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dc.contributor.authorTang, Yuanji
dc.contributor.authorWang, Shixia
dc.date2022-08-11T08:08:10.000
dc.date.accessioned2022-08-23T15:44:50Z
dc.date.available2022-08-23T15:44:50Z
dc.date.issued2020-04-30
dc.date.submitted2020-04-29
dc.identifier.citation<p>Tang Y, Wang S. Mathematic modeling of COVID-19 in the United States. Emerg Microbes Infect. 2020 Dec;9(1):827-829. doi: 10.1080/22221751.2020.1760146. PMID: 32338150. <a href="https://doi.org/10.1080/22221751.2020.1760146">Link to article on publisher's site</a></p>
dc.identifier.issn2222-1751 (Linking)
dc.identifier.doi10.1080/22221751.2020.1760146
dc.identifier.pmid32338150
dc.identifier.urihttp://hdl.handle.net/20.500.14038/27403
dc.description.abstractSince the early reports of COVID-19 cases in China in late January 2020 (1-2), the worst pandemic in 100 years has spread to the entire globe with approximately 2.4 million diagnosed cases and over 165,000 deaths up to April 20, 2020. While scientists from various public and private groups use math and computer to simulate the course of this pandemic to try to predict how this outbreak might evolve (3), most of such analyses are either quite complicated or not publicly available. Here a simple mathematic modeling approach is taken to track the outbreaks of COVID-19 in the US and its selected states to identify the peak point of such outbreak within a given geographic population, the trend of decreasing numbers of new cases after the peak and the rough calculation of accumulated total cases in this population from the beginning to the end of June 2020. The sources of COVID-19 case data are taken from various public websites since not all the data are readily available.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=32338150&dopt=Abstract">Link to Article in PubMed</a></p>
dc.rights© 2020 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCOVID-19
dc.subjectSARS-CoV-2
dc.subjectmodeling
dc.subjectUnited States
dc.subjectEpidemiology
dc.subjectDisease Modeling
dc.subjectEpidemiology
dc.subjectImmunology and Infectious Disease
dc.subjectInfectious Disease
dc.subjectMathematics
dc.subjectMicrobiology
dc.subjectVirus Diseases
dc.titleMathematic Modeling of COVID-19 in the United States
dc.typeJournal Article
dc.source.journaltitleEmerging microbes and infections
dc.source.volume9
dc.source.issue1
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1022&amp;context=covid19&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/covid19/20
dc.identifier.contextkey17564360
refterms.dateFOA2022-08-23T15:44:50Z
html.description.abstract<p>Since the early reports of COVID-19 cases in China in late January 2020 (1-2), the worst pandemic in 100 years has spread to the entire globe with approximately 2.4 million diagnosed cases and over 165,000 deaths up to April 20, 2020.</p> <p>While scientists from various public and private groups use math and computer to simulate the course of this pandemic to try to predict how this outbreak might evolve (3), most of such analyses are either quite complicated or not publicly available.</p> <p>Here a simple mathematic modeling approach is taken to track the outbreaks of COVID-19 in the US and its selected states to identify the peak point of such outbreak within a given geographic population, the trend of decreasing numbers of new cases after the peak and the rough calculation of accumulated total cases in this population from the beginning to the end of June 2020. The sources of COVID-19 case data are taken from various public websites since not all the data are readily available.</p>
dc.identifier.submissionpathcovid19/20
dc.contributor.departmentDepartment of Medicine
dc.source.pages827-829


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© 2020 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as © 2020 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.