  Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to
global health. The rapid spread of the virus has created pandemic, and
countries all over the world are struggling with a surge in COVID-19 infected
cases. There are no drugs or other therapeutics approved by the US Food and
Drug Administration to prevent or treat COVID-19: information on the disease is
very limited and scattered even if it exists. This motivates the use of data
integration, combining data from diverse sources and eliciting useful
information with a unified view of them. In this paper, we propose a Bayesian
hierarchical model that integrates global data for real-time prediction of
infection trajectory for multiple countries. Because the proposed model takes
advantage of borrowing information across multiple countries, it outperforms an
existing individual country-based model. As fully Bayesian way has been
adopted, the model provides a powerful predictive tool endowed with uncertainty
quantification. Additionally, a joint variable selection technique has been
integrated into the proposed modeling scheme, which aimed to identify possible
country-level risk factors for severe disease due to COVID-19.
