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.