基于改进的SEIR+CAQ传染病动力学模型进行新型冠状病毒肺炎疫情趋势分析

目的 拟合并预测新型冠状病毒肺炎(COVID-19)疫情的发展趋势,为疫情防控提供科学依据。方法 基于SEIR动力学模型,考虑COVID-19的传播机制、感染谱、隔离措施等,建立SEIR^+CAQ传播动力学模型。基于官方公布的每日确诊病例数进行建模,利用2020年1月20日至2月7日的报告疫情数据进行拟合。采用2月8-12日的数据评估预测效果,并进行疫情预测。结果 SEIR^+CAQ模型对全国(湖北省除外)和湖北省(武汉市除外)的累计确诊病例数的过去10日拟合偏差<5%;未来5日预测偏差<10%,略有高估。全国(湖北省除外)和湖北省(武汉市除外)的每日新增确诊病例数已于2月1-2日达峰值;武汉市亦已于2月9日达到高峰。在当前防控措施不变的情况下,截至2月29日,预计全国累计确诊病例将达80 417例。预测结果尚未包含临床诊断病例。结论 SEIR^+CAQ模型可用于COVID-19疫情趋势预测,为疫情防控决策和效果评价提供参考。

Fitting and forecasting the trend of COVID-19 by SEIR+CAQ dynamic model

Objectives Fitting and forecasting the trend of COVID-19 epidemics. Methods Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR^+CAQ dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting. Results According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR^+CAQ model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory-confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively. Conclusions The proposed SEIR^+CAQ dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.