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COVID-19 is a highly contagious, potentially lethal respiratory disease caused by a strain of coronavirus. Utilizing sets of data collected by Johns Hopkins University, this research paper analyzes the global trends of COVID-19 and the pandemic’s effect on the global economy. The aim of this paper is to provide accurate information of COVID-19 through comparing the situation of the world before and after the pandemic. All visual representations have been created using python, a programming language, and each figure is accompanied with a thorough breakdown of its cumulative data. Its analysis shows the impact of COVID-19 globally and regionally. Machine learning is utilized to predict the future trends in the number of cases, from which it can be forecasted that the world will see a continuous increase in COVID-19 cases with an exception of a few countries where cases of COVID-19 have been declining consistently. Polynomial regression has been used to predict the future trend of COVID-19. Observing the numbers used in this paper such as card usage, job posts, and confirmed cases provides evidence to the fact that COVID-19 has negatively impacted the global economy. Statistics can show the relationship between COVID-19 and the global economy, nonetheless providing evidence on why certain events are occuring during this pandemic.

Analyzing the Impact of COVID 19 on Global Trends and Predicting Future Cases