Details

Autor(en) / Beteiligte

• Chaity, Taspia Tazri
• Khan, Md. Ashikur Rahman
• Rahman, Fardowsi

Titel

Prediction of impacts and outbreak of COVID-19 on the society using distinct machine learning algorithms

Ist Teil von

• Iran Journal of Computer Science (Online), 2024-06, Vol.7 (2), p.155-175

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The COVID-19 pandemic has substantially impacted daily wage workers in various professions due to restrictions such as lockdowns and social distancing measures. COVID-19 severely affects those people with weakened immune systems. The duration of isolation, lockdown, social distancing, and economic instability changes people’s lifestyles. Thus, COVID-19 has some implicit effects on daily livelihood. Considering this matter, this study desires to analyze and forecast COVID-19’s effects. We collected data on 1665 daily wage workers from eight individual professions to analyze and predict these impacts through an offline survey. Distinct Machine Learning techniques are used for prediction. This research compares distinct machine learning methods for selecting an excellent model to forecast the COVID-19 impact on daily wage workers. Among the seven ML models investigated, Random Forest and XGBoost showed promising results with 92.4% and 91.2% accuracy, respectively. This research will serve as a manual for government employees and regulators to help them comprehend the implications of COVID-19 and implement measures to mitigate these adverse effects. Furthermore, the predictive model developed in this study can help predict the impact of future pandemics or crises on these workers and support the development of targeted policies and interventions to help them.