ArticlePLoS neglected tropical diseases2017
Developing a dengue forecast model using machine learning: A case study in China.
Article in PLoS neglected tropical diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
87 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic.Journal of epidemiology and global health · 2024Pooled it
- Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review.International journal of biometeorology · 2024Pooled it
- A systematic review of dengue outbreak prediction models: Current scenario and future directions.PLoS neglected tropical diseases · 2023Pooled it
- Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions.Frontiers in public health · 2022Pooled it
- Developing machine learning models to forecast mosquito abundance for climate change impacts and early warning system.One health (Amsterdam, Netherlands) · 2026Article
- Tree-Based Machine Learning for Diagnostic Classification of Dengue Fever Using Routine Hematological Parameters: A Secondary Analysis of a Publicly Available Dataset.Diagnostics (Basel, Switzerland) · 2026Article
- Comparative evaluation of machine learning strategies for short-term dengue forecasting in Brazilian capital municipalities.International journal of biometeorology · 2026Article
- Artificial intelligence research in journals indexed in the web of science "infectious diseases" category: a bibliometric analysis, 2016-2025.Infection · 2026Article
- Comparative Performance of Mechanistic, Statistical, and Hybrid Models of Forecasting Dengue Fever Incidence in Somalia. A Retrospective Time Series Analysis.Health science reports · 2026Article
- Rethinking Dengue Preparedness in the Era of Climate Change, Urbanisation, and Digital Health: A Structured Narrative Review.Medicina (Kaunas, Lithuania) · 2026Review
- Predictive Modeling of Enterovirus Hospital Burden Using Machine Learning and Age-Specific Surveillance Data: Operational Forecasting in Taiwan During the Postpandemic Era.JMIR formative research · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- AI-based prediction of dengue incidence using climatic, environmental, and socio-demographic factors: an ensemble random forest approach with agile system development.BMC infectious diseases · 2026Article
- DengueGNN: Graph-based deep learning for modeling disease spread dynamics and prediction.Scientific reports · 2026Article
- Impact of projected climate and socioeconomic scenarios on state-wise annual dengue incidence in India using ensemble models.PLoS neglected tropical diseases · 2026Article
- A novel hybrid model for emotion detection in text through sequential and transformer-based approaches: LSTM enhanced RoBERTa (LER).Scientific reports · 2026Article
- Modelling climatic and temporal dynamics of dengue transmission in Bangladesh using deep learning models.PLOS global public health · 2026Article
- Multiple change point analysis and time series forecast of brucellosis reports in Xinjiang, China from 2010 to 2024.BMC public health · 2025Article
- Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls.Cureus · 2025Review
27 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. METHODOLOGY/PRINCIPAL
findingsWeekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011-2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. CONCLUSION AND SIGNIFICANCE: The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.