基于改进自适应最优分割法的风险预警区间模型研究——针对重大突发公共卫生事件

重大突发公共卫生事件，譬如新型冠状病毒疫情，严重危害着世界各国人民的生命安全，风险预警是构建重大突发公共卫生事件风险预警管控体系的关键所在，而其风险预警区间的精准确定是关乎预警等级的关键问题。基于自适应最优分割模型，引入熵值法计算各指标权重，采用多种函数拟合识别函数特征，构建了改进的自适应最优分割模型，定量科学划分了重大突发公共卫生事件风险预警区间。通过结合实际案例，应用Matlab软件进行仿真，验证了预警区间的吻合度，为构建重大突发公共卫生事件风险预警防控提供了理论参考。

Study on the Risk Early Warning Interval Model Based on Improved Adaptive Optimal Partition Method——Large-scale Public Health Emergency

Large-scale public health emergency, such as COVID-19, has severely endangered the safety of people around the world. Relevant emergency management system needs to be improved, among which risk early warning is the key issue. How to quantitatively estimate the risk early warning thresholds and partition risk early warning intervals is studied. In this paper, improved adaptive optimal partition model based on entropy method is introduced to fix this problem. It is found that the trend of public health emergency changes dynamically, while traditional partition model can not identify the changes of data characteristics in different epidemic periods. The model in this paper uses eight kinds of fitting functions to reduce the sum of squares of deviations when partitioning, so as to more accurately identify the development trends of epidemic in different periods. In addition, considering the diversity of epidemic indicators, five kinds of epidemic indicators are considered and the entropy method is used to assign the weight of each indicator, which can better avoid possible deviations caused by manual selection. In order to verify the effectiveness of the model, the data of daily COVID-19 cases worldwide from January 20, 2020 to March 31, 2020, are used and the simulation results of risk early warning intervals are given. It is found that our partition results obtained by improved adaptive optimal partition model are basically consistent with the WHO statements about the epidemic development stages. What’s more, comparing with traditional partition model, the results in this paper are more accurate and the risk early warning thresholds are more advanced. Therefore, the study has guiding value for enhancing the accuracy of risk early warning of public health emergencies, and provides a theoretical reference for the construction of emergency management system.