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The purpose of the paper is to provide innovative emerging technologyframework for community to combat epidemic situations. The paper proposes aunique outbreak response system framework based on artificial intelligence andedge computing for citizen centric services to help track and trace peopleeluding safety policies like mask detection and social distancing measure inpublic or workplace setup. The framework further provides implementationguideline in industrial setup as well for governance and contact tracing tasks.The adoption will thus lead in smart city planning and development focusing oncitizen health systems contributing to improved quality of life. The conceptualframework presented is validated through quantitative data analysis viasecondary data collection from researcher's public websites, GitHubrepositories and renowned journals and further benchmarking were conducted forexperimental results in Microsoft Azure cloud environment. The study includesselective AI-models for benchmark analysis and were assessed on performance andaccuracy in edge computing environment for large scale societal setup. OverallYOLO model Outperforms in object detection task and is faster enough for maskdetection and HRNetV2 outperform semantic segmentation problem applied to solvesocial distancing task in AI-Edge inferencing environmental setup. The paperproposes new Edge-AI algorithm for building technology-oriented solutions fordetecting mask in human movement and social distance. The paper enriches thetechnological advancement in artificial intelligence and edge-computing appliedto problems in society and healthcare systems. The framework further equipsgovernment agency, system providers to design and constructstechnology-oriented models in community setup to Increase the quality of lifeusing emerging technologies into smart urban environments.

HRNET: AI on Edge for mask detection and social distancing