IMPACT OF CORONA VIRUS ON GLOBAL ECONOMY

This study examines the profound impact of the COVID-19 pandemic on the global economy through the lens of Machine Learning (ML), Deep Learning (DL), and specialized software applications. Traditional econometric models, while valuable, often lacked the agility and capacity to process the vast, heterogeneous, and rapidly evolving data streams generated during the pandemic. This research highlights how AI-driven methodologies have enabled more accurate forecasting of economic indicators, granular analysis of sectoral vulnerabilities, and the assessment of policy effectiveness. We explore the use of time-series DL models (e.g., LSTMs, GRUs) for predicting GDP fluctuations, unemployment rates, and inflation under various pandemic scenarios. ML classification and regression algorithms (e.g., Random Forest, XGBoost) are investigated for identifying key drivers of economic resilience or fragility, such as lockdown stringency, vaccination rates, and digital infrastructure adoption. Furthermore, the role of Natural Language Processing (NLP) in analyzing sentiment from news and social media to gauge consumer and business confidence, and Computer Vision for monitoring mobility and supply chain disruptions, will be discussed. The study also delves into how specialized economic modeling software and data visualization tools facilitated real-time monitoring and scenario planning for policymakers. Through a comprehensive approach encompassing diverse data sources (epidemiological, economic, mobility, policy), advanced pre-processing, and rigorous model evaluation, this research aims to underscore the critical role of AI and software in understanding, predicting, and mitigating the economic consequences of global health crises.