Journal of Data Science

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronvirus, which was declared as a global pandemic by the World Health Organization on March 11, 2020. In this work, we conduct a cross-sectional study to investigate how the infection fatality rate (IFR) of COVID-19 may be associated with possible geographical or demographical features of the infected population. We employ a multiple index model in combination with sliced inverse regression to facilitate the relationship between the IFR and possible risk factors. To select associated features for the infection fatality rate, we utilize an adaptive Lasso penalized sliced inverse regression method, which achieves variable selection and sufficient dimension reduction simultaneously with unimportant features removed automatically. We apply the proposed method to conduct a cross-sectional study for the COVID-19 data obtained from two time points of the outbreak.

The swift spread of the novel coronavirus is largely attributed to its stealthy transmissions in which infected patients may be asymptomatic or exhibit only flu-like symptoms in the early stage. Undetected transmissions present a remarkable challenge for the containment of the virus and pose an appalling threat to the public. An urgent question is on testing of the coronavirus. In this paper, we evaluate the situation from the statistical viewpoint by discussing the accuracy of test procedures and stress the importance of rationally interpreting test results.