Coronavirus and the COVID-19 pandemic have substantially altered the ways in which people learn, interact, and discover information. In the absence of everyday in-person interaction, how do people self-educate while living in isolation during such times? More specifically, do communities emerge in Google search trends related to coronavirus? Using a suite of network and community detection algorithms, we scrape and mine all Google search trends in America related to an initial search for “coronavirus,” starting with the first Google search on the term (January 16, 2020) to recently (August 11, 2020). Results indicate a near-constant shift in the structure of how people educate themselves on coronavirus. Queries in the earliest days focusing on “Wuhan” and “China”, then shift to “stimulus checks” at the height of the virus in the U.S., and finally shift to queries related to local surges of new cases in later days. A few communities emerge surrounding terms more overtly related to coronavirus (e.g., “cases”, “symptoms”, etc.). Yet, given the shift in related Google queries and the broader information environment, clear community structure for the full search space does not emerge.
COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) that was reported to spread in people in December 2019. Understanding epidemiological
features of COVID-19 is important for the ongoing global efforts to contain the virus. As a
complement to the available work, in this article we analyze the Kaggle novel coronavirus dataset
of 3397 patients dated from January 22, 2020 to March 29, 2020. We employ semiparametric
and nonparametric survival models as well as text mining and data visualization techniques to
examine the clinical manifestations and epidemiological features of COVID-19. Our analysis
shows that: (i) the median incubation time is about 5 days and older people tend to have a
longer incubation period; (ii) the median time for infected people to recover is about 20 days,
and the recovery time is significantly associated with age but not gender; (iii) the fatality rate
is higher for older infected patients than for younger patients