Modelling COVID-19 transmission in supermarkets using an agent-based model
Since the outbreak of COVID-19 in early March 2020, supermarkets around the world have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforc...
Gespeichert in:
| Veröffentlicht in: | PloS one 2021-04, Vol.16 (4), p.e0249821 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 4 |
| container_start_page | e0249821 |
| container_title | PloS one |
| container_volume | 16 |
| creator | Ying, Fabian O'Clery, Neave |
| description | Since the outbreak of COVID-19 in early March 2020, supermarkets around the world have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforcing a mandatory face covering policy. To quantitatively assess these mitigation methods, we formulate an agent-based model of customer movement in a supermarket (which we represent by a network) with a simple virus transmission model based on the amount of time a customer spends in close proximity to infectious customers (which we call the exposure time). We apply our model to synthetic store and shopping data to show how one can use our model to estimate exposure time and thereby the number of infections due to human-to-human contact in stores and how to model different store interventions. The source code is openly available under https://github.com/fabianying/covid19-supermarket-abm. We encourage retailers to use the model to find the most effective store policies that reduce virus transmission in stores and thereby protect both customers and staff. |
| doi_str_mv | 10.1371/journal.pone.0249821 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2510585677</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A657925362</galeid><doaj_id>oai_doaj_org_article_55bd8e37e86143a3b90d374e5a88900f</doaj_id><sourcerecordid>A657925362</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-26673cc9d23742bb13cccb18739472489db6978a55f88e427417b5c2205cf5193</originalsourceid><addsrcrecordid>eNqNkktv1DAUhSMEoqXwDxBEQkKwyOBH_NpUqobXoKKReHRrOY6T8ZCxBztB8O9xOmk1QV2gLJLY3z2-9_hk2VMIFhAz-Gbrh-BUt9h7ZxYAlYIjeC87hQKjgiKA7x99n2SPYtwCQDCn9GF2gjHHFEB2mn367GvTdda1-XJ9tXpbQJH3Qbm4szFa73Lr8jjsTdip8MP0MR_iyCqXq9a4vqhUNHW-G0UeZw8a1UXzZHqfZd_fv_u2_Fhcrj-slheXhaYC9QWilGGtRY0wK1FVwfSjK8gZFiVDJRd1RQXjipCGc1MiVkJWEY0QILohaaSz7PlBd9_5KCcbokQEAsIJZSwRqwNRe7WV-2BT83-kV1ZeL_jQShV6qzsjCalqbjAznMISK1wJUKe-DFGcCwCapHU-nTZUO1PrNHRQ3Ux0vuPsRrb-l-QAlwySJPBqEgj-52BiL5O1OnmunPHDdd8QjeSIvvgHvXu6iWpVGsC6xqdz9SgqLyhhAhFMUaIWd1Dpqc3O6pSZxqb1WcHrWUFievO7b9UQo1x9_fL_7Ppqzr48YjdGdf0m-m7oU7riHCwPoA4-xmCaW5MhkGPkb9yQY-TlFPlU9uz4gm6LbjKO_wKPg_iy</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2510585677</pqid></control><display><type>article</type><title>Modelling COVID-19 transmission in supermarkets using an agent-based model</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><source>Public Library of Science (PLoS)</source><creator>Ying, Fabian ; O'Clery, Neave</creator><creatorcontrib>Ying, Fabian ; O'Clery, Neave</creatorcontrib><description>Since the outbreak of COVID-19 in early March 2020, supermarkets around the world have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforcing a mandatory face covering policy. To quantitatively assess these mitigation methods, we formulate an agent-based model of customer movement in a supermarket (which we represent by a network) with a simple virus transmission model based on the amount of time a customer spends in close proximity to infectious customers (which we call the exposure time). We apply our model to synthetic store and shopping data to show how one can use our model to estimate exposure time and thereby the number of infections due to human-to-human contact in stores and how to model different store interventions. The source code is openly available under https://github.com/fabianying/covid19-supermarket-abm. We encourage retailers to use the model to find the most effective store policies that reduce virus transmission in stores and thereby protect both customers and staff.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0249821</identifier><identifier>PMID: 33836017</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aerodynamics ; Agent-based models ; Air flow ; Airflow models ; Biology and life sciences ; Computational fluid dynamics ; Computer and Information Sciences ; Computer applications ; Consumer Behavior ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - transmission ; Customers ; Differential equations ; Disease Outbreaks ; Disease transmission ; Distribution ; Drafting software ; Ecology and Environmental Sciences ; Editing ; Epidemics ; Fluid dynamics ; Health aspects ; Health risks ; Humans ; Hydrodynamics ; Infections ; Linear functions ; Mathematical models ; Medicine and Health Sciences ; Models, Biological ; Pandemics ; Partial differential equations ; Physical Sciences ; Prevention ; Quantitative research ; Research and Analysis Methods ; Retail stores ; SARS-CoV-2 ; Severe acute respiratory syndrome coronavirus 2 ; Shopping ; Shutdowns ; Simulation ; Social aspects ; Spatial analysis ; Supermarkets ; United Kingdom ; Viral diseases ; Viruses</subject><ispartof>PloS one, 2021-04, Vol.16 (4), p.e0249821</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Ying, O’Clery. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Ying, O’Clery 2021 Ying, O’Clery</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-26673cc9d23742bb13cccb18739472489db6978a55f88e427417b5c2205cf5193</citedby><cites>FETCH-LOGICAL-c692t-26673cc9d23742bb13cccb18739472489db6978a55f88e427417b5c2205cf5193</cites><orcidid>0000-0003-1876-8002</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034715/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034715/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33836017$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ying, Fabian</creatorcontrib><creatorcontrib>O'Clery, Neave</creatorcontrib><title>Modelling COVID-19 transmission in supermarkets using an agent-based model</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Since the outbreak of COVID-19 in early March 2020, supermarkets around the world have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforcing a mandatory face covering policy. To quantitatively assess these mitigation methods, we formulate an agent-based model of customer movement in a supermarket (which we represent by a network) with a simple virus transmission model based on the amount of time a customer spends in close proximity to infectious customers (which we call the exposure time). We apply our model to synthetic store and shopping data to show how one can use our model to estimate exposure time and thereby the number of infections due to human-to-human contact in stores and how to model different store interventions. The source code is openly available under https://github.com/fabianying/covid19-supermarket-abm. We encourage retailers to use the model to find the most effective store policies that reduce virus transmission in stores and thereby protect both customers and staff.</description><subject>Aerodynamics</subject><subject>Agent-based models</subject><subject>Air flow</subject><subject>Airflow models</subject><subject>Biology and life sciences</subject><subject>Computational fluid dynamics</subject><subject>Computer and Information Sciences</subject><subject>Computer applications</subject><subject>Consumer Behavior</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - transmission</subject><subject>Customers</subject><subject>Differential equations</subject><subject>Disease Outbreaks</subject><subject>Disease transmission</subject><subject>Distribution</subject><subject>Drafting software</subject><subject>Ecology and Environmental Sciences</subject><subject>Editing</subject><subject>Epidemics</subject><subject>Fluid dynamics</subject><subject>Health aspects</subject><subject>Health risks</subject><subject>Humans</subject><subject>Hydrodynamics</subject><subject>Infections</subject><subject>Linear functions</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Models, Biological</subject><subject>Pandemics</subject><subject>Partial differential equations</subject><subject>Physical Sciences</subject><subject>Prevention</subject><subject>Quantitative research</subject><subject>Research and Analysis Methods</subject><subject>Retail stores</subject><subject>SARS-CoV-2</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Shopping</subject><subject>Shutdowns</subject><subject>Simulation</subject><subject>Social aspects</subject><subject>Spatial analysis</subject><subject>Supermarkets</subject><subject>United Kingdom</subject><subject>Viral diseases</subject><subject>Viruses</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNkktv1DAUhSMEoqXwDxBEQkKwyOBH_NpUqobXoKKReHRrOY6T8ZCxBztB8O9xOmk1QV2gLJLY3z2-9_hk2VMIFhAz-Gbrh-BUt9h7ZxYAlYIjeC87hQKjgiKA7x99n2SPYtwCQDCn9GF2gjHHFEB2mn367GvTdda1-XJ9tXpbQJH3Qbm4szFa73Lr8jjsTdip8MP0MR_iyCqXq9a4vqhUNHW-G0UeZw8a1UXzZHqfZd_fv_u2_Fhcrj-slheXhaYC9QWilGGtRY0wK1FVwfSjK8gZFiVDJRd1RQXjipCGc1MiVkJWEY0QILohaaSz7PlBd9_5KCcbokQEAsIJZSwRqwNRe7WV-2BT83-kV1ZeL_jQShV6qzsjCalqbjAznMISK1wJUKe-DFGcCwCapHU-nTZUO1PrNHRQ3Ux0vuPsRrb-l-QAlwySJPBqEgj-52BiL5O1OnmunPHDdd8QjeSIvvgHvXu6iWpVGsC6xqdz9SgqLyhhAhFMUaIWd1Dpqc3O6pSZxqb1WcHrWUFievO7b9UQo1x9_fL_7Ppqzr48YjdGdf0m-m7oU7riHCwPoA4-xmCaW5MhkGPkb9yQY-TlFPlU9uz4gm6LbjKO_wKPg_iy</recordid><startdate>20210409</startdate><enddate>20210409</enddate><creator>Ying, Fabian</creator><creator>O'Clery, Neave</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>COVID</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-1876-8002</orcidid></search><sort><creationdate>20210409</creationdate><title>Modelling COVID-19 transmission in supermarkets using an agent-based model</title><author>Ying, Fabian ; O'Clery, Neave</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-26673cc9d23742bb13cccb18739472489db6978a55f88e427417b5c2205cf5193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aerodynamics</topic><topic>Agent-based models</topic><topic>Air flow</topic><topic>Airflow models</topic><topic>Biology and life sciences</topic><topic>Computational fluid dynamics</topic><topic>Computer and Information Sciences</topic><topic>Computer applications</topic><topic>Consumer Behavior</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>COVID-19 - transmission</topic><topic>Customers</topic><topic>Differential equations</topic><topic>Disease Outbreaks</topic><topic>Disease transmission</topic><topic>Distribution</topic><topic>Drafting software</topic><topic>Ecology and Environmental Sciences</topic><topic>Editing</topic><topic>Epidemics</topic><topic>Fluid dynamics</topic><topic>Health aspects</topic><topic>Health risks</topic><topic>Humans</topic><topic>Hydrodynamics</topic><topic>Infections</topic><topic>Linear functions</topic><topic>Mathematical models</topic><topic>Medicine and Health Sciences</topic><topic>Models, Biological</topic><topic>Pandemics</topic><topic>Partial differential equations</topic><topic>Physical Sciences</topic><topic>Prevention</topic><topic>Quantitative research</topic><topic>Research and Analysis Methods</topic><topic>Retail stores</topic><topic>SARS-CoV-2</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Shopping</topic><topic>Shutdowns</topic><topic>Simulation</topic><topic>Social aspects</topic><topic>Spatial analysis</topic><topic>Supermarkets</topic><topic>United Kingdom</topic><topic>Viral diseases</topic><topic>Viruses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ying, Fabian</creatorcontrib><creatorcontrib>O'Clery, Neave</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ying, Fabian</au><au>O'Clery, Neave</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modelling COVID-19 transmission in supermarkets using an agent-based model</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-04-09</date><risdate>2021</risdate><volume>16</volume><issue>4</issue><spage>e0249821</spage><pages>e0249821-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Since the outbreak of COVID-19 in early March 2020, supermarkets around the world have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforcing a mandatory face covering policy. To quantitatively assess these mitigation methods, we formulate an agent-based model of customer movement in a supermarket (which we represent by a network) with a simple virus transmission model based on the amount of time a customer spends in close proximity to infectious customers (which we call the exposure time). We apply our model to synthetic store and shopping data to show how one can use our model to estimate exposure time and thereby the number of infections due to human-to-human contact in stores and how to model different store interventions. The source code is openly available under https://github.com/fabianying/covid19-supermarket-abm. We encourage retailers to use the model to find the most effective store policies that reduce virus transmission in stores and thereby protect both customers and staff.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33836017</pmid><doi>10.1371/journal.pone.0249821</doi><tpages>e0249821</tpages><orcidid>https://orcid.org/0000-0003-1876-8002</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2021-04, Vol.16 (4), p.e0249821 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2510585677 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Aerodynamics Agent-based models Air flow Airflow models Biology and life sciences Computational fluid dynamics Computer and Information Sciences Computer applications Consumer Behavior Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - transmission Customers Differential equations Disease Outbreaks Disease transmission Distribution Drafting software Ecology and Environmental Sciences Editing Epidemics Fluid dynamics Health aspects Health risks Humans Hydrodynamics Infections Linear functions Mathematical models Medicine and Health Sciences Models, Biological Pandemics Partial differential equations Physical Sciences Prevention Quantitative research Research and Analysis Methods Retail stores SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 Shopping Shutdowns Simulation Social aspects Spatial analysis Supermarkets United Kingdom Viral diseases Viruses |
| title | Modelling COVID-19 transmission in supermarkets using an agent-based model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T15%3A15%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Modelling%20COVID-19%20transmission%20in%20supermarkets%20using%20an%20agent-based%20model&rft.jtitle=PloS%20one&rft.au=Ying,%20Fabian&rft.date=2021-04-09&rft.volume=16&rft.issue=4&rft.spage=e0249821&rft.pages=e0249821-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0249821&rft_dat=%3Cgale_plos_%3EA657925362%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2510585677&rft_id=info:pmid/33836017&rft_galeid=A657925362&rft_doaj_id=oai_doaj_org_article_55bd8e37e86143a3b90d374e5a88900f&rfr_iscdi=true |