ABSTRACT

Influenza causes repeat epidemics and huge loss of lives and properties. To predict influenza epidemics, we proposed an infectious disease dynamic prediction model with control variables (SEIR-CV), which considers the characteristics of the influenza epidemic transmission, seasonal impacts, and the intensity changes of control measures over time. The critical parameters of the model were inversed using an adjoint method. When using the surveillance data of the past 15 weeks to invert the parameters, the epidemic in the next 3 weeks in the United States can be accurately predicted. In addition, roll predictions from 26 September 2016 to 27 September 2018 were implemented. The correlation coefficient between the predicted values and the surveillance values was greater than 0.975, and the overall relative error of the predictions was less than 10%. These good model performances demonstrated the practicability and feasibility of SEIR-CV for influenza and corresponding infectious disease prediction.

摘要

流感导致反复流行和巨大的生命财产损失。为了预测流感的流行，我们提出了一种带有控制变量的传染病动态预测模型（SEIR-CV），该模型考虑了流感流行的传播特征、季节性影响以及控制措施的强度随时间的变化。使用伴随方法对模型的关键参数进行反演。用过去15周的监测数据反演参数，可以准确预测美国未来3周的疫情。此外，实施了 2016 年 9 月 26 日至 2018 年 9 月 27 日的滚动预测。预测值与监测值的相关系数大于0.975，预测值总体相对误差小于10%。