In this paper, we mainly investigate how COVID-19 affects society in multiple ways, such as infected, death, and recovered population, air and ground transportation, and unemployment rate in specific countries. This is important to study since it can help the government create a specific policy to minimize the negative influence of COVID-19 on our society as a whole. Recent works focus on the prevention of COVID-19 and the efficiency of specific immunization. However, the influence of COVID-19 in a specific area of society, such as transportation, has not been paid enough attention to. We first used K-means to find similarities between countries, then we grouped all nations by their continents and used bar plots to visualize how each continent performs. Then, we used matplotlib to visualize the influence of pandemics on air transportation and ground transportation of the U.S. Finally. We create an interactive visualization that investigates the unemployment rate in specific countries, such as China, the United States, Japan, the United Kingdom, and Canada, during the pandemic period. Our methods show all those continents, including Africa, Asia, Australia/Oceania, Europe, North America, and South America, can be divided into five clusters according to the test percentage, and South America represents the highest death percentage. Europe has both the highest test and infected percentage. For both air transportation and inbound ground transportation, revenue and numbers of transportation dropped dramatically at the beginning of April 2020, which is the outbreak of COVID-19 in the U.S. Similarly, the unemployment rate for North America suddenly boost. © 2021 IEEE.
COVID-19 Influence: A General Analysis using Machine Learning Methods