Previous |  Up |  Next

Article

Keywords:
SIR epidemic models; stochastic differential equations; weak solution; simulation
Summary:
This paper proposes a stochastic diffusion model for the spread of a susceptible-infective-removed Kermack–McKendric epidemic (M1) in a population which size is a martingale $N_t$ that solves the Engelbert–Schmidt stochastic differential equation (). The model is given by the stochastic differential equation (M2) or equivalently by the ordinary differential equation (M3) whose coefficients depend on the size $N_t$. Theorems on a unique strong and weak existence of the solution to (M2) are proved and computer simulations performed.
References:
[1] Allen E. J.: Stochastic differential equations and persistence time for two interacting populations. Dynamics of Continuous, Discrete and Impulsive Systems 5 (1999), 271–281 MR 1678255 | Zbl 0946.60058
[2] Allen L. J. S., Kirupaharan N.: Asymptotic dynamics of deterministic and stochastic epidemic models with multiple pathogens. Internat. J. Num. Anal. Model. 2 (2005), 329–344 MR 2112651 | Zbl 1080.34033
[3] Andersson H., Britton T.: Stochastic Epidemic Models and Their Statistical Analysis. (Lecture Notes in Statistics 151.) Springer–Verlag, New York 2000 MR 1784822 | Zbl 0951.92021
[4] Bailey N. T. J.: The Mathematical Theory of Epidemics. Hafner Publishing Comp., New York 1957 MR 0095085
[5] Ball F., O’Neill P.: A modification of the general stochastic epidemic motivated by AIDS modelling. Adv. in Appl. Prob. 25 (1993), 39–62 MR 1206532 | Zbl 0777.92018
[6] Becker N. G.: Analysis of infectious disease data. Chapman and Hall, London 1989 MR 1014889 | Zbl 0782.92015
[7] Daley D. J., Gani J.: Epidemic Modelling; An Introduction. Cambridge University Press, Cambridge 1999 MR 1688203 | Zbl 0964.92035
[8] Greenhalgh D.: Stochastic Processes in Epidemic Modelling and Simulation. In: Handbook of Statistics 21 (D. N. Shanbhag and C. R. Rao, eds.), North–Holland, Amsterdam 2003, pp. 285–335 MR 1973547 | Zbl 1017.92030
[9] Hurt J.: Mathematica$^{®}$ program for Kermack–McKendrick model. Department of Probability and Statistics, Charles University in Prague 2005
[10] Kallenberg O.: Foundations of Modern Probability. Second edition. Springer–Verlag, New York 2002 MR 1876169 | Zbl 0996.60001
[11] Kendall D. G.: Deterministic and stochastic epidemics in closed population. In: Proc. Third Berkeley Symp. Math. Statist. Probab. 4, Univ. of California Press, Berkeley, Calif. 1956, pp. 149–165 MR 0084936
[12] Kermack W. O., McKendrick A. G.: A contribution to the mathematical theory of epidemics. Proc. Roy. Soc. London Ser. A 115 (1927), 700–721
[13] Kirupaharan N.: Deterministic and Stochastic Epidemic Models with Multiple Pathogens. PhD Thesis, Texas Tech. Univ., Lubbock 2003 MR 2704799 | Zbl 1080.34033
[14] Kirupaharan N., Allen L. J. S.: Coexistence of multiple pathogen strains in stochastic epidemic models with density-dependent mortality. Bull. Math. Biol. 66 (2004), 841–864 MR 2255779
[15] Rogers L. C. G., Williams D.: Diffusions, Markov Processes and Martingales. Vol. 1: Foundations. Cambridge University Press, Cambridge 2000 MR 1796539 | Zbl 0977.60005
[16] Rogers L. C. G., Williams D.: Diffusions, Markov Processes and Martingales. Vol. 2: Itô Calculus. Cambridge University Press, Cambridge 2000 MR 1780932 | Zbl 0977.60005
[17] Štěpán J., Dostál P.: The $dX(t)=Xb(X)dt + X\sigma (X)\,dW$ equation and financial mathematics I. Kybernetika 39 (2003), 653–680 MR 2035643
[18] Štěpán J., Dostál P.: The $dX(t)=Xb(X)dt + X\sigma (X)dW$ equation and financial mathematics II. Kybernetika 39 (2003), 681–701 MR 2035644
[19] Subramaniam R., Balachandran, K., Kim J. K.: Existence of solution of a stochastic integral equation with an application from the theory of epidemics. Nonlinear Funct. Anal. Appl. 5 (2000), 23–29 MR 1795707
Partner of
EuDML logo