AI-Powered Disease Outbreak Prediction Using Environmental and Social Data
Abstract
Predicting disease outbreaks is crucial for proactive healthcare planning and response. This paper presents an AI-powered framework that integrates environmental, social, and epidemiological data to forecast disease outbreaks with high accuracy. The system employs ensemble machine learning models to analyze diverse datasets, identifying correlations between environmental factors, human activity, and disease spread. Case studies on malaria and dengue outbreaks validate the framework, showing improved prediction accuracy compared to traditional epidemiological models. The findings underscore the role of AI in enhancing public health preparedness and mitigating the impact of disease outbreaks.
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