Vector-borne disease epidemics do not occur in a vacuum. They are shaped by the interplay of climate anomalies, urbanization density, and regional socioeconomic resilience.
In this research initiative, we developed a predictive time-series framework for forecasting dengue fever outbreaks.
- Research Archive: https://kholipha-ahmmad-al-amin.me/#research
- Projects Catalog: https://kholipha-ahmmad-al-amin.me/#projects
- Portfolio: https://kholipha-ahmmad-al-amin.me/
Data Pipeline and Predictive Modeling
- Multi-Modal Data Integration: Combined historical meteorological records (ambient temperature, rainfall volume, relative humidity) with demographic indicators (population density, civic water drainage access).
- Lagged Feature Engineering: Mosquito breeding cycles introduce non-linear biological lags (typically 2 to 4 weeks between high rainfall and clinical infection surges). We engineered rolling temporal windows to capture these lagged effects.
- Time-Series Forecasting: Evaluated auto-regressive models alongside tree-based ensembles to generate actionable warning forecasts for regional healthcare planners.
Explore the complete dataset breakdown, code repositories, and research findings at https://kholipha-ahmmad-al-amin.me/#research.
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