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- Publisher Website: 10.1155/2023/5046932
- Scopus: eid_2-s2.0-85177821139
- WOS: WOS:001070817800001
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Article: International Importation Risk Estimation of SARS-CoV-2 Omicron Variant with Incomplete Mobility Data
Title | International Importation Risk Estimation of SARS-CoV-2 Omicron Variant with Incomplete Mobility Data |
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Authors | |
Issue Date | 14-Sep-2023 |
Publisher | Wiley |
Citation | Transboundary and Emerging Diseases, 2023, v. 2023 How to Cite? |
Abstract | A novel Omicron subvariant named BQ.1 emerged in Nigeria in July 2022 and has since become a dominant strain, causing a significant number of repeated infections even in countries with high-vaccination rates. Due to the high flow of people between Western Africa and other non-African countries, there is a high risk of Omicron BQ.1 being introduced to other countries from Western Africa. In this context, we developed a model based on deep neural networks to estimate the probability that the Omicron BQ.1 introduced to other countries from Western Africa based on the incomplete population mobility data from Western Africa to other non-African countries. Our study found that the highest risk was in France and Spain during the study period, while the importation risk of other 13 non-African countries including Canada and the United States is also high. Our approach sheds light on how deep learning techniques can assist in the development of public health policies, and it has the potential to be extended to other types of viruses. |
Persistent Identifier | http://hdl.handle.net/10722/341757 |
ISSN | 2023 Impact Factor: 3.5 2023 SCImago Journal Rankings: 0.921 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhu, Yan | - |
dc.contributor.author | Bai, Yuan | - |
dc.contributor.author | Xu, Mingda | - |
dc.contributor.author | Wang, Lin | - |
dc.contributor.author | Li, Thomas K T | - |
dc.contributor.author | Du, Zhanwei | - |
dc.contributor.author | Wang, Yuexuan | - |
dc.date.accessioned | 2024-03-26T05:36:57Z | - |
dc.date.available | 2024-03-26T05:36:57Z | - |
dc.date.issued | 2023-09-14 | - |
dc.identifier.citation | Transboundary and Emerging Diseases, 2023, v. 2023 | - |
dc.identifier.issn | 1865-1674 | - |
dc.identifier.uri | http://hdl.handle.net/10722/341757 | - |
dc.description.abstract | <p>A novel Omicron subvariant named BQ.1 emerged in Nigeria in July 2022 and has since become a dominant strain, causing a significant number of repeated infections even in countries with high-vaccination rates. Due to the high flow of people between Western Africa and other non-African countries, there is a high risk of Omicron BQ.1 being introduced to other countries from Western Africa. In this context, we developed a model based on deep neural networks to estimate the probability that the Omicron BQ.1 introduced to other countries from Western Africa based on the incomplete population mobility data from Western Africa to other non-African countries. Our study found that the highest risk was in France and Spain during the study period, while the importation risk of other 13 non-African countries including Canada and the United States is also high. Our approach sheds light on how deep learning techniques can assist in the development of public health policies, and it has the potential to be extended to other types of viruses.</p> | - |
dc.language | eng | - |
dc.publisher | Wiley | - |
dc.relation.ispartof | Transboundary and Emerging Diseases | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | International Importation Risk Estimation of SARS-CoV-2 Omicron Variant with Incomplete Mobility Data | - |
dc.type | Article | - |
dc.identifier.doi | 10.1155/2023/5046932 | - |
dc.identifier.scopus | eid_2-s2.0-85177821139 | - |
dc.identifier.volume | 2023 | - |
dc.identifier.eissn | 1865-1682 | - |
dc.identifier.isi | WOS:001070817800001 | - |
dc.identifier.issnl | 1865-1674 | - |