A Framework for Crowd Management during COVID-19 with Artificial Intelligence

Mishaal M. Almutairi, Mohammad Yamin, George Halikias and Adnan Ahmed Abi Sen
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Mishaal M. Almutairi: Department of Electrical and Electronic Engineering, School of Mathematics, Computer Science and Engineering, London EC1V 0HB, UK
Mohammad Yamin: Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi Arabia
George Halikias: Department of Electrical and Electronic Engineering, School of Mathematics, Computer Science and Engineering, London EC1V 0HB, UK
Adnan Ahmed Abi Sen: Deanship of Information Technology, Islamic University, Madinah 42351, Saudi Arabia

Sustainability, 2021, vol. 14, issue 1, 1-13

Abstract: COVID-19 requires crowded events to enforce restrictions, aimed to contain the spread of the virus. However, we have seen numerous events not observing these restrictions, thus becoming super spreader events. In order to contain the spread of a human to human communicable disease, a number of restrictions, including wearing face masks, maintaining social distancing, and adhering to regular cleaning and sanitization, are critical. These restrictions are absolutely essential for crowded events. Some crowded events can take place spontaneously, such as a political rally or a protest march or a funeral procession. Controlling spontaneous crowded events, like a protest march, political rally, celebration after a sporting event, or concert, can be quite difficult, especially during a crisis like the COVID-19 pandemic. In this article, we review some well-known crowded events that have taken place during the ongoing pandemic. Guided by our review, we provide a framework using machine learning to effectively organize crowded events during the ongoing and for future crises. We also provide details of metrics for the validation of some components in the proposed framework, and an extensive algorithm. Finally, we offer explanations of its various functions of the algorithm. The proposed framework can also be adapted in other crises.

Keywords: COVID-19; crowded events; framework; machine learning; regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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