Time series modelling of childhood diseases: a dynamical systems approach

A key issue in the dynamical modelling of epidemics is the synthesis of complex mathematical models and data by means of time series analysis. We report such an approach, focusing on the particularly well‐documented case of measles. We propose the use of a discrete time epidemic model comprising the infected and susceptible class as state variables. The model uses a discrete time version of the susceptible–exposed–infected–recovered type epidemic models, which can be fitted to observed disease incidence time series. We describe a method for reconstructing the dynamics of the susceptible class, which is an unobserved state variable of the dynamical system. The model provides a remarkable fit to the data on case reports of measles in England and Wales from 1944 to 1964. Morever, its systematic part explains the well‐documented predominant biennial cyclic pattern. We study the dynamic behaviour of the time series model and show that episodes of annual cyclicity, which have not previously been explained quantitatively, arise as a response to a quicker replenishment of the susceptible class during the baby boom, around 1947.

[1]  D Mollison,et al.  Deterministic and stochastic models for the seasonal variability of measles transmission. , 1993, Mathematical biosciences.

[2]  Mick G. Roberts,et al.  Mathematical models for microparasites of wildlife , 1995 .

[3]  Bryan T. Grenfell,et al.  Chance and Chaos in Measles Dynamics , 1992 .

[4]  S. P. Ellner,et al.  Measles as a case study in nonlinear forecasting and chaos , 1994, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

[5]  Robert M. May,et al.  Simple mathematical models with very complicated dynamics , 1976, Nature.

[6]  A. R. Gallant,et al.  Noise and Nonlinearity in Measles Epidemics: Combining Mechanistic and Statistical Approaches to Population Modeling , 1998, The American Naturalist.

[7]  Adam Kleczkowski,et al.  Seasonality and extinction in chaotic metapopulations , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[8]  S. Levin,et al.  Dynamical behavior of epidemiological models with nonlinear incidence rates , 1987, Journal of mathematical biology.

[9]  R. Anderson,et al.  Measles in developing countries Part I. Epidemiological parameters and patterns , 1988, Epidemiology and Infection.

[10]  G Sugihara,et al.  Distinguishing error from chaos in ecological time series. , 1990, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[11]  N. Ling The Mathematical Theory of Infectious Diseases and its applications , 1978 .

[12]  L. Olsen,et al.  Chaos versus noisy periodicity: alternative hypotheses for childhood epidemics. , 1990, Science.

[13]  D Brown,et al.  Outbreak of measles in a teenage school population: the need to immunize susceptible adolescents , 1994, Epidemiology and Infection.

[14]  B. T. Grenfell,et al.  Ecology of Infectious Diseases in Natural Populations: Introduction , 1995 .

[15]  D. Schenzle An age-structured model of pre- and post-vaccination measles transmission. , 1984, IMA journal of mathematics applied in medicine and biology.

[16]  M. Bartlett Measles Periodicity and Community Size , 1957 .

[17]  D. Earn,et al.  A simple model for complex dynamical transitions in epidemics. , 2000, Science.

[18]  B T Grenfell,et al.  Spatial heterogeneity, nonlinear dynamics and chaos in infectious diseases , 1995, Statistical methods in medical research.

[19]  F. Takens Detecting strange attractors in turbulence , 1981 .

[20]  W M Schaffer,et al.  The case for chaos in childhood epidemics. II. Predicting historical epidemics from mathematical models , 1993, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[21]  W. O. Kermack,et al.  Contributions to the Mathematical Theory of Epidemics. III. Further Studies of the Problem of Endemicity , 1933 .

[22]  K. Dietz,et al.  The Incidence of Infectious Diseases under the Influence of Seasonal Fluctuations , 1976 .

[23]  Stephen P. Ellner,et al.  Chaos in a Noisy World: New Methods and Evidence from Time-Series Analysis , 1995, The American Naturalist.

[24]  Mark Bartlett,et al.  The Critical Community Size for Measles in the United States , 1960 .

[25]  J. Yorke,et al.  Recurrent outbreaks of measles, chickenpox and mumps. I. Seasonal variation in contact rates. , 1973, American journal of epidemiology.

[26]  Robert M. May,et al.  Population Biology of Microparasitic Infections , 1986 .

[27]  D. Rand,et al.  Correlation models for childhood epidemics , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[28]  R. Anderson,et al.  Measles in developing countries. Part II. The predicted impact of mass vaccination , 1988, Epidemiology and Infection.

[29]  B. Bolker,et al.  Impact of vaccination on the spatial correlation and persistence of measles dynamics. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[30]  A. Gallant,et al.  Finding Chaos in Noisy Systems , 1992 .

[31]  M. Keeling,et al.  Patterns of density dependence in measles dynamics , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[32]  M. Kot,et al.  Nearly one dimensional dynamics in an epidemic. , 1985, Journal of theoretical biology.

[33]  H. B. Wilson,et al.  Chaotic stochasticity: a ubiquitous source of unpredictability in epidemics , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[34]  E. Miller,et al.  Immunisation policies--successes, failures and the future. , 1996, Journal of clinical pathology.

[35]  B. T. Grenfell,et al.  Disease Extinction and Community Size: Modeling the Persistence of Measles , 1997, Science.

[36]  Odo Diekmann,et al.  How does transmission of infection depend on population size , 1995 .

[37]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[38]  B Grenfell,et al.  Empirical determinants of measles metapopulation dynamics in England and Wales , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[39]  P. Fine,et al.  Measles in England and Wales--I: An analysis of factors underlying seasonal patterns. , 1982, International journal of epidemiology.

[40]  A. J. Hall Infectious diseases of humans: R. M. Anderson & R. M. May. Oxford etc.: Oxford University Press, 1991. viii + 757 pp. Price £50. ISBN 0-19-854599-1 , 1992 .

[41]  Jianqing Fan,et al.  Local polynomial modelling and its applications , 1994 .