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Using Data Analytics for Evaluating Social and Economic Impacts of Post-COVID Pandemic |
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PP: 259-269 |
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doi:10.18576/amis/180206
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Author(s) |
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Aline Abboud,
Hassan Harb,
Alaaeddine Ramadan,
Chamseddine Zaki,
Louai Saker,
Moustafa Ibrahim,
Abbass Nasser,
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Abstract |
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| The global spread of COVID-19 exposed flaws in healthcare systems worldwide, hindering effective response to the outbreak. Beyond a health crisis, it disrupted societies, causing widespread business closures and economic losses. Urgent socio- economic solutions are crucial to prevent prolonged suffering and safeguard lives and livelihoods. In this paper, we propose a technique that follows typical algorithms to understand the entire theory behind the effect of COVID-19 on socioeconomic levels. By convention, the technique we are mentioning consists of six stages: Data Collection, Data Cleansing, Data Transformation, Data Discretization, Correlation Analysis, and Association Rules. We collected data from approximately 500 participants using a survey addressing questions about their socioeconomic status in light of the COVID-19 pandemic. All stages are related, starting from the Data Collection Stage which is distributing the survey and retrieving data using Google Forms. Next, follows the Data Cleansing stage which fills any gaps in the raw data coming from the CSV File and throws this newly cleansed data to the next stage, the Data Transformation, which does the necessary transformations to apply the algorithms. Also, the Data Discretization stage aims to discretize non-meaningful data into intervals of meaningful data. These four stages allow the data to be ready for the Correlation Analysis stage which shows the correlation between all variables in the questionnaire. Finally, leveraging the Apriori algorithm—an optimized version of Association Rules—we extract significant if-then patterns from the processed data. All procedures were executed using the R Programming Language and its associated libraries.
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