ASCLEPIUS: AN AI-ENHANCED COMPUTATIONAL FRAMEWORK FOR REAL-TIME DENGUE OUTBREAK GEOSPATIAL ANALYSIS USING MATHEMATICAL MODELLING AND MACHINE LEARNING FORECASTING
Keywords:
Dengue Forecasting, Mathematical modelling, Geospatial analysis, Machine learning, AI chatbotAbstract
Dengue fever is still a public health problem in South Cotabato, Philippines, where late discovery makes it hard to respond quickly to outbreaks. Tupi is a municipality in South Cotabato that has 15 barangays. The dengue virus spreads differently in different areas because of the different people, topography, and weather. This study created ASCLEPIUS, an AI-enhanced framework for real-time dengue geospatial analysis. It uses hybrid MLR–LSTM modeling and machine learning predictions to look at dengue incidence, temperature, humidity, windspeed, rainfall, and population size. The approach was integrated into a web-mobile platform that included geospatial mapping, symptom logging, and an AI chatbot. It achieved R² = 0.9993 in forecasts and 98% accuracy with chatbot responses, which helped promote proactive, climate-responsive dengue.
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