ArticleBMC bioinformatics2016
Analysis of significant factors for dengue fever incidence prediction.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 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
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 71 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
- Bridging ensemble model and public health practice: an approach for refining understanding of seasonal dengue transmission patterns in Bangladesh.BMC infectious diseases · 2026Article
- Innovative application of a traffic-prediction spatio-temporal graph convolutional network for dengue disease forecasting.Scientific reports · 2026Article
- Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy.BMC infectious diseases · 2025Article
- Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia.Tropical medicine and health · 2025Article
- Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia.BMC public health · 2025Article
- Forecasting dengue in Bangladesh using meteorological variables with a novel feature selection approach.Scientific reports · 2024Article
- Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review.BMC public health · 2023Article
- Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model.Bioengineering (Basel, Switzerland) · 2023Article
- Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction.PLoS neglected tropical diseases · 2023Article
- Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree.Health information science and systems · 2022Article
- A Systematic Review of Applications of Machine Learning and Other Soft Computing Techniques for the Diagnosis of Tropical Diseases.Tropical medicine and infectious disease · 2022Review
- Forecasting the incidence of dengue in Bangladesh-Application of time series model.Health science reports · 2022Article
- Deep learning models for forecasting dengue fever based on climate data in Vietnam.PLoS neglected tropical diseases · 2022Article
- A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation in Liaoning Province, China.BMC infectious diseases · 2022Article
- Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review.Risk management and healthcare policy · 2022Review
- Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method.BMC bioinformatics · 2021Article
- Low yield but high levels of multidrug resistance in urinary tract infections in a tertiary hospital, Nepal.Public health action · 2021Article
- Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions.PLoS neglected tropical diseases · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak.
resultsThe predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention.
conclusionsThe present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.
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.