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

Se Yoon Lee, Bowen Lei and Bani Mallick

PLOS ONE, 2020, vol. 15, issue 7, 1-17

Abstract: 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.

Date: 2020
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0236860

DOI: 10.1371/journal.pone.0236860

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