全国新型冠状病毒肺炎发病情况室模型分析及疫情进展短期预测

SIR model analysis and short-term prediction of epidemic progress of corona virus disease 2019 in China

目的预测新型冠状病毒肺炎疫情短期进展,评估不同省之间的群体防控度。方法根据流行病传播动力学原理,建立室模型拟合全国及5省1月份疫情数据,获得参数后预测短期疫情进展,计算群体防控度。结果室模型拟合了全国及5个省份1月份报告发病情况,模型具有统计学意义(P<0.05)。根据模型参数计算的预测病例数对实际病例数的解释度全国达92.64%,浙江省最低为72.86%。群体防控度全国平均水平较高,与湖北省接近,其他4省较低。根据模型参数预测未来2月上旬全国及5省每日新增病例数仍将处于上升期。结论室模型较好地拟合了疫情初期报告发病数据,可用于疫情进展的短期预测。

Objective To predict the short-term progress of corona virus disease 2019(COVID-19)and evaluate the degree of population control among different provinces. Methods A SIR model was established to fit the epidemic data of the whole country and 5 provinces in January according to the principle of epidemic dynamics,After the parameters were obtained,the short-term epidemic progress was predicted and the population prevention and control degree was calculated.Results The SIR model fitted the incidence reported from the whole country and 5 provinces in January,and the model was statistically significant(P<0.05). The degree of explanation of the predicted number of cases calculated according to the model parameters to the actual number of cases was 92.64% in China,and the lowest was 72.86% in Zhejiang province. The national average level of population prevention and control was high,which was close to that of Hubei province,while that of Guangdong province was low. According to the model parameters,it predicted that the daily number of new cases in the country and provinces in the earlier days of February was still on the rise. Conclusion The SIR model could well fit the reported incidence data at the initial stage of the epidemic and could be used to predict the short-term progress of the epidemic.