Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation
* Model construction, analysis and interpretation receive detailed attention
* Uniquely covers both deterministic and stochastic viewpoints
* Examples of applications given throughout
* Extensive coverage of the latest research into the mathematical modelling of epidemics of infectious diseases
* Provides a solid foundation of modelling skills
The reader will learn to translate, model, analyse and interpret, with the help of the numerous exercises. In literally working through this text, the reader acquires modelling skills that are also valuable outside of epidemiology, certainly within population dynamics, but even beyond that. In addition, the reader receives training in mathematical argumentation. The text is aimed at applied mathematicians with an interest in population biology and epidemiology, at theoretical biologists and epidemiologists. Previous exposure to epidemic concepts is not required, as all background information is given. The book is primarily aimed at self-study and ideally suited for small discussion groups, or for use as a course text.
What it is All About and What Not.
The Top Down Approach.
Portrait of the Reader as a Young Person.
A Brief Outline of the Book.
And What About Reality?
I: THE BARE BONES: BASIC ISSUES EXPLAINED IN THE SIMPLEST CONTEXT.
The Epidemic in a Closed Population.
Heterogeneity: The Art of Averaging.
Dynamics at the Demographic Time Scale.
II: STRUCTURED POPULATIONS.
The Concept of State.
The Basic Reproduction Ratio.
And Everything Else......
What is Contact?
III: THE HARD PART: ELABORATIONS (ALMOST) ALL EXERCISES.
Elaborations for Part I.
Elaborations for Part II.
Appendix A: Stochastic Basic of the Kermack-McKendrick ODE Model.
Appendix B: Bibliographic Skeleton.
It is a real tour de force - a mine of wisdom and intuition. The style has just the right level of informality and the way in which the main exposition is separated from the "elaborations" works extremely well.", Professor Valerie Isham, Head of Department, Department of Statistical Science, University College London, UK#