Estimation of COVID-19 spread curves integrating global data and borrowing information. Academic Article uri icon


  • 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.

published proceedings

  • PLoS One

altmetric score

  • 7.25

author list (cited authors)

  • Lee, S. Y., Lei, B., & Mallick, B.

citation count

  • 38

complete list of authors

  • Lee, Se Yoon||Lei, Bowen||Mallick, Bani

editor list (cited editors)

  • Samy, A. M.

publication date

  • January 2020