Are college campuses superspreaders? A data-driven modeling study.
Identifieur interne : 000105 ( Main/Exploration ); précédent : 000104; suivant : 000106Are college campuses superspreaders? A data-driven modeling study.
Auteurs : Hannah Lu [États-Unis] ; Cortney Weintz [États-Unis] ; Joseph Pace [États-Unis] ; Dhiraj Indana [États-Unis] ; Kevin Linka [États-Unis] ; Ellen Kuhl [États-Unis]Source :
- Computer methods in biomechanics and biomedical engineering [ 1476-8259 ] ; 2021.
Abstract
The COVID-19 pandemic continues to present enormous challenges for colleges and universities and strategies for save reopening remain a topic of ongoing debate. Many institutions that reopened cautiously in the fall experienced a massive wave of infections and colleges were soon declared as the new hotspots of the pandemic. However, the precise effects of college outbreaks on their immediate neighborhood remain largely unknown. Here we show that the first two weeks of instruction present a high-risk period for campus outbreaks and that these outbreaks tend to spread into the neighboring communities. By integrating a classical mathematical epidemiology model and Bayesian learning, we learned the dynamic reproduction number for 30 colleges from their daily case reports. Of these 30 institutions, 14 displayed a spike of infections within the first two weeks of class, with peak seven-day incidences well above 1,000 per 100,000, an order of magnitude larger than the nation-wide peaks of 70 and 150 during the first and second waves of the pandemic. While most colleges were able to rapidly reduce the number of new infections, many failed to control the spread of the virus beyond their own campus: Within only two weeks, 17 campus outbreaks translated directly into peaks of infection within their home counties. These findings suggests that college campuses are at risk to develop an extreme incidence of COVID-19 and become superspreaders for neighboring communities. We anticipate that tight test-trace-quarantine strategies, flexible transition to online instruction, and-most importantly-compliance with local regulations will be critical to ensure a safe campus reopening after the winter break.
DOI: 10.1080/10255842.2020.1869221
PubMed: 33439055
Affiliations:
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Many institutions that reopened cautiously in the fall experienced a massive wave of infections and colleges were soon declared as the new hotspots of the pandemic. However, the precise effects of college outbreaks on their immediate neighborhood remain largely unknown. Here we show that the first two weeks of instruction present a high-risk period for campus outbreaks and that these outbreaks tend to spread into the neighboring communities. By integrating a classical mathematical epidemiology model and Bayesian learning, we learned the dynamic reproduction number for 30 colleges from their daily case reports. Of these 30 institutions, 14 displayed a spike of infections within the first two weeks of class, with peak seven-day incidences well above 1,000 per 100,000, an order of magnitude larger than the nation-wide peaks of 70 and 150 during the first and second waves of the pandemic. While most colleges were able to rapidly reduce the number of new infections, many failed to control the spread of the virus beyond their own campus: Within only two weeks, 17 campus outbreaks translated directly into peaks of infection within their home counties. These findings suggests that college campuses are at risk to develop an extreme incidence of COVID-19 and become superspreaders for neighboring communities. We anticipate that tight test-trace-quarantine strategies, flexible transition to online instruction, and-most importantly-compliance with local regulations will be critical to ensure a safe campus reopening after the winter break.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">33439055</PMID><DateRevised><Year>2021</Year><Month>01</Month><Day>27</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1476-8259</ISSN><JournalIssue CitedMedium="Internet"><PubDate><Year>2021</Year><Month>Jan</Month><Day>13</Day></PubDate></JournalIssue><Title>Computer methods in biomechanics and biomedical engineering</Title><ISOAbbreviation>Comput Methods Biomech Biomed Engin</ISOAbbreviation></Journal><ArticleTitle>Are college campuses superspreaders? A data-driven modeling study.</ArticleTitle><Pagination><MedlinePgn>1-11</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1080/10255842.2020.1869221</ELocationID><Abstract><AbstractText>The COVID-19 pandemic continues to present enormous challenges for colleges and universities and strategies for save reopening remain a topic of ongoing debate. Many institutions that reopened cautiously in the fall experienced a massive wave of infections and colleges were soon declared as the new hotspots of the pandemic. However, the precise effects of college outbreaks on their immediate neighborhood remain largely unknown. Here we show that the first two weeks of instruction present a high-risk period for campus outbreaks and that these outbreaks tend to spread into the neighboring communities. By integrating a classical mathematical epidemiology model and Bayesian learning, we learned the dynamic reproduction number for 30 colleges from their daily case reports. Of these 30 institutions, 14 displayed a spike of infections within the first two weeks of class, with peak seven-day incidences well above 1,000 per 100,000, an order of magnitude larger than the nation-wide peaks of 70 and 150 during the first and second waves of the pandemic. While most colleges were able to rapidly reduce the number of new infections, many failed to control the spread of the virus beyond their own campus: Within only two weeks, 17 campus outbreaks translated directly into peaks of infection within their home counties. These findings suggests that college campuses are at risk to develop an extreme incidence of COVID-19 and become superspreaders for neighboring communities. We anticipate that tight test-trace-quarantine strategies, flexible transition to online instruction, and-most importantly-compliance with local regulations will be critical to ensure a safe campus reopening after the winter break.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lu</LastName><ForeName>Hannah</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Energy Resources Engineering, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Weintz</LastName><ForeName>Cortney</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Computer Science, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pace</LastName><ForeName>Joseph</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Mechanical Engineering, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Indana</LastName><ForeName>Dhiraj</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Mechanical Engineering, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Linka</LastName><ForeName>Kevin</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Mechanical Engineering, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kuhl</LastName><ForeName>Ellen</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Mechanical Engineering, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2021</Year><Month>01</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Comput Methods Biomech Biomed Engin</MedlineTA><NlmUniqueID>9802899</NlmUniqueID><ISSNLinking>1025-5842</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Coronavirus</Keyword><Keyword MajorTopicYN="N">SEIR model</Keyword><Keyword MajorTopicYN="N">epidemiology</Keyword><Keyword MajorTopicYN="N">machine learning</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2021</Year><Month>1</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2021</Year><Month>1</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2021</Year><Month>1</Month><Day>13</Day><Hour>12</Hour><Minute>10</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33439055</ArticleId><ArticleId IdType="doi">10.1080/10255842.2020.1869221</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Californie</li></region><settlement><li>Stanford (Californie)</li></settlement><orgName><li>Université Stanford</li></orgName></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Lu, Hannah" sort="Lu, Hannah" uniqKey="Lu H" first="Hannah" last="Lu">Hannah Lu</name></region><name sortKey="Indana, Dhiraj" sort="Indana, Dhiraj" uniqKey="Indana D" first="Dhiraj" last="Indana">Dhiraj Indana</name><name sortKey="Kuhl, Ellen" sort="Kuhl, Ellen" uniqKey="Kuhl E" first="Ellen" last="Kuhl">Ellen Kuhl</name><name sortKey="Linka, Kevin" sort="Linka, Kevin" uniqKey="Linka K" first="Kevin" last="Linka">Kevin Linka</name><name sortKey="Pace, Joseph" sort="Pace, Joseph" uniqKey="Pace J" first="Joseph" last="Pace">Joseph Pace</name><name sortKey="Weintz, Cortney" sort="Weintz, Cortney" uniqKey="Weintz C" first="Cortney" last="Weintz">Cortney Weintz</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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