Epidemiological Modeling: Simulating Interventions and Calibration

Understanding how infectious diseases spread and how public health interventions impact their trajectory is crucial for modern health planning. This course teaches you how to construct, simulate, and refine mathematical models to evaluate real-world health strategies. You will transition from basic epidemiological concepts to advanced compartment models that evaluate vaccination and treatment programs. Through clear, step-by-step written explanations, you will gain the skills to simulate diverse intervention scenarios and align your models with actual historical data. What you'll learn: - Understand foundational epidemiological terms, compartment structures, and the mathematical principles behind the SIR model. - Model public health interventions by incorporating treatment and vaccination compartments into standard disease frameworks. - Simulate complex vaccine dynamics, including partial immunity, waning protection, and "leaky" vaccines. - Calibrate disease models to real-world public health data using modern parameter estimation and data-fitting techniques. - Analyze the simulated outcomes of different policy scenarios to evaluate the potential impact of health interventions. The course starts with fundamental concepts of compartment modeling before moving into intervention design and advanced calibration techniques. You will progress from basic differential equations to practical simulation scenarios entirely through guided written explanations and code-based examples. This course is designed for beginners in public health, data science, or applied mathematics. No prior experience in epidemiology or advanced programming is required to start. Start your journey into public health modeling and learn how to simulate real-world disease interventions today.