ArticleEBioMedicine2019
Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Influenza-Like Illness Forecasting Using Multisource Data: Comparative Deep Learning Study.JMIR medical informatics · 2026Article
- Multi-source machine learning for short-term influenza forecasting in Hunan Province, China: a spatiotemporal analysis using surveillance, search, meteorological and air-quality data.BMC infectious diseases · 2026Article
- Research on influenza surveillance and a prediction model based on multi-source data.BMC medical informatics and decision making · 2026Article
- Forecasting influenza activity based on internet retrieval behavior and meteorological data in four megacities of China.BMC infectious diseases · 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
- Annual global dengue dynamics are related to multi-source factors revealed by a machine learning prediction analysis.PLoS neglected tropical diseases · 2025Article
- Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research.Bioengineering (Basel, Switzerland) · 2025Review
- Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study.Journal of medical Internet research · 2025Article
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
- Article
- Deep recurrent models for forecasting infectious diseases.Frontiers in public health · 2025Article
- Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.BMC public health · 2024Article
- Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022.BMC infectious diseases · 2024Article
- Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.Journal of medical Internet research · 2023Article
- Long Short-term Memory-Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation.Journal of medical Internet research · 2023Article
- Application of big data and artificial intelligence in epidemic surveillance and containment.Intelligent medicine · 2023Review
- Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season.PloS one · 2023Article
- Influenza surveillance with Baidu index and attention-based long short-term memory model.PloS one · 2023Article
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic. However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends. There are few feasible strategies to forecast influenza activity at the local level with irregular trends.
methodsMulti-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism. SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting.
findingsILI% showed an irregular seasonal trend from 2012 to 2018 in Chongqing. Compared with three reference models, SAAIM achieved the best performance on forecasting ILI% of Chongqing with the mean absolute percentage error (MAPE) of 11·9%, 7·5%, and 11·9% during the periods of the year 2014-2016, 2017, and 2018 respectively. Among the three categories of source data, historical influenza activity contributed the most to the forecast accuracy by decreasing the MAPE by 19·6%, 43·1%, and 11·1%, followed by weather information (MAPE reduced by 3·3%, 17·1%, and 2·2%), and Internet-related public sentiment data (MAPE reduced by 1·1%, 0·9%, and 1·3%).
interpretationAccurate influenza forecast in areas with irregular seasonal influenza trends can be made by SAAIM with multi-source electronic data.
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