![]() Recent surveys and literature have studied how to handle community gatherings to prevent the global spread of COVID-19. Public health authorities have approached to contain the virus spread via isolation, personal protection, and hygiene compliance, social distancing, contact tracing, and surveillance application. In the compliant scenario for industrial workplaces, airports, and places for community gathering possess the highest risk of spread without prevention. The literature had shown evidence indicating the use of a surgical mask reduces the transmissibility per individual by preventing the droplets transmission in both laboratory and clinical contexts. ![]() The role of flattering the curve via quarantine and preventing community spread using a respiratory surgical mask or N95 mask have found significance in controlling the spread in previously published literature. ![]() In the latest report, the total death caused by fever is 64.7% and 52.9% due to cough. The magnitude of infectious spread has affected more than 3.2 million peoples causing 239 K deaths, according to the European Centre for Disease Prevention and Control. The recent outbreak of coronavirus SARS-CoV-2 infection early detected in December 2019 in Wuhan, China. The framework further equips government agency, system providers to design and construct technology-oriented models in community setup to increase the quality of life using emerging technologies into smart urban environments. The paper enriches the technological advancement in artificial intelligence and edge computing applied to problems in society and healthcare systems. The paper proposes new Edge-AI algorithm for building technology-oriented solutions for detecting mask in human movement and social distance. 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 study includes selective AI models for benchmark analysis and were assessed on performance and accuracy in edge computing environment for large-scale societal setup. ![]() 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 adoption will thus lead in smart city planning and development focusing on citizen health systems contributing to improved quality of life. The framework further provides implementation guideline in industrial setup as well for governance and contact tracing tasks. 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 purpose of the paper is to provide innovative emerging technology framework for community to combat epidemic situations.
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