TITLE:
CRISE: Toward Better Understanding of COVID-19 Psychological Impact during Lockdown
AUTHORS:
Hussein Baalbaki, Hassan Harb, Chamseddine Zaki, Youssef Alakoury, Layla Tannoury, Michel Nabaa
KEYWORDS:
COVID-19, Mental Health, Data Clustering, Similarity Function, Data Reduction, Correlation Matrix
JOURNAL NAME:
Journal of Computer and Communications,
Vol.9 No.8,
August
12,
2021
ABSTRACT: Recently, the COVID-19 emerged in China and propagated around all the world has threatened millions of people and affected most countries and governments at several sides such as economical, educational, tourism, healthcare, etc. Indeed, one of the most important challenges that directly affect the people is the psychological side due to the harsh policies imposed by public authorities in most countries. In this paper, we propose a framework called CRISE that allows studying and understanding the psychological effect of COVID-19 during the lockdown period. Mainly, CRISE consists of four data stages: Collection, tRansformation, reductIon, and cluStEring. The first stage collects data from more than 2000 participants through a questionnaire containing attributes related to psychological effect before and during the lockdown. The second stage aims to preprocess the data before performing the study stage. The third stage proposes a model that finds the similarities among the attributes, based on the correlation matrix, to reduce its number. Finally, the fourth stage introduces a new version of Kmeans algorithm, called as Jaccard-based Kmeans (JKmeans), that allows to group participants having similar psychological situation in the same cluster for a later analysis. We show the effectiveness of CRISE in terms of clustering accuracy and understanding the psychological effect of COVID-19.