计算机与现代化Issue(9)：38-44,7.DOI:10.3969/j.issn.1006-2475.2024.09.007
基于LSTM-SIR-EAKF的流感样病例预测
Influenza-like Illness Prediction Based on LSTM-SIR-EAKF
摘要
Abstract
The paper explores the combination method based on machine learning model and infectious disease model to predict influenza trend,and provides advice for medical institutions to take preventive measures.To precisely capture the temporal fea-tures of influenza-like illness(ILI),this paper proposes a combined prediction model(LSTM-SIR-EAKF)based on long and short-term memory(LSTM)neural networks,Suceptible-Infected-Recovered(SIR)model,and Ensemble Adjustment Kalman Filter(EAKF).Firstly,the model of LSTM is employed to learn the temporal relationship between ILI.Then,SIR model is used to simulate the transmission process of ILI.Finally,EAKF correctes the anticipated values of ILI from SIR model to obtain the fi-nal prediction values of ILI.The experimental results show that through the prediction of ILI in three time periods,the goodness of fit(R2)proposed by the LSTM-SIR-EAKF model are 0.996,0.991 and 0.995,respectively,and the evaluation indicators of the prediction results are better than the comparison model.LSTM-SIR-EAKF model makes long-term prediction of influenza in time through long and short term memory network,and the infectious disease model simulates the changes of influenza population in space,effectively improving the prediction effect.关键词
流感预测/长短期记忆网络/易感-感染-康复模型/集合调整卡尔曼滤波/时间序列Key words
ILI prediction/LSTM/SIR/ensemble adjustment Kalman filter/time series分类
信息技术与安全科学引用本文复制引用
李进,魏艳龙,薛红新,梁海坚..基于LSTM-SIR-EAKF的流感样病例预测[J].计算机与现代化,2024,(9):38-44,7.基金项目
国家自然科学基金资助项目(62106238) （62106238）
山西省基础研究计划项目(202203021212185) （202203021212185）
山西省高等学校科技创新项目(2020L0283) （2020L0283）
