Koge Daiki, Wagatsuma Keita
Division of Bioinformatics, Department of Information Science, Graduate School of Science and Technology, Niigata University, Niigata, Japan.
Institute for Research Administration, Niigata University, Niigata, Japan.
Front Public Health. 2025 Aug 22;13:1618508. doi: 10.3389/fpubh.2025.1618508. eCollection 2025.
Influenza remains a significant public health challenge worldwide, necessitating robust forecasting models to facilitate timely interventions and resource allocation. The aim of this study was to develop a long short-term memory (LSTM)-based short-term forecasting model to accurately predict weekly influenza case counts in Tokyo, Japan.
By using weekly time-series data on influenza incidence in Tokyo from 2000 to 2019, along with meteorological variables, we developed four distinct models to evaluate the impact of the external variables of mean temperature, relative humidity, and national public holidays. After model training, we assessed the predictive performance on an independent test dataset, using mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and Pearson's correlation coefficient.
During the study period, 1,445,944 influenza cases were analyzed. The model incorporating all three external variables demonstrated superior predictive accuracy, with an MSE of 3,646,084, RMSE of 1,909, MAE of 849, and Pearson's correlation coefficient of 0.924. These findings underscore the substantial contribution of these external factors to improving the prediction performance.
This study highlighted the efficacy of LSTM-based models for short-term influenza forecasting and reinforces the importance of integrating meteorological variables and national public holidays into predictive frameworks. Our optimal model provided more precise forecasts of influenza activity in Tokyo, Japan.
流感仍然是全球重大的公共卫生挑战，需要强大的预测模型来促进及时干预和资源分配。本研究的目的是开发一种基于长短期记忆（LSTM）的短期预测模型，以准确预测日本东京每周的流感病例数。
利用2000年至2019年东京流感发病率的每周时间序列数据以及气象变量，我们开发了四个不同的模型，以评估平均温度、相对湿度和国家公共假日等外部变量的影响。模型训练后，我们使用均方误差（MSE）、均方根误差（RMSE）、平均绝对误差（MAE）和皮尔逊相关系数，在独立测试数据集上评估预测性能。
在研究期间，共分析了1,445,944例流感病例。纳入所有三个外部变量的模型显示出卓越的预测准确性，MSE为3,646,084，RMSE为1,909，MAE为849，皮尔逊相关系数为0.924。这些发现强调了这些外部因素对提高预测性能的重大贡献。
本研究突出了基于LSTM的模型在短期流感预测中的有效性，并强化了将气象变量和国家公共假日纳入预测框架的重要性。我们的最优模型为日本东京的流感活动提供了更精确的预测。
