AI-Driven Data Engineering for Real-Time Public Health Surveillance and Early Outbreak Detection
DOI:
https://doi.org/10.18034/ei.v11i2.732Keywords:
AI-Driven Data Engineering, Public Health Surveillance, Real-Time Detection, Outbreak Prediction, Data Integration, Machine Learning, Epidemiology, Health InformaticsAbstract
This research examines AI-driven data engineering in real-time public health monitoring and early epidemic detection to improve outbreak response speed, accuracy, and effectiveness. The study investigates frameworks and technologies that use electronic health records, social media, and environmental sensors via secondary data review. AI increases epidemic detection and response via sophisticated data integration and analysis, but data quality discrepancies, model interpretability, and privacy problems persist. The research also finds that resource constraints, especially in low-income areas, hinder the broad use of these technologies. Policy implications include standardizing data frameworks to improve integration, establishing AI transparency rules, and strengthening privacy safeguards to retain public confidence. We advocate investing in scalable, cloud-based infrastructures to access AI-driven surveillance technologies equally. Addressing these difficulties will strengthen public health systems' resilience and reactivity to new health risks, improving global health security.
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