AI-Driven IoT for Early Detection of Epidemics: A Case Study on Influenza Outbreaks
Abstract
Early detection of epidemics is crucial for effective public health response and mitigation. This paper explores the use of AI-driven IoT systems for the early detection of influenza outbreaks. We present a framework that integrates IoT devices to collect real-time health data from populations and machine learning algorithms to identify patterns indicative of an outbreak. The system's performance is evaluated using historical influenza data, demonstrating its ability to detect outbreaks earlier than traditional methods. The paper discusses the implications for public health policy and the potential for applying this approach to other infectious diseases.
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