Analysis and Visualization of Data on the Impacts of Covid-19 Globally and Locally

Abstract

The COVID-19 pandemic has had a profound impact on multiple aspects of human life, including food supply, mental health, and healthcare service management. This study aims to examine these impacts by applying a combination of data analysis methods such as data preprocessing, exploratory data analysis (EDA), predictive algorithms, and data visualization. The datasets utilized include information related to mental health conditions, food security, and COVID-19-related health statistics. The findings indicate a significant increase in mental health issues, such as anxiety and depression, as well as disruptions in food supply chains that have adversely affected global food security. Moreover, data visualization has proven to be a valuable tool in supporting decision-making processes in healthcare management. However, most implementations remain limited in scope and are often confined to internal agency use. Therefore, this study recommends further development in integrating data sources, enhancing the application of predictive algorithms, and optimizing data visualization for more effective decision-making in managing global health crises.