Estimation of COVID-19 spread curves integrating global data and borrowing information

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. Scientists are working on estimating or predicting infection trajectory for the COVID-19 confirmed cases, which will be useful for future planning and policymaking to effectively cope with the disease. 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 to estimate COVID-19 infection trajectories. Due to information borrowing across multiple countries, the proposed growth curve models provide a powerful predictive tool endowed with uncertainty quantification. They outperform the existing individual country-based models. Additionally, we use countrywide covariates to adjust infection trajectories. A joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify the possible country-level risk factors for severe disease due to COVID-19.