COVID-19疫情传播建模分析

当大规模传染性疾病威胁到人类安全时,有效预测其传播趋势是减少疾病对人们伤亡和财产损失的重要措施.该文使用3种方法对2019年新冠肺炎的传播趋势进行建模分析,并以北京市、云南省、江西省为代表预测其传播趋势和确诊人数的峰值.实验结果表明:当使用高阶函数拟合对前期的发展趋势进行建模时,其趋势与真实的疫情最吻合,趋势拟合效果最佳,峰值误差最小.当使用Logistic增长曲线中的S型曲线对前期的发展趋势进行建模时,其趋势与真实的疫情基本吻合,趋势拟合效果次佳,峰值误差次佳.当使用基于动力学传播模型中的SIR模型对前期的发展趋势进行建模时,其趋势与真实的疫情基本吻合,趋势拟合效果在3种方法中最差,峰值误差也最大.

The Prediction of Epidemic Trend of COVID-19

When large-scale infectious diseases threaten human beings,effectively predicting the transmission trend is an important measure to reduce people's casualties or property losses.The novel coronavirus pneumonia epidemic trend in 2019 is modeled and analyzed by three methods,and the trend of transmission and number of confirmed cases are predicted by Beijing,Yunnan and Jiangxi.The results of experiments show that using high-order function fitting to model the development trend in the early stage,the trend is most consistent with the real epidemic situation,the trend fitting effect is the best and the peak error is the smallest.The S-shaped curve in the logistic growth curve is used to model the development trend in the early stage.The trend is basically consistent with the real epidemic situation,the trend fitting effect is the second best,and the peak error is the second best.The SIR model based on the dynamic propagation model is used to model the development trend in the early stage.The trend is basically consistent with the real epidemic situation.The trend fitting effect is the worst among the three methods,and the peak error is also the largest.