  The purpose of the paper is to provide innovative emerging technology
framework for community to combat epidemic situations. The paper proposes a
unique outbreak response system framework based on artificial intelligence and
edge computing for citizen centric services to help track and trace people
eluding safety policies like mask detection and social distancing measure in
public or workplace setup. The framework further provides implementation
guideline in industrial setup as well for governance and contact tracing tasks.
The adoption will thus lead in smart city planning and development focusing on
citizen health systems contributing to improved quality of life. The conceptual
framework presented is validated through quantitative data analysis via
secondary data collection from researcher's public websites, GitHub
repositories and renowned journals and further benchmarking were conducted for
experimental results in Microsoft Azure cloud environment. The study includes
selective AI-models for benchmark analysis and were assessed on performance and
accuracy in edge computing environment for large scale societal setup. Overall
YOLO model Outperforms in object detection task and is faster enough for mask
detection and HRNetV2 outperform semantic segmentation problem applied to solve
social distancing task in AI-Edge inferencing environmental setup. The paper
proposes new Edge-AI algorithm for building technology-oriented solutions for
detecting mask in human movement and social distance. The paper enriches the
technological advancement in artificial intelligence and edge-computing applied
to problems in society and healthcare systems. The framework further equips
government agency, system providers to design and constructs
technology-oriented models in community setup to Increase the quality of life
using emerging technologies into smart urban environments.
