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论文以全国甲型H1N1流行性感冒(下简称甲流)疫情数据为实例,讨论了采用SIR模型对甲流的传播过程进行模拟时相关参数的求解问题。分别通过优化的遗传算法(Genetic Algorithm,GA)和模拟退火算法(Simula-ted Annealing Algorithm,SA)求得该非线性模型中的重要参数阈值(日治愈率与日传染率的比值),并由该参数阈值计算出各月患病人数。论文比较分析了两种算法在精度和效率上的优劣,发现遗传算法优于模拟退火。同时模拟结果验证了SIR模型适合甲流疫情的分析模拟。
In this paper, we take the data of the outbreak of Influenza A (H1N1) as an example to discuss the solution of the relevant parameters when using the SIR model to simulate the propagation of A-flow. The thresholds of important parameters (the ratio of daily cure rate to daily transmission rate) of the nonlinear model were obtained by using genetic algorithm (GA) and simulated annealing algorithm (SA) This parameter threshold calculates the number of patients affected by each month. Papers comparative analysis of the two algorithms in the accuracy and efficiency of the pros and cons, genetic algorithm found better than simulated annealing. At the same time, the simulation results verify the SIR model is suitable for the analysis and simulation of a flu epidemic situation.