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Article: Early COVID-19 pandemic modeling: Three compartmental model case studies from Texas, USA

TitleEarly COVID-19 pandemic modeling: Three compartmental model case studies from Texas, USA
Authors
Keywordsdecision support
compartmental models
Surveillance
Computational modeling
Pandemics
uncertainty quantification
SARS-CoV-2
Hospitals
Data models
Uncertainty
COVID-19
Issue Date2021
Citation
Computing in Science and Engineering, 2021, v. 23 n. 1, p. 25-34 How to Cite?
AbstractIEEE 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 Identifierhttp://hdl.handle.net/10722/296013
ISSN
2023 Impact Factor: 1.8
2023 SCImago Journal Rankings: 0.375
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorPierce, Kelly Anne-
dc.contributor.authorHo, Ethan-
dc.contributor.authorWang, Xutong-
dc.contributor.authorPasco, Remy-
dc.contributor.authorDu, Zhanwei-
dc.contributor.authorZynda, Greg-
dc.contributor.authorSong, Jawon-
dc.contributor.authorWells, Gordon-
dc.contributor.authorFox, Spencer-
dc.contributor.authorMeyers, Lauren Ancel-
dc.date.accessioned2021-02-11T04:52:39Z-
dc.date.available2021-02-11T04:52:39Z-
dc.date.issued2021-
dc.identifier.citationComputing in Science and Engineering, 2021, v. 23 n. 1, p. 25-34-
dc.identifier.issn1521-9615-
dc.identifier.urihttp://hdl.handle.net/10722/296013-
dc.description.abstractIEEE 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.languageeng-
dc.relation.ispartofComputing in Science and Engineering-
dc.subjectdecision support-
dc.subjectcompartmental models-
dc.subjectSurveillance-
dc.subjectComputational modeling-
dc.subjectPandemics-
dc.subjectuncertainty quantification-
dc.subjectSARS-CoV-2-
dc.subjectHospitals-
dc.subjectData models-
dc.subjectUncertainty-
dc.subjectCOVID-19-
dc.titleEarly COVID-19 pandemic modeling: Three compartmental model case studies from Texas, USA-
dc.typeArticle-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1109/MCSE.2020.3037033-
dc.identifier.scopuseid_2-s2.0-85098762591-
dc.identifier.volume23-
dc.identifier.issue1-
dc.identifier.spage25-
dc.identifier.epage34-
dc.identifier.eissn1558-366X-
dc.identifier.isiWOS:000623419900004-
dc.identifier.issnl1521-9615-

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