Abstract
Our study delves into the intricate relationship between socio-economic factors and the outcomes of the COVID-19 pandemic. Through comprehensive data analysis and visualization techniques, we scrutinize the disparities in case distribution and uncover trends across diverse economic backgrounds. Utilizing data from various countries, we present compelling evidence of the significant impact of economic status on pandemic outcomes. Notably, our findings reveal a strong correlation between economic indicators, such as GDP per capita, and infection rates, highlighting the critical role of economic resilience in managing public health crises. Furthermore, predictive modeling based on economic variables demonstrates promising capabilities in forecasting pandemic trajectories. We showcase the effectiveness of such models in anticipating COVID-19 trends, emphasizing the importance of integrating economic insights into public health strategies. Additionally, our analysis identifies regional patterns and correlations, offering valuable insights for targeted interventions and policy formulation. Through our research, we underscore the importance of data-driven approaches and the intersection of socio-economic factors in understanding and responding to global health crises.