The well-known stochastic SIS model characterized by highly nonlinear in epidemiology has a unique positive solution taking values in a bounded domain with a series of dynamical behaviors. However, the approximation methods to maintain the positivity and long-time behaviors for the stochastic SIS model, while very important, are also lacking. In this paper, based on a logarithmic transformation, we propose a novel explicit numerical method for a stochastic SIS epidemic model whose coefficients violate the global monotonicity condition, which can preserve the positivity of the original stochastic SIS model. And we show the strong convergence of the numerical method and derive that the rate of convergence is of order one. Moreover, the extinction of the exact solution of stochastic SIS model is reproduced. Some numerical experiments are given to illustrate the theoretical results and testify the efficiency of our algorithm.
翻译:以高度非线性流行病学为特征的众所周知的随机SIS模型具有独特的积极解决办法,在封闭的域域内采用一系列动态行为的价值。然而,维持随机SIS模型的假设性和长期行为的近似方法虽然非常重要,但也缺乏。在本文中,基于对数变换,我们提议对随机SIS流行病模型采用新的明确数字方法,该模型的系数违反全球单一性条件,这可以保持原始随机性SIS模型的假设性。我们展示了数字方法的高度趋同,并推断趋同率是顺序的。此外,还复制了随机SISS模型确切解决办法的消亡。一些数字实验是为了说明理论结果并证明我们算法的效率。