Data Analytics for the COVID-19 Epidemic

Abstract

With the spread of COVID-19 worldwide, people¡¯s production and life have been significantly affected. Artificial intelligence and big data technologies have been vigorously developed in recent years. It is very significant to use data science and technology to help humans in a timely and accurate manner to prevent and control the development of the epidemic, maintain social stability and assess the impact of the epidemic. This paper explores how data science can play a role from the perspectives of epidemiology, social networking, and economics. In particular, for the existing epidemic model SIR, we present a parameter learning method using particle swarm optimization (PSO) and the least squares method, and use it to predict the trend of the epidemic. Aiming at the social network data, we provide a specific method to realize sentiment analysis during the epidemic and propose an explainable fake news detection technique based on a variety of data mining methods.