The balanced implicit method of preserving positivity for the stochastic SIQS epidemic model

Abstract The main purpose of this paper is to develop a numerical method preserving positivity for a stochastic SIQS epidemic model which is an effective tactics for forecasting and controlling infectious diseases. By the explicit Euler–Maruyama (EM) scheme, we can obtain a numerical approximate solution of the stochastic SIQS epidemic model. We will explore the convergence property of the EM approximate solution to the true solution. However, the explicit EM scheme has it own defection because of the influence of environmental fluctuation, it may not preserve positivity of numerical solution. Therefore, we will construct a balanced implicit numerical method which motivated by Schurz in Schurz (1996). It is confirmed that the Balanced Implicit Method (BIM) can preserve positivity. We prove that the BIM approximate solution will converge to the true solution. By numerical simulations to verify the positivity of solution and the efficiency of our proposed numerical method.

[1]  Weiming Wang,et al.  Periodic behavior in a FIV model with seasonality as well as environment fluctuations , 2017, J. Frankl. Inst..

[2]  Yongfeng Guo,et al.  Construction of positivity preserving numerical method for stochastic age-dependent population equations , 2017, Appl. Math. Comput..

[3]  Fengying Wei,et al.  Stochastic permanence of an SIQS epidemic model with saturated incidence and independent random perturbations , 2016 .

[4]  Martin Hairer,et al.  Loss of regularity for Kolmogorov equations , 2012, 1209.6035.

[5]  X. Mao,et al.  Stochastic Differential Equations and Applications , 1998 .

[6]  Henri Schurz,et al.  CONVERGENCE AND STABILITY OF BALANCED IMPLICIT METHODS FOR SYSTEMS OF SDES , 2005 .

[7]  Alexander Grey,et al.  The Mathematical Theory of Infectious Diseases and Its Applications , 1977 .

[8]  Chun-Fang Chiang,et al.  Learning during a crisis: The SARS epidemic in Taiwan , 2011, Journal of Development Economics.

[9]  Christian Kahl,et al.  Balanced Milstein Methods for Ordinary SDEs , 2006, Monte Carlo Methods Appl..

[10]  Huanhe Dong,et al.  Periodic solution and ergodic stationary distribution of two stochastic SIQS epidemic systems , 2018, Physica A: Statistical Mechanics and its Applications.

[11]  Yuecai Han,et al.  The threshold of a stochastic SIQS epidemic model , 2014 .

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

[13]  G. Maruyama Continuous Markov processes and stochastic equations , 1955 .

[14]  Hua Yang,et al.  Construction of positivity preserving numerical method for jump-diffusion option pricing models , 2017, J. Comput. Appl. Math..

[15]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[16]  Bo Yang Stochastic dynamics of an SEIS epidemic model , 2016 .

[17]  H. Kong,et al.  Economic Impact of SARS: The Case of Hong Kong , 2004, Asian Economic Papers.

[18]  X. Mao,et al.  A highly sensitive mean-reverting process in finance and the Euler-Maruyama approximations , 2008 .

[19]  Xuerong Mao,et al.  The Euler-Maruyama approximation for the asset price in the mean-reverting-theta stochastic volatility model , 2012, Comput. Math. Appl..

[20]  Hai-Feng Huo,et al.  The threshold of a stochastic SIQS epidemic model , 2017 .

[21]  Zhijun Liu,et al.  Modelling and analysis of global resurgence of mumps: A multi-group epidemic model with asymptomatic infection, general vaccinated and exposed distributions , 2017 .

[22]  Hong Xiang,et al.  Dynamics of the deterministic and stochastic SIQS epidemic model with non-linear incidence , 2014, Appl. Math. Comput..

[23]  Julien Arino,et al.  Global Results for an Epidemic Model with Vaccination that Exhibits Backward Bifurcation , 2003, SIAM J. Appl. Math..

[24]  Henri Schurz,et al.  Numerical Regularization for SDEs: Construction of Nonnegative Solutions , 1995 .

[25]  Daqing Jiang,et al.  A new existence theory for positive periodic solutions to functional differential equations with impulse effects , 2006, Comput. Math. Appl..

[26]  E. Platen,et al.  Balanced Implicit Methods for Stiff Stochastic Systems , 1998 .

[27]  Xuerong Mao,et al.  The truncated Euler-Maruyama method for stochastic differential equations , 2015, J. Comput. Appl. Math..

[28]  E. Helfand,et al.  Numerical integration of stochastic differential equations — ii , 1979, The Bell System Technical Journal.