基于机器学习的新冠肺炎疫情趋势预测方法  

Prediction method of the pandemic trend of COVID-19 based on machine learning

作　　者：任建强 崔亚鹏 倪顺江 REN Jianqiang;CUI Yapeng;NI Shunjiang(Institute of Public Safety Research,Department of Engineering Physics,Tsinghua University,Beijing 100084,China;Beijing Key Laboratory of City Integrated Emergency Response Science,Beijing 100084,China)

机构地区：[1]清华大学工程物理系,公共安全研究院,北京100084 [2]城市综合应急科学北京市重点实验室,北京100084

出　　处：《清华大学学报（自然科学版）》2023年第6期1003-1011,共9页Journal of Tsinghua University(Science and Technology)

基　　金：国家自然科学基金面上项目(72174104)。

摘　　要：防控措施对传染病的传播过程有重要作用,因此在预测新型冠状病毒肺炎疫情未来发展趋势时必须要考虑防控措施的影响。该文提出了基于机器学习的新冠肺炎疫情三步预测模型,将神经网络、随机森林、长短期记忆网络和序列到序列等机器学习算法引入到新冠肺炎传染病疫情预测中。与前人的预测模型相比,所提出的模型考虑了新冠肺炎疫情发展过程中防控措施的变化情况,可以使用检测数据预测未来的确诊人数和实际感染规模。研究结果表明:预测结果与实际数据基本一致,模型具有较高的可靠性。该预测方法可以使政府管理部门更准确地了解新冠肺炎疫情的实际发展态势,帮助管理者更有效地分配医疗资源,为新冠肺炎疫情防控提供决策参考。[Objective]To estimate and predict the actual infection scale of COVID-19 in a population,a COVID-19 pandemic trend prediction method based on machine learning is proposed.This method uses detection data to predict the development trend of the pandemic and can implicitly consider the impact of prevention and control measures.Additionally,this method can predict the number of confirmed cases in the future and estimate the actual infection scale of COVID-19.[Methods]In this paper,a three-step prediction model based on machine learning(TSPM-ML)is proposed.Machine learning algorithms,such as neural networks,random forest,long short-term memory(LSTM),and sequence to sequence(seq2seq),are introduced into the prediction of the COVID-19 development situation,and the detection data are used to predict the number of people diagnosed and the actual scale of the infection in the future.The TSPM-ML includes three steps:(1)predicting the actual infection scale of COVID-19 based on the detection data,(2)predicting the future development trend of the actual infection scale based on the predicted results of the first step,and(3)predicting the number of people diagnosed in the future based on the actual infection scale obtained in the second step.The TSPM-ML is used to predict the actual pandemic situation in Germany,France,South Korea,the United States,Russia,and Finland.[Results]The largest prediction error is in the United States,with a forecast error of 23.71 per million people,while South Korea has the smallest prediction error of 0.63 per million people.Overall,the prediction results of the TSPM-ML are consistent with the simulation and actual data,and the reliability of the model is verified.[Conclusions]The predicted results are consistent with the actual data,and the TSPM-ML is highly reliable.The prediction results can enable government management departments to more accurately understand the actual development trend of COVID-19 and allocate medical resources more effectively,and provide decision support for COVID-19 pre

关 键 词：机器学习 防控措施 疫情趋势预测 突发公共卫生事件 

分 类 号：X959[环境科学与工程—安全科学]