摘要
目的研究如何利用Monte-Carlo模拟开展传染病爆发早期预警。方法结合autonlab提供的症状监测数据进行模型构建和概率分布的拟合及症状监测数据可信区间的比较。结果模拟的症状监测数据频率分布经拟合符合Gamma、webull、lognormal、normal、Max Extreme、和logistic6种分布,以Gamma分布拟合最佳。经过实例验证,模拟结果及时有效。结论应用Monte-Carlo模拟,实现了对症状监测数据的分布估计,该方法可应用于更为复杂的参数分布估计。
Objective Explore how to develop early prewarning of infectious diseases by Monte-Carlo simulation. Methods According to the syndromic surveillance data provided by auton lab, fitting between model construction and probability distribution was made as well as confidence interval of syndromic surveillance data was compared. Results The simulated syndromic surveillance data frequency distribution was fitted well among Gamma, webull, lognormal, normal, Max Extreme and logistic among which Gamma fitted best. Through examples of verification, simulation results are timely and effective. Conclusions Through Monte-Carlo simulation, we can estimate the distribution of syndromic surveillance data. It may also be applied to much more complicated parameter estimation.
出处
《口岸卫生控制》
2009年第2期48-51,共4页
Port Health Control
关键词
蒙特卡罗模拟
早期预警
Monte-Carlo simulation Early prewarning
