International Journal of Virus Studies Review Article
An Overview of AI-Driven IoT Framework for Real-Time Pathogen Surveillance and Predictive Decision Support: KSK Approach
Abstract
The rapid globalization of trade and travel has underscored the critical need for proactive, data-driven systems capable of intercepting viral outbreaks before they cascade into global crises. This paper introduces an integrated architecture that fuses the Internet of Things (IoT) with advanced Machine Learning (ML) to establish a real-time, autonomous decision-making framework for viral studies. By deploying a dense network of smart biosensors and edge-computing nodes, our system continuously monitors environmental parameters and human-to-human contact metrics, feeding high-fidelity data into a cloud-based deep learning engine. We employ a hybrid model – combining Long Short-Term Memory (LSTM) networks for temporal epidemic trend forecasting and Graph Neural Networks (GNN) for spatial contact tracing – to discern subtle anomalies in transmission patterns. Our findings demonstrate that this closed-loop system not only reduces latency in early-warning detection by over 40% compared to traditional epidemiological reporting but also provides actionable, localized policy recommendations. This research provides a scalable blueprint for “Next-Generation Public Health Infrastructure,” moving away from reactive strategy toward a paradigm of continuous, predictive viral vigilance. The results indicate that the KSK-driven IoT system significantly outperforms traditional monitoring methods in terms of speed, accuracy, and proactive management of viral diseases.
Keywords
References (1)
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