Modeling and Controlling Epidemics on Networks

How do infectious diseases spread through modern, interconnected populations, and how can we stop them? Understanding the structural networks of human interaction is key to predicting and controlling outbreaks in the real world. This written course guides you through the foundational mathematics and computational concepts of network epidemiology, empowering you to simulate disease spread and evaluate containment strategies. What you'll learn: - Understand basic epidemic models like SIR and SIS and how they translate to network structures - Analyze network topology, including degree distribution and clustering, to predict transmission dynamics - Simulate disease propagation using modern Python libraries like NetworkX for graph representations - Evaluate control strategies such as targeted vaccination, quarantine, and contact tracing - Compare random, scale-free, and small-world networks in the context of disease vulnerability You will start with core terminology, basic graph theory, and foundational epidemiological definitions before moving on to practical simulation scenarios. Through step-by-step written explanations and structured code snippets, you will learn to construct network models and design targeted interventions. This course is designed for beginners in data science, public health, or systems engineering, with no advanced prerequisites required. Start reading today to build your foundational skills in network epidemiology.