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?:abstract
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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.
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?:doi
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10.1371/journal.pone.0236860
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document_parses/pdf_json/a990754ac8d968bcc7e9eb6f88f61a89b6300e8a.json; document_parses/pdf_json/cebcf16999f2255a91353ba21e81b39d195a6099.json
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document_parses/pmc_json/PMC7390340.xml.json
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?:title
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Estimation of COVID-19 spread curves integrating global data and borrowing information
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