ArticleBMC public health2024
The prediction of influenza-like illness using national influenza surveillance data and Baidu query data.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Stage-aware deep learning with adversarial data synthesis enables prospective season-scale A/H1N1 influenza forecasting in China.Infectious Disease Modelling · 2027Article
- Multisource Early Warning Framework Integrating Regional Pathogen Surveillance and Baidu Index Data for Respiratory Infectious Disease Prediction in Beijing: Retrospective Time-Series Modeling Study.JMIR formative research · 2026Article
- Wavelet analysis and time series forecasting of malaria incidence across different transmission settings in Nigeria.BMC infectious diseases · 2026Article
- Influenza-Like Illness Forecasting Using Multisource Data: Comparative Deep Learning Study.JMIR medical informatics · 2026Article
- Toward Drift-Aware Multimodal Early Warning for Acute Exacerbations of Chronic Obstructive Pulmonary Disease.JMIR medical informatics · 2026Article
- Research on influenza surveillance and a prediction model based on multi-source data.BMC medical informatics and decision making · 2026Article
- LSTCNet: timely prediction of influenza in China using Baidu index and LSTM-TCN hybrid network.BMC public health · 2026Article
- Generalized additive model integrating multi-source data for short-term influenza forecasting in Shenzhen, China (2023-2025).Frontiers in public health · 2026Article
- Dynamic ensemble deep learning with multi-source data for robust influenza forecasting in Yangzhou.BMC public health · 2025Article
- Prediction of influenza-like illness incidence using meteorological factors in Kunming : deep learning model study.BMC public health · 2025Article
- A Deep Learning Framework for Using Search Engine Data to Predict Influenza-Like Illness and Distinguish Epidemic and Nonepidemic Seasons: Multifeature Time Series Analysis.Journal of medical Internet research · 2025Article
- Deep learning models for hepatitis E incidence prediction leveraging Baidu index.BMC public health · 2024Article
- Artificial intelligence-based prediction of pathogen emergence and evolution in the world of synthetic biology.Microbial biotechnology · 2024Review
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Abstract
backgroundSeasonal influenza and other respiratory tract infections are serious public health problems that need to be further addressed and investigated. Internet search data are recognized as a valuable source for forecasting influenza or other respiratory tract infection epidemics. However, the selection of internet search data and the application of forecasting methods are important for improving forecasting accuracy. The aim of the present study was to forecast influenza epidemics based on the long short-term memory neural network (LSTM) method, Baidu search index data, and the influenza-like-illness (ILI) rate.
methodsThe official weekly ILI% data for northern and southern mainland China were obtained from the Chinese Influenza Center from 2018 to 2021. Based on the Baidu Index, search indices related to influenza infection over the corresponding time period were obtained. Pearson correlation analysis was performed to explore the association between influenza-related search queries and the ILI% of southern and northern mainland China. The LSTM model was used to forecast the influenza epidemic within the same week and at lags of 1-4 weeks. The model performance was assessed by evaluation metrics, including the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE).
resultsIn total, 24 search queries in northern mainland China and 7 search queries in southern mainland China were found to be correlated and were used to construct the LSTM model, which included the same week and a lag of 1-4 weeks. The LSTM model showed that ILI% + mask with one lag week and ILI% + influenza name were good prediction modules, with reduced RMSE predictions of 16.75% and 4.20%, respectively, compared with the estimated ILI% for northern and southern mainland China.
conclusionsThe results illuminate the feasibility of using an internet search index as a complementary data source for influenza forecasting and the efficiency of using the LSTM model to forecast influenza epidemics.
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