Epidemic models : their structure and relation to data


    • Mollison, Denis


Epidemic models : their structure and relation to data

edited by Denis Mollison

(Publications of the Newton Institute, 5)

Cambridge University Press, 1995

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Includes bibliographical references



The problems of understanding and controlling disease present a range of mathematical challenges, from broad theoretical issues to specific practical ones, making epidemiology one of the most vibrant branches of applied ecology. Progress in this field requires interdisciplinary collaboration; leading researchers with a wide range of mathematical expertise and close involvement in applied fields across the social, medical and biological sciences came together for a NATO Advanced Research Workshop marking the opening of a six-month programme on Epidemic Models at the Newton Institute in Cambridge in 1993. This volume is a result of that collaboration and surveys the state of epidemic modelling at the time in relation to basic aims such as understanding, prediction, and evaluation and implementation of control strategies.


  • 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.

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