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.

Highlights

  • Academic Editor: Amir MosaviOrganizing crowded events often involve complex tasks, but to organize them during an ongoing crisis or pandemic, such as COVID-19, which requires adherence to many restrictions, is much more difficult

  • We are passing through the COVID19 pandemic, a disease caused by a coronavirus [1]

  • The framework is designed for crowded events during the ongoing COVID-19 pandemic, but can be used during other pandemics and crises, ongoing COVID-19 pandemic, but can be used during other pandemics and crises, caused by either natural disasters or manmade blunders

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Summary

Introduction

Organizing crowded events often involve complex tasks, but to organize them during an ongoing crisis or pandemic, such as COVID-19, which requires adherence to many restrictions, is much more difficult. In order to check the spread of the virus, measures recommended by the WHO have been proven to be effective, and are agreed upon globally. These include socialhave distancing, limiting masks upon in public places, regularly. WHO been proven to beexposure, effective,wearing and are agreed globally These includewashing social hands, regular sanitization, so on.masks in public places, regularly washing hands, distancing, limiting exposure,and wearing events, social, religious, recreational, and political gatherings, regularCrowded sanitization, andespecially so on. We have While witnessed theofmanagement of several and irregular events during some them mandated and regular implemented pandemic restrictions, While some of them and implemented others others failed to do so.

The Hajj before and during COVID-19
Arbaeen before and during COVID-19
2.2.Literature
Proposed Framework
Application for Interface and Communication
Data Transfer for Connection and Networking
Information Gathering from Sensing Components
Data Management
Knowledge-Driven Decision Making
Algorithm for Proposed Framework
Important Procedures and Policies for Event Execution
Explanations of Main Functions of the Algorithm
Factors for Validation for ML Model
K-Fold Cross Verification
Nested Cross Validation
Time-Series Cross Validation
Model Comparison
Future Research
Findings
Conclusions
Full Text
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