Logistic与SEIR结合模型预测新型冠状病毒肺炎传播规律

Prediction of COVID-19 transmission law by combining Logistic and SEIR model

研究新型冠状病毒肺炎(COVID-19)传播方式规律及趋势有助于有效遏制其蔓延.介绍了一些常用的传染病预测模型,提出了Logistic_SEIR模型,既克服了Logistic模型不能预测现有确诊人数的缺点又克服了SEIR模型调参太多的缺点,并通过实验证明了所提出模型的实现和预测的优越性.同时进一步研究了Logistic_SEIR模型需要调试的参数取不同的初始值对预测结果的影响,并通过加权误差来量化分析预测效果.最后指出未来进一步的研究方向.

To study the law and trend of coronavirus disease 2019(COVID-19)transmission mode is helpful for effectively curbing its spread.In this article,we first introduce some commonly used prediction models of infectious diseases,and then introduce the Logistic_SEIR model.This model not only overcomes shortcomings of the Logistic model that cannot predict the number of confirmed patients,but also overcomes those of the SEIR model in which plenty of parameters exist.In experiments,we prove the superiority of the realization and prediction of the model proposed in our study.At the same time,we further study the influence of different initial values of the parameters that need to be debugged in the Logistic_SEIR model on the prediction results,and quantifies the prediction effect through the weighted error.Finally,in the summary,we point out the future research directions.