Artificial Intelligence And Robotics Nexus: Transforming Pandemic Containment Dynamics Through Elevated Anomaly Recognition And Autonomous Operational Methodologies

Abstract

Backround Information: Gaps in conventional containment tactics were made clear by the COVID-19 pandemic.
In order to improve anomaly detection and autonomous operations, this project combines robotics and artificial
intelligence (AI). This system improves the effectiveness of pandemic containment and lowers human exposure in
crucial operations by utilizing robotics for operational automation and artificial intelligence (AI) for real-time
anomaly identification.
Objectives: In order to increase efficiency, adaptability, and precision in pandemic containment across
healthcare, logistics, and high-risk areas, this project intends to develop AI-based anomaly detection systems,
integrate robotics for autonomous operations, and combine these technologies.
Methods: AI identifies hotspots using anomaly detection techniques, including Mahalanobis distance. Robotics
uses optimization methods for logistics and self-cleaning. In simulated pandemic containment scenarios,
performance is assessed using criteria for task completion, accuracy, and efficiency.
Emphirical results: By achieving 94% task efficiency, the integrated system reduced the need for human
intervention by 80%. Across test situations, anomaly detection improved response capabilities, operational safety,
and resource usage due to its high accuracy.
Conclusion: The dynamics of pandemic containment are greatly enhanced when AI and robotics are combined.
To improve adaptation and resilience during global health emergencies, future research should concentrate on
scalability, real-world deployment, and cross-domain applications.