Community venue exposure risk estimator for the COVID-19 pandemic.
Identifieur interne : 000193 ( Main/Exploration ); précédent : 000192; suivant : 000194Community venue exposure risk estimator for the COVID-19 pandemic.
Auteurs : Ziheng Sun [États-Unis] ; Liping Di [États-Unis] ; William Sprigg [États-Unis] ; Daniel Tong [États-Unis] ; Mariana Casal [États-Unis]Source :
- Health & place [ 1873-2054 ] ; 2020.
Abstract
Complexities of virus genotypes and the stochastic contacts in human society create a big challenge for estimating the potential risks of exposure to a widely spreading virus such as COVID-19. To increase public awareness of exposure risks in daily activities, we propose a birthday-paradox-based probability model to implement in a web-based system, named COSRE (community social risk estimator) and make in-time community exposure risk estimation during the ongoing COVID-19 pandemic. We define exposure risk to mean the probability of people meeting potential cases in public places such as grocery stores, gyms, libraries, restaurants, coffee shops, offices, etc. Our model has three inputs: the real-time number of active and asymptomatic cases, the population in local communities, and the customer counts in the room. With COSRE, possible impacts of the pandemic can be explored through spatiotemporal analysis, e.g., a variable number of people may be projected into public places through time to assess changes of risk as the pandemic unfolds. The system has potential to advance understanding of the true exposure risks in various communities. It introduces an objective element to plan, prepare and respond during a pandemic. Spatial analysis tools are used to draw county-level exposure risks of the United States from April 1 to July 15, 2020. The correlation experiment with the new cases in the next two weeks shows that the risk estimation model offers promise in assisting people to be more precise about their personal safety and control of daily routine and social interaction. It can inform business and municipal COVID-19 policy to accelerate recovery.
DOI: 10.1016/j.healthplace.2020.102450
PubMed: 33010661
PubMed Central: PMC7522786
Affiliations:
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To increase public awareness of exposure risks in daily activities, we propose a birthday-paradox-based probability model to implement in a web-based system, named COSRE (community social risk estimator) and make in-time community exposure risk estimation during the ongoing COVID-19 pandemic. We define exposure risk to mean the probability of people meeting potential cases in public places such as grocery stores, gyms, libraries, restaurants, coffee shops, offices, etc. Our model has three inputs: the real-time number of active and asymptomatic cases, the population in local communities, and the customer counts in the room. With COSRE, possible impacts of the pandemic can be explored through spatiotemporal analysis, e.g., a variable number of people may be projected into public places through time to assess changes of risk as the pandemic unfolds. The system has potential to advance understanding of the true exposure risks in various communities. It introduces an objective element to plan, prepare and respond during a pandemic. Spatial analysis tools are used to draw county-level exposure risks of the United States from April 1 to July 15, 2020. The correlation experiment with the new cases in the next two weeks shows that the risk estimation model offers promise in assisting people to be more precise about their personal safety and control of daily routine and social interaction. It can inform business and municipal COVID-19 policy to accelerate recovery.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">33010661</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>26</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1873-2054</ISSN><JournalIssue CitedMedium="Internet"><Volume>66</Volume><PubDate><Year>2020</Year><Month>11</Month></PubDate></JournalIssue><Title>Health & place</Title><ISOAbbreviation>Health Place</ISOAbbreviation></Journal><ArticleTitle>Community venue exposure risk estimator for the COVID-19 pandemic.</ArticleTitle><Pagination><MedlinePgn>102450</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S1353-8292(20)31033-9</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.healthplace.2020.102450</ELocationID><Abstract><AbstractText>Complexities of virus genotypes and the stochastic contacts in human society create a big challenge for estimating the potential risks of exposure to a widely spreading virus such as COVID-19. To increase public awareness of exposure risks in daily activities, we propose a birthday-paradox-based probability model to implement in a web-based system, named COSRE (community social risk estimator) and make in-time community exposure risk estimation during the ongoing COVID-19 pandemic. We define exposure risk to mean the probability of people meeting potential cases in public places such as grocery stores, gyms, libraries, restaurants, coffee shops, offices, etc. Our model has three inputs: the real-time number of active and asymptomatic cases, the population in local communities, and the customer counts in the room. With COSRE, possible impacts of the pandemic can be explored through spatiotemporal analysis, e.g., a variable number of people may be projected into public places through time to assess changes of risk as the pandemic unfolds. The system has potential to advance understanding of the true exposure risks in various communities. It introduces an objective element to plan, prepare and respond during a pandemic. Spatial analysis tools are used to draw county-level exposure risks of the United States from April 1 to July 15, 2020. The correlation experiment with the new cases in the next two weeks shows that the risk estimation model offers promise in assisting people to be more precise about their personal safety and control of daily routine and social interaction. It can inform business and municipal COVID-19 policy to accelerate recovery.</AbstractText><CopyrightInformation>Copyright © 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Ziheng</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA; Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA. Electronic address: zsun@gmu.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Di</LastName><ForeName>Liping</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA; Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA. Electronic address: ldi@gmu.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sprigg</LastName><ForeName>William</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA. Electronic address: wsprigg@arizona.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tong</LastName><ForeName>Daniel</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA; Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA, USA. Electronic address: qtong@gmu.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Casal</LastName><ForeName>Mariana</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Infectious Disease and Epidemiology Section, Pinal County Health Department, Florence, AZ, USA. Electronic address: mariana.casal@pinal.gov.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>29</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Health Place</MedlineTA><NlmUniqueID>9510067</NlmUniqueID><ISSNLinking>1353-8292</ISSNLinking></MedlineJournalInfo><CitationSubset>T</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Birthday paradox</Keyword><Keyword MajorTopicYN="Y">COVID-19 pandemic</Keyword><Keyword MajorTopicYN="Y">Exposure risk</Keyword><Keyword MajorTopicYN="Y">Probability</Keyword><Keyword MajorTopicYN="Y">Risk mapping</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>09</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>09</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>10</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>10</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>10</Month><Day>3</Day><Hour>20</Hour><Minute>15</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33010661</ArticleId><ArticleId IdType="pii">S1353-8292(20)31033-9</ArticleId><ArticleId IdType="doi">10.1016/j.healthplace.2020.102450</ArticleId><ArticleId IdType="pmc">PMC7522786</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Arizona</li><li>Virginie</li></region></list><tree><country name="États-Unis"><region name="Virginie"><name sortKey="Sun, Ziheng" sort="Sun, Ziheng" uniqKey="Sun Z" first="Ziheng" last="Sun">Ziheng Sun</name></region><name sortKey="Casal, Mariana" sort="Casal, Mariana" uniqKey="Casal M" first="Mariana" last="Casal">Mariana Casal</name><name sortKey="Di, Liping" sort="Di, Liping" uniqKey="Di L" first="Liping" last="Di">Liping Di</name><name sortKey="Sprigg, William" sort="Sprigg, William" uniqKey="Sprigg W" first="William" last="Sprigg">William Sprigg</name><name sortKey="Tong, Daniel" sort="Tong, Daniel" uniqKey="Tong D" first="Daniel" last="Tong">Daniel Tong</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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