AI-Powered Digital Twins Integrated with IoT for Advanced Pandemic Analytics: Transforming Urban Healthcare Infrastructure and Enabling Resilient, Data-Driven Response Mechanisms

Abstract

Background Information: The necessity of flexible healthcare systems was brought to light by the COVID-19 pandemic. IoT-enabled digital twins driven by AI offer a revolutionary solution for urban healthcare infrastructure. In order to support pandemic containment and recovery efforts, this system makes real-time data analysis, predictive analytics, and robust response mechanisms possible.
Objectives: The goal of this project is to improve urban healthcare infrastructure by combining IoT technology with AI-powered digital twins for advanced pandemic analytics. During emergencies, this integration will increase work efficiency, optimize resource management, and improve real-time decision-making. Furthermore, by using predictive analytics, the system aims to offer proactive solutions that facilitate robust, data-driven response mechanisms. In order to provide more successful containment and recovery measures, the ultimate goal is to improve healthcare systems' capacity to predict, react to, and manage pandemic events.
Methods: The strategy integrates digital twin technologies, IoT data integration, and AI-based anomaly detection. To forecast hazards, real-time data is gathered from health measurements, processed, and examined. For an automated response, robotic systems are integrated. Actionable insights for resource management and decision-making are offered by data analytics.
Empirical results: When compared to conventional techniques, the system showed increased accuracy, work efficiency, and resource usage, along with a notable improvement in predicting skills during pandemic scenarios.
Conclusion: IoT-enabled digital twins with AI capabilities provide for better pandemic control by increasing system efficiency and response times.