Epidemic Models: Their Structure and Relation to Data / Edition 1

by Denis Mollison

Overview

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 biology. Progress in this field requires collaboration among leading researchers with a wide range of mathematical expertise and close involvement in applied fields across the social, medical and biological sciences. This volume surveys the current state of epidemic modeling in relation to basic aims such as understanding, prediction, and evaluation and implementation of control strategies. The book is divided into five parts, covering the conceptual framework, three major problem areas (space, nonlinearity, heterogeneity), and the direct relation of models to data. The contributors discuss a wide range of methodological issues, e.g. comparing different approaches to the modeling of heterogeneity and relations among different types of model; and different data analytic approaches, together with the availability and quality of the data they require.

Product Details

ISBN-13:	9780521475365
Publisher:	Cambridge University Press
Publication date:	07/13/1995
Series:	Publications of the Newton Institute , #5
Pages:	444
Product dimensions:	6.14(w) x 9.29(h) x 1.18(d)

About the Author

Mollison, Denis (Heriot-Watt Univ)

The contributors represent the specialties of statistics, mathematics, epidemiology, zoology, biostatistics, ecology, and evolutionary biology. Most are from universities and research institutions in the U.K., the Netherlands, Australia, Scotland, and Switzerland. Institutions prominently represented include Cambridge Univ, Imperial Coll, London, Heriot-Watt Univ in Scotland, Emory, and Univ of Georgia.

Table of Contents

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 Lefévre 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.

Editorial Reviews

Reviewer: Norma Kanarek, MPH, PhD (Maryland Department of Health & Mental Hygiene)
Description: A NATO Advanced Research Workshop Conference held in 1993 yielded this collection of papers about epidemic modeling updated by a renewed interest in methods to predict, manage, and understand current public health problems of infectious disease.
Purpose: The editor has amassed presentations that explicate the background theory of infectious diseases and their control; the notions of time, space, and nonlinearity in an epidemic; heterogeneity in human populations; and the fit between data and the models to be tested.
Audience: The text is a compendium of mathematical epidemiologic process issues that initiated a six-month Newton Institute study section. Although highly technical and quite comprehensive, it is a readable volume and irresistible to the epidemiologist in pursuit of understanding disease processes.
Features: A conceptual framework for the study of infectious disease determinants is offered, examined, and modified. Papers deal with the particular issues of irregularities of place, time, host characteristics, population size, and data needs for estimation and prediction. Each chapter includes examples, a summary of the topic, and applications to the practice of public health. The Appendix captures edited symposium comments about issues remaining.
Assessment: It is a textbook only for those sufficiently oriented to the statistical and epidemiologic issues. Terminology is neither indexed, defined in the order used (e.g., the definition of SIR is first found on page 53,), nor used consistently. Those with curiosity about both the basic elements of an epidemic process or a more complex view ( for example, deterministic versus stochastic processes) will be rewarded. Those with interests outside the infectious disease realm will find applications to other areas (e.g., diffusion of innovation). Use of these models to reason deductively or inductively is discussed and highlights the need for an interface between those who dwell on these models (theoreticians) and those who dwell in them (practitioners).

Doody's Review Service

"The book is a nice reference work for students and researchers interested in epidemic models." JASA

From the Publisher

4 Stars! from Doody

A NATO Advanced Research Workshop Conference held in 1993 yielded this collection of papers about epidemic modeling updated by a renewed interest in methods to predict, manage, and understand current public health problems of infectious disease. The editor has amassed presentations that explicate the background theory of infectious diseases and their control; the notions of time, space, and nonlinearity in an epidemic; heterogeneity in human populations; and the fit between data and the models to be tested. The text is a compendium of mathematical epidemiologic process issues that initiated a six-month Newton Institute study section. Although highly technical and quite comprehensive, it is a readable volume and irresistible to the epidemiologist in pursuit of understanding disease processes. A conceptual framework for the study of infectious disease determinants is offered, examined, and modified. Papers deal with the particular issues of irregularities of place, time, host characteristics, population size, and data needs for estimation and prediction. Each chapter includes examples, a summary of the topic, and applications to the practice of public health. The Appendix captures edited symposium comments about issues remaining. It is a textbook only for those sufficiently oriented to the statistical and epidemiologic issues. Terminology is neither indexed, defined in the order used (e.g., the definition of SIR is first found on page 53,), nor used consistently. Those with curiosity about both the basic elements of an epidemic process or a more complex view ( for example, deterministic versus stochastic processes) will be rewarded. Those with interests outside the infectious diseaserealm will find applications to other areas (e.g., diffusion of innovation). Use of these models to reason deductively or inductively is discussed and highlights the need for an interface between those who dwell on these models (theoreticians) and those who dwell in them (practitioners).

Norma Kanarek