ArticleHealth science reports2026
Global Prediction of Dengue Incidence Using an Explainable Artificial Intelligence-Driven ConvLSTM Integrating Environmental, Health, and Socio-Economic Determinants.
Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Explainable Machine Learning for Predicting Global Malaria Incidence From 2000 to 2022 With Climatic and Extreme Weather Conditions Across 78 Endemic Countries: A Retrospective Ecological Study.Health science reports · 2026Article
- Dengue Surge in Bangladesh: Exploring the Role of Climate Factors-A Narrative Review With Ecological Analysis.Health science reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Dengue fever is a rapidly expanding vector-borne disease that poses significant global epidemiological and public health challenges. Accurate and interpretable forecasting is essential for timely interventions, yet most models overlook spatiotemporal, sex-specific, and country-level heterogeneity in disease dynamics. This study aimed to develop a robust explainable AI (XAI) framework to predict dengue incidence globally and identify key environmental, health system, and socio-economic drivers. Methods: A Convolutional Long Short-Term Memory (ConvLSTM) network was applied to predict dengue incidence across 118 countries from 2000 to 2021. The data set included total, male, and female dengue incodence alongside 20 climatic, environmental, health system, and socio-economic predictors. The model was trained using data from 2000 to 2018 and tested on 2019-2021. Model performance was evaluated using RMSE, MAE, Results: ConvLSTM achieved the best predictive performance ( Conclusion: By integrating SHAP, IG, and LRP, the ConvLSTM-XAI framework provides transparent, sex-aware, and country-specific dengue forecasts. The results support targeted, climate-resilient, and equitable dengue control strategies.
Indexed as
Identifiers
What Socratic 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 Socratic graph.