Main description:

This book is designed to be a practical study in infectious disease dynamics. It offers an easy-to-follow implementation and analysis of mathematical epidemiology. It focuses on recent case studies in order to explore various conceptual, mathematical, and statistical issues. The dynamics of infectious diseases shows a wide diversity of pattern. Some have locally persistent chains-of-transmission, others persist spatially in consumer-resource metapopulations. Some infections are prevalent among the young, some among the old and some are age-invariant. Temporally, some diseases have little variation in prevalence, some have predictable seasonal shifts and others exhibit violent epidemics that may be regular or irregular in their timing.
Models and 'models-with-data' have proved invaluable for understanding and predicting this diversity, and thence help improve intervention and control. Using mathematical models to understand infectious disease, dynamics has a very rich history in epidemiology. The field has seen broad expansions of theories as well as a surge in real-life application of mathematics to dynamics and control of infectious disease. The chapters of Epidemics: Models and Data Using R have been organized as follows: chapters 1-10 is a mix and match of models, data and statistics pertaining to local disease dynamics; chapters 11-13 pertains to spatial and spatiotemporal dynamics; chapter 14 highlights similarities between the dynamics of infectious disease and parasitoid-host dynamics; Finally, chapters 15 and 16 overview additional statistical methodology useful in studies of infectious disease dynamics.
This book can be used as a guide for working with data, models and 'models-and-data' to understand epidemics and infectious disease dynamics in space and time. All the code and data sets are distributed in the epimdr2 R package to facilitate the hands-on philosophy of the text.

Contents:

Chapter 1. Introduction.- Chapter 2. SIR.- Chapter 3. R0.- Chapter 4. FoI and age-dependent incidence.- Chapter 5. Seasonality.- Chapter 6. Time Series Analysis.- Chapter 7. TSIR.- Chapter 8.- Trajectory Matching.- Chapter 9. Stability and Resonant Periodicity.- Chapter 10. Exotica.- Chapter 11. Spatial Dynamics.- Chapter 12. Transmission on Networks.- Chapter 13. Spatial and Spatiotemporal Patterns.- Chapter 14. Parasitoids.- Chapter 15. Non-Independent Data.- Chapter 16. Quantifying In-Host Patterns.- Bibliography.- Index.