传染病流行模型中高维非线性参数估计方法

Higher dimensional nonlinear parameters estimation in the epidemic model of infectious diseases

目的研究根据流行数据逆向估计流行模型中高维非线性参数的方法。方法采用Gepasi3.3软件中的全局优化方法估计非线性传播动力学模型中的各个参数。以SARS流行模型的参数估计为例说明其应用。结果流行模型中各状态变量之间互相转换的公式可以方便地移植到Gepasi软件中的模型定义部分。选择适当的全局优化算法,容易估计出与实际流行数据拟合最佳的各个参数的取值。实例研究发现,Gepasi软件中的遗传算法可以用于估计SARS流行模型中的未知参数。采用估计参数模拟的北京每日新增SARS临床诊断病例数与实际的流行数据相比无明显差异(P>0.05)。结论Gepasi软件中的全局优化方法是强健和可靠的,可以用于流行模型中高维非线性参数的估计。

Objective To develop a method to estimate higher dimensional nonlinear parameters in the epidemic model of infectious diseases. Methods Global optimization method in software Gepasi 3.3 was employed to estimate the parameters in the epidemic model. A case study about parameters estimation of the epidemic model of severe acute respiratory syndrome (SARS) was carried out to illustrate its application. Results The formulas for describing transforms between state variables in the epidemic model can be transplanted to that for model definition in Gepasi easily. By employing an appropriate global optimization algorithm, the estimated values of the parameters which fit the epidemic data perfectly were obtained. The case study found that genetic algorithm in software Gepasi could be used to estimate parameters of SARS epidemic model successfully and there was no significant difference between estimated number based estimated model parameters and actual number of new SARS probable cases per day in Beijing (P>0.05). Conclusions Global optimization method in software Gepasi is robust and reliable for estimation of higher dimensional nonlinear parameters in the epidemic model of infectious diseases.