Epidemic Models: Their Structure and Relation to Data

Epidemiology is one of the most vibrant branches of applied ecology, and draws heavily on interdisciplinary studies, as demonstrated by the results gained from a six-month NATA Advanced Research Workshop, held in 1993.

Preface; Introduction; Part I. Conceptual Framework: 1. Some problems in the theory of infectious disease transmission and control Klaus Dietz; 2. The structure of epidemic models Denis Mollison; 3. Coupling methods in epidemic theory Frank Ball; 4. Collective epidemic processes: a general modelling approach to the final outcome of SIR epidemics Claude Lefevre and Philippe Picard; 5. The threshold concept in deterministic and stochastic models Ingemar Nasell; 6. How does transmission of infection depend on population size? Mart de Jong, Odo Diekmann and Hans Heesterbeek; 7. The legacy of Kermack and McKendrick Odo Diekmann, Hans Metz and Hans Heesterbeek; Part II. Spatial Models: 8. Incorporating spatial components into models of epidemic spread Andrew Cliff; 9. Velocities of epidemic spread Hans Metz and Frank van den Bosch; 10. Spatial epidemic models Richard Durrett; 11. A perturbation approach to nonlinear deterministic epidemic waves Henry Daniels; 12. Epidemic plant diseases: a stochastic model of leaf and stem lesion Lynne Billard, P. W. A. Dayananda and Zhen Zhao; Part III. Nonlinear Time and Space-Time Dynamics: 13. Detecting nonlinearity and chaos in epidemic data Stephen Ellner, Ronald Gallant and James Theiler; 14. Seasonality, demography and the dynamics of measles in developed countries Bryan Grenfell, Ben Bolker and Adam Kleczkowski; Part IV. Heterogeneity in Human Diseases: 15. Grouping in population models Simon Levin; 16. Core groups and R0s for subgroups in heterogeneous SIS and SI models John Jacquez, Carl Simon and James Koopman; 17. Data driven network models for the spread of disease Martina Morris; 18. The effect of antigenic diversity on endemic prevalence Sunetra Gupta, Katherine Trenholme, Martin Cox, Roy Anderson and Karen Day; Part V. Data Analysis: Estimation and Prediction: 19. Statistical challenges of epidemic data Niels Becker; 20. Primary components of epidemic models Andrew Cairns; 21. Estimation and prediction in tropical disease control: the example of onchocerciasis Hans Remme, Soumbey Alley and Anton Plaisier; 22. Some current trends in estimating vaccine efficacy Ira Longini, Elizabeth Halloran and Michael Haber; 23. Operational modelling of HIV/AIDS to assist public health control Norman Bailey; Appendix. Problem areas S. Ellner, O. Diekmann and N. Becker.