基于生态模型的新型冠状病毒肺炎疫情分析

Analysis of COVID-19 epidemic based on ecological model

为了研究和解释新型冠状病毒肺炎(COVID-19)疫情的变化趋势,本研究利用生态增长模型对实际数据进行拟合分析.收集COVID-19疫情的相关数据,构建确诊患者例数、治愈患者例数和死亡患者例数3项数值的logistic模型,并预测世界6个区域内的疫情发展趋势.截至2020年4月18日,logistic增长曲线的拟合数值与实际观测数据差异较小,且曲线的决定系数(R2)均>0.9970.全国COVID-19疫情的累计确诊患者例数、累计治愈患者例数和累计死亡患者例数变化趋势基本符合logistic增长曲线;全球COVID-19疫情分析显示,世界各地区的感染、暴发有时间差异,通过对现有数据进行曲线拟合,预测不同区域在具有不同拐点时的最终确诊患者例数.本研究证实logistic曲线可以模拟COVID-19疫情的变化趋势,也可以对疫情做出粗略预测,为未来的疫情防治工作提供一些帮助.

The trend of novel coronavirus pneumonia(COVID-19)epidemic was studied and explained by fitting the actual data with the ecological growth model in this paper.This study collected the data of COVID-19 epidemic situation and constructed the logistic model of three numerical values:the number of confirmed patients,cured patients and dead patients.The logistic growth curve was used to predict the trend of epidemic situation in six regions of the world.As of April 18,2020,the fitting value of logistic model are slightly different from the actual observation data,and the determination coefficient(R2)of the curves are greater than 0.9970.The trend of the cumulative number of confirmed cases,cured cases and dead cases of COVID-19 in China basically conform to the logistic growth curve.The analysis of global epidemic shows that the time of infection and outbreak are different in the regions.The number of confirmed cases in six regions with different inflexion points are inferred according to the curve fitting of existing data.This study confirmed that the logistic growth curve can simulate the trend of COVID-19,it can also make a rough forecast of the epidemic situation to provide data for the epidemic prevention and control work.