The effect of using different types of periodic contact rate on the behaviour of infectious diseases: A simulation study

This paper studies the effect of using different types of seasonally varying contact rate on the behaviour of the seasonally varying infectious diseases for an SEIR epidemic model. Our target is to investigate the long term behaviour of the system in response to changes in beta(1), the amplitude parameter of the seasonal contact rate, which is our bifurcation parameter. This amplitude parameter is used as a filter to plot the length in years of the period of the stable endemic periodic solution of the SEIR model. Another main aim of this simulation study is to explain how can the type of the contact rate affect the behaviour of the disease dynamics. The simulation results have indicated that using different functional forms of seasonally varying contact rate generates different patterns of solutions for each disease parameter set and type of contact rate. So prediction of the type of disease outbreaks depends on the form of contact rate. Thus it is important to determine which type of contact rate is more likely to match the actual dynamics of each disease. Also these results have shown how the dynamics of the disease depend on the amplitude of the seasonally varying contact rate. Apart from some of the results for measles with a sinusoidal periodic function the simulation results are original and give a clear and a much broader insight into the features of the dynamics of these diseases [D. Greenhalgh, I.A. Moneim, SIRS epidemic model and simulations using different types of seasonal contact rate, Syst. Anal. Modelling Simul. 43(5) (2003) 573-600; I.A. Moneim, D. Greenhalgh, Threshold and stability results for an SIRS epidemic model with a general periodic vaccination strategy, J. Biol. Syst. 13(2) (2005), to appear].

[1]  David Greenhalgh,et al.  SIRS epidemic model and simulations using different types of seasonal contact rate , 2003 .

[2]  B. Bolker,et al.  Chaos and complexity in measles models: a comparative numerical study. , 1993, IMA journal of mathematics applied in medicine and biology.

[3]  David Greenhalgh,et al.  Deterministic models for common childhood diseases , 1990 .

[4]  I B Schwartz,et al.  Seasonality and period-doubling bifurcations in an epidemic model. , 1984, Journal of theoretical biology.

[5]  David Greenhalgh,et al.  THRESHOLD AND STABILITY RESULTS FOR AN SIRS EPIDEMIC MODEL WITH A GENERAL PERIODIC VACCINATION STRATEGY , 2005 .

[6]  Horst R. Thieme,et al.  Persistence under relaxed point-dissipativity (with application to an endemic model) , 1993 .

[7]  I B Schwartz,et al.  Infinite subharmonic bifurcation in an SEIR epidemic model , 1983, Journal of mathematical biology.

[8]  I B Schwartz,et al.  Small amplitude, long period outbreaks in seasonally driven epidemics , 1992, Journal of mathematical biology.

[9]  David Greenhalgh,et al.  Use of a periodic vaccination strategy to control the spread of epidemics with seasonally varying contact rate. , 2005, Mathematical biosciences and engineering : MBE.

[10]  Louis J. Gross,et al.  Applied Mathematical Ecology , 1990 .

[11]  R. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991, Annals of Internal Medicine.

[12]  I B Schwartz,et al.  Multiple stable recurrent outbreaks and predictability in seasonally forced nonlinear epidemic models , 1985, Journal of mathematical biology.

[13]  T. A. Burton,et al.  Stability and Periodic Solutions of Ordinary and Functional Differential Equations , 1986 .

[14]  S. Duncan,et al.  The dynamics of measles epidemics. , 1997, Theoretical population biology.

[15]  H. Hethcote Three Basic Epidemiological Models , 1989 .

[16]  I. Gumowski,et al.  THE INCIDENCE OF INFECTIOUS DISEASES UNDER THE INFLUENCE OF SEASONAL FLUCTUATIONS - ANALYTICAL APPROACH , 1977 .

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