基于COVID-19的“大流行”病定量界定研究

Quantitative Definition of “Pandemic” Disease Based on COVID-19

摘要: 本文基于Logistic预测模型对新型冠状病毒肺炎(COVID-19)的传染率以及最大感染人数进行了预测，计算出感染率与非大流行传染病的感染率，并进行比较，从而对大流行传染病进行定量界定。基于中国2020年1月22日~2020年4月15日疫情数据作为实验数据，建立防疫政策函数、经济函数、医疗函数，以及Logistic预测模型对COVID-19传染率及最大感染人数进行了预测，并且基于预测结果计算出感染率与非大流行传染病的感染率并作了比较，量化了大流行病的界定。运用Logistic预测模型对COVID-19的传染率进行预测，其结果具有很好的拟合性，对比分析可量化出“流行”与“大流行”病的界限。

Abstract: This paper predicts the infection rate of new coronary pneumonia based on the Logistic prediction model, and compares the prediction result with the infection rate of non-pandemic infectious diseases, so as to quantify the definition of pandemic infectious diseases. Based on China’s epidemic data from January 22, 2020 to April 15, 2020, as experimental data, the epidemic prevention policy function, economic function, medical function, and Logistic prediction model are established to predict the infection rate of new coronary pneumonia, and the infection rate of pandemic infectious diseases is compared to quantify the definition of pandemic. The Logistic prediction model has a good fit for predicting the infection rate of new coronary pneumonia. Comparative analysis can quantify the boundary between “epidemic” and “pandemic”.