Data Analytics for COVID-19 Pandemic

Abstract

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, the pandemic situation has begun to undergo positive changes with the joint efforts of various countries and world organizations. However, pressures such as the COVID-19 mutations and the sharp rise in confirmed cases have brought uncertainties to the prevention and control of the pandemic. The overall situation is still severe and complex. Based on the multi-dimensional spatial-temporal COVID-19 data collected by the open-source NetEase News (NEN) website and a real-time dynamic website, it is to explore the characteristics of the pandemic data, visualize the development trend, and analyze the spread of the pandemic in this paper. Moreover, it is to provide a rule basis for the prevention and control of the COVID-19 pandemic by constructing the decision tree model. From the results, some suggestions are provided for decision-makers.