Introduction to Epidemic Network Models

How do viruses, information, or behaviors spread through a population? Understanding the structural pathways of transmission is key to predicting and controlling outbreaks in our interconnected world. This text-only course guides you from the fundamental principles of epidemiology to the mathematics of network science, enabling you to conceptualize and analyze how diseases propagate through complex systems. What you'll learn: Understand the core terminology of epidemiology, including transmission rates, recovery rates, and basic reproduction numbers; Analyze network structures using nodes and edges to map real-world contact patterns; Apply classic compartmental models like SIR and SIS to simulate disease progression; Evaluate how network topology, such as scale-free and small-world networks, influences epidemic thresholds; Explore modern computational approaches to modeling intervention strategies like vaccination and quarantine. You will start with the basic definitions of disease dynamics before moving step-by-step into network theory and mathematical modeling. Through clear written explanations and structured analytical exercises, you will build a solid foundation in network epidemiology. This course is designed for beginners interested in public health, network science, or data modeling, with no prior advanced mathematical background required. Begin reading today to master the fundamentals of epidemic network modeling.