science, with no prior modeling experience required. Start reading today to build your foundational skills in epidemiological modeling.