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- Publisher Website: 10.1109/MCSE.2020.3037033
- Scopus: eid_2-s2.0-85098762591
- WOS: WOS:000623419900004
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Article: Early COVID-19 pandemic modeling: Three compartmental model case studies from Texas, USA
Title | Early COVID-19 pandemic modeling: Three compartmental model case studies from Texas, USA |
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Authors | |
Keywords | decision support compartmental models Surveillance Computational modeling Pandemics uncertainty quantification SARS-CoV-2 Hospitals Data models Uncertainty COVID-19 |
Issue Date | 2021 |
Citation | Computing in Science and Engineering, 2021, v. 23 n. 1, p. 25-34 How to Cite? |
Abstract | IEEE The novel coronavirus (SARS-CoV-2) emerged in late 2019 and spread globally in early 2020. Initial reports suggested the associated disease, COVID-19, produced rapid epidemic growth and caused high mortality. As the virus sparked local epidemics in new communities, health systems and policy makers were forced to make decisions with limited information about the spread of the disease. We developed a compartmental model to project COVID-19 healthcare demands that combined information regarding SARS-CoV-2 transmission dynamics from international reports with local COVID-19 hospital census data to support response efforts in three Metropolitan Statistical Areas (MSAs) in Texas, USA: Austin-Round Rock, Houston-The Woodlands-Sugar Land, and Beaumont-Port Arthur. Our model projects that strict stay-home orders and other social distancing measures could suppress the spread of the pandemic. Our capacity to provide rapid decision-support in response to emerging threats depends on access to data, validated modeling approaches, careful uncertainty quantification, and adequate computational resources. |
Persistent Identifier | http://hdl.handle.net/10722/296013 |
ISSN | 2023 Impact Factor: 1.8 2023 SCImago Journal Rankings: 0.375 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Pierce, Kelly Anne | - |
dc.contributor.author | Ho, Ethan | - |
dc.contributor.author | Wang, Xutong | - |
dc.contributor.author | Pasco, Remy | - |
dc.contributor.author | Du, Zhanwei | - |
dc.contributor.author | Zynda, Greg | - |
dc.contributor.author | Song, Jawon | - |
dc.contributor.author | Wells, Gordon | - |
dc.contributor.author | Fox, Spencer | - |
dc.contributor.author | Meyers, Lauren Ancel | - |
dc.date.accessioned | 2021-02-11T04:52:39Z | - |
dc.date.available | 2021-02-11T04:52:39Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Computing in Science and Engineering, 2021, v. 23 n. 1, p. 25-34 | - |
dc.identifier.issn | 1521-9615 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296013 | - |
dc.description.abstract | IEEE The novel coronavirus (SARS-CoV-2) emerged in late 2019 and spread globally in early 2020. Initial reports suggested the associated disease, COVID-19, produced rapid epidemic growth and caused high mortality. As the virus sparked local epidemics in new communities, health systems and policy makers were forced to make decisions with limited information about the spread of the disease. We developed a compartmental model to project COVID-19 healthcare demands that combined information regarding SARS-CoV-2 transmission dynamics from international reports with local COVID-19 hospital census data to support response efforts in three Metropolitan Statistical Areas (MSAs) in Texas, USA: Austin-Round Rock, Houston-The Woodlands-Sugar Land, and Beaumont-Port Arthur. Our model projects that strict stay-home orders and other social distancing measures could suppress the spread of the pandemic. Our capacity to provide rapid decision-support in response to emerging threats depends on access to data, validated modeling approaches, careful uncertainty quantification, and adequate computational resources. | - |
dc.language | eng | - |
dc.relation.ispartof | Computing in Science and Engineering | - |
dc.subject | decision support | - |
dc.subject | compartmental models | - |
dc.subject | Surveillance | - |
dc.subject | Computational modeling | - |
dc.subject | Pandemics | - |
dc.subject | uncertainty quantification | - |
dc.subject | SARS-CoV-2 | - |
dc.subject | Hospitals | - |
dc.subject | Data models | - |
dc.subject | Uncertainty | - |
dc.subject | COVID-19 | - |
dc.title | Early COVID-19 pandemic modeling: Three compartmental model case studies from Texas, USA | - |
dc.type | Article | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1109/MCSE.2020.3037033 | - |
dc.identifier.scopus | eid_2-s2.0-85098762591 | - |
dc.identifier.volume | 23 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 25 | - |
dc.identifier.epage | 34 | - |
dc.identifier.eissn | 1558-366X | - |
dc.identifier.isi | WOS:000623419900004 | - |
dc.identifier.issnl | 1521-9615 | - |