Empirical assessment of COVID-19 infections and information diffusion: a data science approach

Abstract

The spread of the novel coronavirus disease, SARS-CoV-19 (COVID-19), has affected human activities everywhere, resulting in fear and panic among all age groups. Hence, this study implements a novel data science process to empirically model the daily reported cases and Google search queries in 14 countries. We observed a strong positive association (0.79-0.96) among reported cases of COVID-19 in the 14 countries. Furthermore, there is an inverse correlation of -0.18 to -0.62 between information diffusion on the virus and reported cases (new cases and deaths). Our outcome shows that contagious diseases are highly predictable using historical records from other countries and information spread on the disease.