A study on IoT and AI for Predictive Modeling and Control of Infectious Disease Transmission - Kazi Sultanabanu Sayyad Liyakat (International Journal of Pathogens, 2025)
Background: The global response to novel and recurring infectious diseases is frequently hindered by surveillance systems that are slow, siloed, and reactive. Traditional epidemiology relies on retrospective analysis of clinical reports, often missing the critical early phase of autocatalytic spread. The urgency of modern public health necessitates a shift toward real-time, predictive intelligence. Methods: This study investigates the development and deployment of a synergistic paradigm integrating the Internet of Things (IoT) infrastructure with advanced Artificial Intelligence (AI) modeling to enhance the spatiotemporal fidelity of infectious disease monitoring. IoT devices—including environmental sensors (e.g., wastewater metagenomics, air quality monitors), smart wearables capturing anonymized physiological metrics, and aggregated mobile movement data—were utilized to continuously stream massive, multi-modal datasets. These high-velocity data streams were then processed using deep learning and sophisticated Machine Learning (ML) algorithms (specifically, Recurrent Neural Networks and Bayesian networks) to identify non-linear correlations between environmental, behavioral, and clinical markers. Conclusion: The integration of IoT for ubiquitous data capture and AI for pattern recognition establishes a robust foundation for next-generation digital epidemiology. This intelligent surveillance framework enables public health authorities to transition from retrospective reporting to proactive, preemptive containment, significantly reducing the reproductive number of infectious agents and maximizing public health resilience in the face of future pandemics.
Keywords: AI; IOT; Infection Diseases; Wastewater Epidemiology; pathogens