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. There are no drugs or other therapeutics approved by the US Food and Dru...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0236860 |
| _version_ | 1818590231781179392 |
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| author | Se Yoon Lee Bowen Lei Bani Mallick |
| author_facet | Se Yoon Lee Bowen Lei Bani Mallick |
| author_sort | Se Yoon Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T09:53:16Z |
| format | Article |
| id | doaj.art-844ec3acb481401c98f459df18e9d35e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-16T09:53:16Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-844ec3acb481401c98f459df18e9d35e2022-12-21T22:35:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01157e023686010.1371/journal.pone.0236860Estimation of COVID-19 spread curves integrating global data and borrowing information.Se Yoon LeeBowen LeiBani MallickCurrently, 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.https://doi.org/10.1371/journal.pone.0236860 |
| spellingShingle | Se Yoon Lee Bowen Lei Bani Mallick Estimation of COVID-19 spread curves integrating global data and borrowing information. PLoS ONE |
| title | Estimation of COVID-19 spread curves integrating global data and borrowing information. |
| title_full | Estimation of COVID-19 spread curves integrating global data and borrowing information. |
| title_fullStr | Estimation of COVID-19 spread curves integrating global data and borrowing information. |
| title_full_unstemmed | Estimation of COVID-19 spread curves integrating global data and borrowing information. |
| title_short | Estimation of COVID-19 spread curves integrating global data and borrowing information. |
| title_sort | estimation of covid 19 spread curves integrating global data and borrowing information |
| url | https://doi.org/10.1371/journal.pone.0236860 |
| work_keys_str_mv | AT seyoonlee estimationofcovid19spreadcurvesintegratingglobaldataandborrowinginformation AT bowenlei estimationofcovid19spreadcurvesintegratingglobaldataandborrowinginformation AT banimallick estimationofcovid19spreadcurvesintegratingglobaldataandborrowinginformation |