ArticleInternational journal of environmental research and public health2020
Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- A systematic review of dengue outbreak prediction models: Current scenario and future directions.PLoS neglected tropical diseases · 2023Pooled it
- Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.Frontiers in public health · 2022Pooled it
- Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Dengue fever prediction based on meteorological features and deep learning models.Infectious Disease Modelling · 2026Article
- Climate-Driven Advanced Machine Learning Approach for Dengue Incidence Forecasting in Bangladesh.Health science reports · 2026Article
- Modelling climatic and temporal dynamics of dengue transmission in Bangladesh using deep learning models.PLOS global public health · 2026Article
- Forecasting Acute Hemorrhagic Conjunctivitis Incidence in Henan, China: A Comparative Study of Seasonal Autoregressive Fractionally Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models.Risk management and healthcare policy · 2026Article
- Global bone cancer incidence and death rate analysis at 40 years.Discover oncology · 2025Article
- Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy.BMC infectious diseases · 2025Article
- Integrating meteorological data and hybrid intelligent models for dengue fever prediction.BMC public health · 2025Article
- Spatial Distribution Analysis and Comparative Forecasting of Dengue Resurgence in the Philippines (2025-2027): A Nationwide Study.Transboundary and emerging diseases · 2025Article
- Forecasting dengue in Bangladesh using meteorological variables with a novel feature selection approach.Scientific reports · 2024Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- Leveraging Climate Data for Dengue Forecasting in Ba Ria Vung Tau Province, Vietnam: An Advanced Machine Learning Approach.Tropical medicine and infectious disease · 2024Article
- Forecasting West Nile Virus With Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data.GeoHealth · 2024Article
- Building a pathway to One Health surveillance and response in Asian countries.Science in One Health · 2024Review
- Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model.Scientific reports · 2023Article
- Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning.Scientific reports · 2023Article
- PICTUREE-Aedes: A Web Application for Dengue Data Visualization and Case Prediction.Pathogens (Basel, Switzerland) · 2023Article
- Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dengue fever (DF) is one of the most rapidly spreading diseases in the world, and accurate forecasts of dengue in a timely manner might help local government implement effective control measures. To obtain the accurate forecasting of DF cases, it is crucial to model the long-term dependency in time series data, which is difficult for a typical machine learning method. This study aimed to develop a timely accurate forecasting model of dengue based on long short-term memory (LSTM) recurrent neural networks while only considering monthly dengue cases and climate factors. The performance of LSTM models was compared with the other previously published models when predicting DF cases one month into the future. Our results showed that the LSTM model reduced the average the root mean squared error (RMSE) of the predictions by 12.99% to 24.91% and reduced the average RMSE of the predictions in the outbreak period by 15.09% to 26.82% as compared with other candidate models. The LSTM model achieved superior performance in predicting dengue cases as compared with other previously published forecasting models. Moreover, transfer learning (TL) can improve the generalization ability of the model in areas with fewer dengue incidences. The findings provide a more precise forecasting dengue model and could be used for other dengue-like infectious diseases.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.