AI-Infused Spiking Neural Architectures and Edge Computing Modalities: Recalibrating Pandemic Surveillance, Dynamic Health Interpretations, and Contextual Automations in Complex Urban Terrains

Abstract

Background Information: The need for flexible, real-time systems for urban health monitoring has been made clear by the COVID-19 epidemic. The dynamic nature of pandemics is too much for traditional institutions to handle, particularly in crowded metropolitan settings. AI and edge computing are two examples of advanced technologies that are essential for improving pandemic surveillance and decision-making.
Objectives: The study aims to increase task efficiency in complex urban environments during health crises, optimize real-time decision-making through edge computing, integrate these technologies for dynamic health interpretations, and create an AI-powered framework using spiking neural networks for effective pandemic surveillance.

Methods: This study employs edge computing for localized, low-latency decision-making and spiking neural networks (SNNs) for analyzing health data in real-time. For reliable and flexible responses, the approach also includes AI-driven anomaly detection, autonomous robotic automation, and predictive health assessments.
Empirical results: The results demonstrate that the integrated system outperforms separate approaches in terms of task efficiency, accuracy, precision, and recall. The best results were obtained by the fully integrated strategy, especially in anomaly detection, resource allocation, and decision-making.
Conclusion: Spiking neural networks, edge computing, and AI-driven analysis work together to improve pandemic surveillance and decision-making in intricate urban environments. In order to handle the ever-changing issues in urban healthcare systems, future research will concentrate on enhancing scalability and incorporating more sophisticated AI models.