SARS流行预测分析

Predicting SARS for Guangdong, Beijing and China's Mainland 2003 Cases

表面上突如其来的SARS本质上却有极规律的内在发展演化机制 ,遵从初始缓慢增长、加速、减速和稳定终止四个阶段总体道路 ,自然和社会生活领域众多事件演化都符合这一规律 ,因而可以运用广义的Logistic生长模型进行描述。基于先期流行的广东SARS感染病例数据 ,以及尚未结束的北京、全国 2 0 0 3年SARS流行统计数据 ,借助于最优化分析技术 ,运用广义的Logistic生长模型对该事件演化特征参量进行了辨识 ;在此基础上 ,又借助于广义生长模型的特例———Gompertz函数进行了演化过程的预测 ,并与其他生长模型结果进行了比较。研究表明 ,生长模型模拟结果均与实际数据有很好的一致性 ,可以用来预测事件的发生演化过程 ,此次SARS事件堪称生长模型的经典实例。

The study is aimed at choosing a better predictive model for the accurate description of SARS in Guangdong, Beijing and China's Mainland in 2003. Observation and general experience have shown a sigmoid type of curve consisted of four phases comparable to the phases of the SARS growth in 2003: an initial lagging period, a period of accelerating change, a period of decelerating change, and a stationary period. In order to model the SARS system, a generalized Logistic growth function has been adopted in the paper. With the officially published data, the main features of evolution of the SARS population size have been obtained using the generalized Logistic growth model by optimizing technique. Then, for getting evolutionary process prediction, several classical S models such as the Pearl, the Gompertz, Von Bertalanffy, and Richards are tested. The practice of calculations has found that the Gompertz model gives the most accurate results where fitting criteria are estimated as residual sum of squares (RSS). [