Computational Modeling of Infectious Disease: With Applications in Python provides an illustrated compendium of tools and tactics for analyzing infectious diseases using cutting-edge computational methods. From simple S(E)IR models, and through time series analysis and geospatial models, this book is both a guided tour through the computational analysis of infectious diseases and a quick-reference manual. Chapters are accompanied by extensive practical examples in Python, illustrating applications from start to finish. This book is designed for researchers and practicing infectious disease forecasters, modelers, data scientists, and those who wish to learn more about analysis of infectious disease processes in the real world.
1. 1 Introduction - p. 1 2. 2 Simple compartmental models - p. 19 3. 2.1: The intuition of compartmental models - p. 19 4. 2.2: Modeling mortality and vital dynamics - p. 39 5. 2.3: Models of immunity - p. 53 6. 2.4: Models with latent periods, asymptomatic infection, and carrier states - p. 61 7. 2.5: Empirical parameter estimation - p. 69 8. 3 Host factors - p. 93 9. 4 Host-vector and multihost systems - p. 121 10. 5 Multipathogen dynamics - p. 151 11. 6 Modeling the control of infectious disease - p. 173 12. 6.1: Modeling vaccination - p. 173 13. 6.2: Duration and effectiveness of vaccine-induced immunity - p. 187 14. 6.3: Isolation and quarantine - p. 199 15. 7 Temporal dynamics of epidemics - p. 217 16. 8 Spatial dynamics of epidemics - p. 257 17. 9 Agent-based modeling - p. 305 18. 9.1: The fundamentals of agent-based modeling - p. 305 19. 9.2: Agent-based models of disease control - p. 345 20. 9.3: Agent-based models of mobility - p. 366 21. A Fundamentals of Python syntax - p. 377 22. References - p. 413 23. Index - p. 439
Computational Modeling of Infectious Disease: With Applications in Python provides an illustrated compendium of tools and tactics for analyzing infectious diseases using cutting-edge computational methods. From simple S(E)IR models, and through time series analysis and geospatial models, this book is both a guided tour through the computational analysis of infectious diseases and a quick-reference manual. Chapters are accompanied by extensive practical examples in Python, illustrating applications from start to finish. This book is designed for researchers and practicing infectious disease forecasters, modelers, data scientists, and those who wish to learn more about analysis of infectious disease processes in the real world.
1. 1 Introduction - p. 1 2. 2 Simple compartmental models - p. 19 3. 2.1: The intuition of compartmental models - p. 19 4. 2.2: Modeling mortality and vital dynamics - p. 39 5. 2.3: Models of immunity - p. 53 6. 2.4: Models with latent periods, asymptomatic infection, and carrier states - p. 61 7. 2.5: Empirical parameter estimation - p. 69 8. 3 Host factors - p. 93 9. 4 Host-vector and multihost systems - p. 121 10. 5 Multipathogen dynamics - p. 151 11. 6 Modeling the control of infectious disease - p. 173 12. 6.1: Modeling vaccination - p. 173 13. 6.2: Duration and effectiveness of vaccine-induced immunity - p. 187 14. 6.3: Isolation and quarantine - p. 199 15. 7 Temporal dynamics of epidemics - p. 217 16. 8 Spatial dynamics of epidemics - p. 257 17. 9 Agent-based modeling - p. 305 18. 9.1: The fundamentals of agent-based modeling - p. 305 19. 9.2: Agent-based models of disease control - p. 345 20. 9.3: Agent-based models of mobility - p. 366 21. A Fundamentals of Python syntax - p. 377 22. References - p. 413 23. Index - p. 439
