ArticleBMC infectious diseases2018
Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.
Article in BMC infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
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43 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- Assessing the association between floods and dengue incidence in Vietnam: A time series study.Environmental epidemiology (Philadelphia, Pa.) · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Bridging ensemble model and public health practice: an approach for refining understanding of seasonal dengue transmission patterns in Bangladesh.BMC infectious diseases · 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
- Impact of projected climate and socioeconomic scenarios on state-wise annual dengue incidence in India using ensemble models.PLoS neglected tropical diseases · 2026Article
- Unravelling the progression of the zebrafish primary body axis with reconstructed spatiotemporal transcriptomics.Genome biology · 2026Article
- Forecasting and Early Warning Systems for Dengue Outbreaks: Updated Narrative Review.Revista da Sociedade Brasileira de Medicina Tropical · 2026Review
- Investigating linkages between human movement and meteorological variables on dengue outbreaks in the Pacific Islands.PLoS neglected tropical diseases · 2025Article
- Effects of El Niño and the Positive Indian Ocean Dipole (+IOD) on Health, Food Security, Economics, and Conflict in Low- and Middle-Income Countries in the Indo-Pacific: A Systematic Review.Campbell systematic reviews · 2025Review
- Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques.Parasites & vectors · 2025Article
- Spatial Distribution Analysis and Comparative Forecasting of Dengue Resurgence in the Philippines (2025-2027): A Nationwide Study.Transboundary and emerging diseases · 2025Article
- Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction.PLoS neglected tropical diseases · 2024Article
- Improving dengue fever predictions in Taiwan based on feature selection and random forests.BMC infectious diseases · 2024Article
- Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia.Pathogens (Basel, Switzerland) · 2024Article
- The impact of anthropogenic climate change on pediatric viral diseases.Pediatric research · 2024Review
- Scalable Lightweight IoT-Based Smart Weather Measurement System.Sensors (Basel, Switzerland) · 2023Article
- DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2023Article
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Abstract
backgroundSeveral studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting.
methodsDengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 - December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated.
resultsAmong the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model.
conclusionThe study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.
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