Epidemic Modeling: Principles, Algorithms, and Applications is a comprehensive introduction and practical guide to building, understanding, and applying epidemiological models in real-world contexts. Bridging theory and practice, the book walks readers through the full modelling pipeline from the biological, social, and political drivers of disease spread to classical compartmental models such as SIR, and onward to modern computational techniques including agent-based simulation, machine learning, and optimization under uncertainty. Each chapter builds progressively, pairing clear conceptual explanations with hands-on code, real data examples, and step-by-step methods that readers can adapt to new challenges. Drawing on extensive academic and industry experience, the text emphasizes how modelling decisions are made in practice addressing real-world complications such as incomplete data, reporting delays, and measurement error. Designed as both a learning resource and long-term reference, the book equips readers to move beyond running existing models to designing, evaluating, and communicating their own. It also fosters a shared language across disciplines, helping technical and non-technical audiences engage meaningfully with modelling insights. Timely and practical, this book empowers the next generation of modelers and decision-makers to respond effectively to an increasingly complex epidemic landscape.
1. Chapter 6: Introduction to multi-agent simulations - p. 123 2. Chapter 7: Data-driven models - p. 171 3. Chapter 8: Parameter estimation and model calibration - p. 225 4. Chapter 9: Integrating surveillance, mobility, and genomic data in pandemic models - p. 275 5. Chapter 10: Modeling and optimizing intervention policies - p. 295 6. Chapter 11: Uncertainty quantification, sensitivity analysis, and model comparison - p. 315 7. Chapter 12: Computational considerations in pandemic models - p. 347 8. Chapter 13: Closing remarks and future directions - p. 371 9. References - p. 377 10. Index - p. 415
Epidemic Modeling: Principles, Algorithms, and Applications is a comprehensive introduction and practical guide to building, understanding, and applying epidemiological models in real-world contexts. Bridging theory and practice, the book walks readers through the full modelling pipeline from the biological, social, and political drivers of disease spread to classical compartmental models such as SIR, and onward to modern computational techniques including agent-based simulation, machine learning, and optimization under uncertainty. Each chapter builds progressively, pairing clear conceptual explanations with hands-on code, real data examples, and step-by-step methods that readers can adapt to new challenges. Drawing on extensive academic and industry experience, the text emphasizes how modelling decisions are made in practice addressing real-world complications such as incomplete data, reporting delays, and measurement error. Designed as both a learning resource and long-term reference, the book equips readers to move beyond running existing models to designing, evaluating, and communicating their own. It also fosters a shared language across disciplines, helping technical and non-technical audiences engage meaningfully with modelling insights. Timely and practical, this book empowers the next generation of modelers and decision-makers to respond effectively to an increasingly complex epidemic landscape.
1. Chapter 6: Introduction to multi-agent simulations - p. 123 2. Chapter 7: Data-driven models - p. 171 3. Chapter 8: Parameter estimation and model calibration - p. 225 4. Chapter 9: Integrating surveillance, mobility, and genomic data in pandemic models - p. 275 5. Chapter 10: Modeling and optimizing intervention policies - p. 295 6. Chapter 11: Uncertainty quantification, sensitivity analysis, and model comparison - p. 315 7. Chapter 12: Computational considerations in pandemic models - p. 347 8. Chapter 13: Closing remarks and future directions - p. 371 9. References - p. 377 10. Index - p. 415
