Openly shared datasets and reproducible analysis code from published research in epidemiology, global health, and machine learning.
💾 4 Datasets🧬 R & Python Code📦 ZIP Download🔗 Linked to Publications
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🦟 Dengue · Bangladesh · District Level
District-Level Dengue Early Warning System
Yearly and monthly dengue surveillance data across all 64 districts of Bangladesh (2017–2024), integrated with climate, socio-demographic, economic, and healthcare indicators.
Yearly surveillance datasetLoading…
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Variable dictionary (yearly)
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Monthly surveillance datasetLoading…
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Variable dictionary (monthly)
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R · Bayesian Spatiotemporal
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Python · MLP · ConvLSTM · SHAP
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Shiddik, M.A.B., Toshi, F.Z., Yesmin, S. & Rahman, M.S. (2026). District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning. Tropical Medicine and Infectious Disease, 11(3), p.73. ↗ DOI
🦟 Dengue · Bangladesh · National
Dengue AI & Early Warning System
Monthly national dengue data (2000–2024) with climatic, sociodemographic, and landscape variables used to train XGBoost, LightGBM, and causal inference models.
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Python · XGBoost · LightGBM · SHAP · LIME · CAI
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R · Early Warning System
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Rahman, M.S. & Shiddik, M.A.B. (2025). Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers. Global Epidemiology, 10, 100210. ↗ DOI
Rahman, S. & Shiddik, A.B. (2025). Reflections on explainable artificial intelligence for predicting dengue outbreaks in Bangladesh. Global Epidemiology, p.100230. ↗ DOI
Rahman, M.S., Amrin, M. & Shiddik, M.A. (2025). Dengue Early Warning System using interpretable tree-based machine learning models. Health Science Reports, 8(5), e70726. ↗ DOI
Rahman, M.S. & Shiddik, M.A.B. (2025). Data-driven dengue prevention strategies using explainable AI and causal inference. International Journal of Statistical Sciences, 25(2), 101–113. ↗ DOI
🌍 Tuberculosis · Global · 194 Countries
Global Tuberculosis Project
Global TB incidence and mortality data across 194 countries (2000–2022), with socioeconomic, environmental, and healthcare indicators used in XGBoost + XAI analysis.
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R · XGBoost · XAI · Spatial Analysis
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Rahman, M.S. & Shiddik, A.B. (2025). Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally. Scientific Reports. ↗ DOI
🌍 Malaria · Global · 106 Countries
Global Malaria Project
Malaria incidence and mortality data across 106 countries (2000–2022), incorporating sanitation, electricity, population, and health system variables for XGBoost and spatial hotspot analysis.
Global malaria datasetLoading…
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R · XGBoost · Getis-Ord Gi* · Causal AI
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Rahman, M.S. & Shiddik, M.A.B. (2025). Unraveling global malaria incidence and mortality using machine learning and artificial intelligence–driven spatial analysis. Scientific Reports, 15(1), p.28334. ↗ DOI