ArticleBMC bioinformatics2019
Attention-based recurrent neural network for influenza epidemic prediction.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Epidemiological characteristics and LSTM-based simulation of mumps incidence in Ningbo, China from 2005 to 2024.BMC public health · 2026Article
- Comparative estimation of the spread of acute diarrhea and dengue in India using statistical mathematical and deep learning models.Scientific reports · 2025Article
- A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features.BMC public health · 2025Article
- Frequency-aligned loss and spectral filtering improve long-range influenza forecasting.Frontiers in public health · 2025Article
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
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- Classification needed of the private health sector.Bulletin of the World Health Organization · 2023Article
- Dual-attention-based recurrent neural network for hand-foot-mouth disease prediction in Korea.Scientific reports · 2023Article
- Influenza surveillance with Baidu index and attention-based long short-term memory model.PloS one · 2023Article
- Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm.Frontiers in artificial intelligence · 2023Review
- Article
- Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014-2017.PloS one · 2022Article
- Prediction of hand, foot, and mouth disease epidemics in Japan using a long short-term memory approach.PloS one · 2022Article
- Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.Scientific reports · 2021Article
- Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study.JMIR medical informatics · 2021Article
- Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?Results in physics · 2021Article
- Dual Transcriptomic Analyses Unveil Host-Pathogen Interactions BetweenFrontiers in microbiology · 2021Article
- Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves.Informatics in medicine unlocked · 2021Article
- Isfahan and Covid-19: Deep spatiotemporal representation.Chaos, solitons, and fractals · 2020Article
- Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling.Chaos, solitons, and fractals · 2020Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInfluenza is an infectious respiratory disease that can cause serious public health hazard. Due to its huge threat to the society, precise real-time forecasting of influenza outbreaks is of great value to our public.
resultsIn this paper, we propose a new deep neural network structure that forecasts a real-time influenza-like illness rate (ILI%) in Guangzhou, China. Long short-term memory (LSTM) neural networks is applied to precisely forecast accurateness due to the long-term attribute and diversity of influenza epidemic data. We devise a multi-channel LSTM neural network that can draw multiple information from different types of inputs. We also add attention mechanism to improve forecasting accuracy. By using this structure, we are able to deal with relationships between multiple inputs more appropriately. Our model fully consider the information in the data set, targetedly solving practical problems of the Guangzhou influenza epidemic forecasting.
conclusionWe assess the performance of our model by comparing it with different neural network structures and other state-of-the-art methods. The experimental results indicate that our model has strong competitiveness and can provide effective real-time influenza epidemic forecasting.
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