Modelling optimal vaccination strategy for SARS-CoV-2 in the UK
The COVID-19 outbreak has highlighted our vulnerability to novel infections. Faced with this threat and no effective treatment, in line with many other countries, the UK adopted enforced social distancing (lockdown) to reduce transmission-successfully reducing the reproductive number R below one. Ho...
Gespeichert in:
| Veröffentlicht in: | PLoS computational biology 2021-05, Vol.17 (5), p.e1008849-e1008849 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e1008849 |
|---|---|
| container_issue | 5 |
| container_start_page | e1008849 |
| container_title | PLoS computational biology |
| container_volume | 17 |
| creator | Moore, Sam Hill, Edward M Dyson, Louise Tildesley, Michael J Keeling, Matt J |
| description | The COVID-19 outbreak has highlighted our vulnerability to novel infections. Faced with this threat and no effective treatment, in line with many other countries, the UK adopted enforced social distancing (lockdown) to reduce transmission-successfully reducing the reproductive number R below one. However, given the large pool of susceptible individuals that remain, complete relaxation of controls is likely to generate a substantial further outbreak. Vaccination remains the only foreseeable means of both containing the infection and returning to normal interactions and behaviour. Here, we consider the optimal targeting of vaccination within the UK, with the aim of minimising future deaths or quality adjusted life year (QALY) losses. We show that, for a range of assumptions on the action and efficacy of the vaccine, targeting older age groups first is optimal and may be sufficient to stem the epidemic if the vaccine prevents transmission as well as disease. |
| doi_str_mv | 10.1371/journal.pcbi.1008849 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2541865373</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A665274642</galeid><doaj_id>oai_doaj_org_article_69dfb5730a2b4204a7b0abdf7620c0af</doaj_id><sourcerecordid>A665274642</sourcerecordid><originalsourceid>FETCH-LOGICAL-c661t-5221cb54451e919593531913ea29d08853d2085c67a92c30aa9aed69fd48789f3</originalsourceid><addsrcrecordid>eNqVks1u1DAUhSMEoqXwBggisYFFBv873oBGowIjCkgdytZybCf1KBMPsVO1b4_DpFWDukFe2LK_e67P0c2ylxAsIObw_dYPfafaxV5XbgEBKEsiHmXHkFJccEzLx_fOR9mzELYApKNgT7MjjAVlXMDj7OM3b2zbuq7J_T66nWrzK6W161R0vstD7FW0zU1e-z7fLM83xcr_KlDuujxe2vzi6_PsSa3aYF9M-0l28en05-pLcfbj83q1PCs0YzAWFCGoK0oIhVZAQQWmGAqIrULCpK9TbBAoqWZcCaQxUEooa5ioDSl5KWp8kr0-6O5bH-TkPUhECSwZxRwnYn0gjFdbue-Tl_5GeuXk3wvfN1L10enWSiZMXVGe2qCKIEAUr4CqTM0ZAhqosduHqdtQ7azRtks5tDPR-UvnLmXjr2QJQXJXJoG3k0Dvfw82RLlzQaegVWf9MP4bEUxhSiGhb_5BH3Y3UY1KBlxX-9RXj6JyyRhFnDCCErV4gErL2J3TvrO1S_ezgnezgsREex0bNYQg15vz_2C_z1lyYHXvQ-htfZcdBHIc31uTchxfOY1vKnt1P_e7ott5xX8AlmrnuQ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2541865373</pqid></control><display><type>article</type><title>Modelling optimal vaccination strategy for SARS-CoV-2 in the UK</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><creator>Moore, Sam ; Hill, Edward M ; Dyson, Louise ; Tildesley, Michael J ; Keeling, Matt J</creator><contributor>Perkins, Alex</contributor><creatorcontrib>Moore, Sam ; Hill, Edward M ; Dyson, Louise ; Tildesley, Michael J ; Keeling, Matt J ; Perkins, Alex</creatorcontrib><description>The COVID-19 outbreak has highlighted our vulnerability to novel infections. Faced with this threat and no effective treatment, in line with many other countries, the UK adopted enforced social distancing (lockdown) to reduce transmission-successfully reducing the reproductive number R below one. However, given the large pool of susceptible individuals that remain, complete relaxation of controls is likely to generate a substantial further outbreak. Vaccination remains the only foreseeable means of both containing the infection and returning to normal interactions and behaviour. Here, we consider the optimal targeting of vaccination within the UK, with the aim of minimising future deaths or quality adjusted life year (QALY) losses. We show that, for a range of assumptions on the action and efficacy of the vaccine, targeting older age groups first is optimal and may be sufficient to stem the epidemic if the vaccine prevents transmission as well as disease.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1008849</identifier><identifier>PMID: 33956791</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Asymptomatic ; Biology and Life Sciences ; Communicable Disease Control ; Computer Simulation ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - immunology ; COVID-19 - prevention & control ; COVID-19 - transmission ; COVID-19 vaccines ; COVID-19 Vaccines - administration & dosage ; Disease control ; Disease transmission ; Hospitals ; Human performance ; Human populations ; Humans ; Immunity, Herd ; Infections ; Mathematical models ; Mathematical optimization ; Medicine and Health Sciences ; Mortality ; Ordinary differential equations ; Pandemics ; People and Places ; Public health ; Severe acute respiratory syndrome coronavirus 2 ; Signs and symptoms ; United Kingdom - epidemiology ; Vaccination ; Vaccines ; Viral diseases</subject><ispartof>PLoS computational biology, 2021-05, Vol.17 (5), p.e1008849-e1008849</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Moore et al. 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 Moore et al 2021 Moore et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c661t-5221cb54451e919593531913ea29d08853d2085c67a92c30aa9aed69fd48789f3</citedby><cites>FETCH-LOGICAL-c661t-5221cb54451e919593531913ea29d08853d2085c67a92c30aa9aed69fd48789f3</cites><orcidid>0000-0001-7786-5342 ; 0000-0002-2992-2004 ; 0000-0003-4639-4765 ; 0000-0002-6875-7232 ; 0000-0001-9788-4858</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/PMC8101958/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101958/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33956791$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Perkins, Alex</contributor><creatorcontrib>Moore, Sam</creatorcontrib><creatorcontrib>Hill, Edward M</creatorcontrib><creatorcontrib>Dyson, Louise</creatorcontrib><creatorcontrib>Tildesley, Michael J</creatorcontrib><creatorcontrib>Keeling, Matt J</creatorcontrib><title>Modelling optimal vaccination strategy for SARS-CoV-2 in the UK</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>The COVID-19 outbreak has highlighted our vulnerability to novel infections. Faced with this threat and no effective treatment, in line with many other countries, the UK adopted enforced social distancing (lockdown) to reduce transmission-successfully reducing the reproductive number R below one. However, given the large pool of susceptible individuals that remain, complete relaxation of controls is likely to generate a substantial further outbreak. Vaccination remains the only foreseeable means of both containing the infection and returning to normal interactions and behaviour. Here, we consider the optimal targeting of vaccination within the UK, with the aim of minimising future deaths or quality adjusted life year (QALY) losses. We show that, for a range of assumptions on the action and efficacy of the vaccine, targeting older age groups first is optimal and may be sufficient to stem the epidemic if the vaccine prevents transmission as well as disease.</description><subject>Age</subject><subject>Asymptomatic</subject><subject>Biology and Life Sciences</subject><subject>Communicable Disease Control</subject><subject>Computer Simulation</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - immunology</subject><subject>COVID-19 - prevention & control</subject><subject>COVID-19 - transmission</subject><subject>COVID-19 vaccines</subject><subject>COVID-19 Vaccines - administration & dosage</subject><subject>Disease control</subject><subject>Disease transmission</subject><subject>Hospitals</subject><subject>Human performance</subject><subject>Human populations</subject><subject>Humans</subject><subject>Immunity, Herd</subject><subject>Infections</subject><subject>Mathematical models</subject><subject>Mathematical optimization</subject><subject>Medicine and Health Sciences</subject><subject>Mortality</subject><subject>Ordinary differential equations</subject><subject>Pandemics</subject><subject>People and Places</subject><subject>Public health</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Signs and symptoms</subject><subject>United Kingdom - epidemiology</subject><subject>Vaccination</subject><subject>Vaccines</subject><subject>Viral diseases</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqVks1u1DAUhSMEoqXwBggisYFFBv873oBGowIjCkgdytZybCf1KBMPsVO1b4_DpFWDukFe2LK_e67P0c2ylxAsIObw_dYPfafaxV5XbgEBKEsiHmXHkFJccEzLx_fOR9mzELYApKNgT7MjjAVlXMDj7OM3b2zbuq7J_T66nWrzK6W161R0vstD7FW0zU1e-z7fLM83xcr_KlDuujxe2vzi6_PsSa3aYF9M-0l28en05-pLcfbj83q1PCs0YzAWFCGoK0oIhVZAQQWmGAqIrULCpK9TbBAoqWZcCaQxUEooa5ioDSl5KWp8kr0-6O5bH-TkPUhECSwZxRwnYn0gjFdbue-Tl_5GeuXk3wvfN1L10enWSiZMXVGe2qCKIEAUr4CqTM0ZAhqosduHqdtQ7azRtks5tDPR-UvnLmXjr2QJQXJXJoG3k0Dvfw82RLlzQaegVWf9MP4bEUxhSiGhb_5BH3Y3UY1KBlxX-9RXj6JyyRhFnDCCErV4gErL2J3TvrO1S_ezgnezgsREex0bNYQg15vz_2C_z1lyYHXvQ-htfZcdBHIc31uTchxfOY1vKnt1P_e7ott5xX8AlmrnuQ</recordid><startdate>20210501</startdate><enddate>20210501</enddate><creator>Moore, Sam</creator><creator>Hill, Edward M</creator><creator>Dyson, Louise</creator><creator>Tildesley, Michael J</creator><creator>Keeling, Matt J</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>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7786-5342</orcidid><orcidid>https://orcid.org/0000-0002-2992-2004</orcidid><orcidid>https://orcid.org/0000-0003-4639-4765</orcidid><orcidid>https://orcid.org/0000-0002-6875-7232</orcidid><orcidid>https://orcid.org/0000-0001-9788-4858</orcidid></search><sort><creationdate>20210501</creationdate><title>Modelling optimal vaccination strategy for SARS-CoV-2 in the UK</title><author>Moore, Sam ; Hill, Edward M ; Dyson, Louise ; Tildesley, Michael J ; Keeling, Matt J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c661t-5221cb54451e919593531913ea29d08853d2085c67a92c30aa9aed69fd48789f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age</topic><topic>Asymptomatic</topic><topic>Biology and Life Sciences</topic><topic>Communicable Disease Control</topic><topic>Computer Simulation</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>COVID-19 - immunology</topic><topic>COVID-19 - prevention & control</topic><topic>COVID-19 - transmission</topic><topic>COVID-19 vaccines</topic><topic>COVID-19 Vaccines - administration & dosage</topic><topic>Disease control</topic><topic>Disease transmission</topic><topic>Hospitals</topic><topic>Human performance</topic><topic>Human populations</topic><topic>Humans</topic><topic>Immunity, Herd</topic><topic>Infections</topic><topic>Mathematical models</topic><topic>Mathematical optimization</topic><topic>Medicine and Health Sciences</topic><topic>Mortality</topic><topic>Ordinary differential equations</topic><topic>Pandemics</topic><topic>People and Places</topic><topic>Public health</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Signs and symptoms</topic><topic>United Kingdom - epidemiology</topic><topic>Vaccination</topic><topic>Vaccines</topic><topic>Viral diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moore, Sam</creatorcontrib><creatorcontrib>Hill, Edward M</creatorcontrib><creatorcontrib>Dyson, Louise</creatorcontrib><creatorcontrib>Tildesley, Michael J</creatorcontrib><creatorcontrib>Keeling, Matt J</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: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</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>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace 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>ProQuest One Community College</collection><collection>Coronavirus Research Database</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>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</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>ProQuest Central Basic</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 computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moore, Sam</au><au>Hill, Edward M</au><au>Dyson, Louise</au><au>Tildesley, Michael J</au><au>Keeling, Matt J</au><au>Perkins, Alex</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modelling optimal vaccination strategy for SARS-CoV-2 in the UK</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2021-05-01</date><risdate>2021</risdate><volume>17</volume><issue>5</issue><spage>e1008849</spage><epage>e1008849</epage><pages>e1008849-e1008849</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>The COVID-19 outbreak has highlighted our vulnerability to novel infections. Faced with this threat and no effective treatment, in line with many other countries, the UK adopted enforced social distancing (lockdown) to reduce transmission-successfully reducing the reproductive number R below one. However, given the large pool of susceptible individuals that remain, complete relaxation of controls is likely to generate a substantial further outbreak. Vaccination remains the only foreseeable means of both containing the infection and returning to normal interactions and behaviour. Here, we consider the optimal targeting of vaccination within the UK, with the aim of minimising future deaths or quality adjusted life year (QALY) losses. We show that, for a range of assumptions on the action and efficacy of the vaccine, targeting older age groups first is optimal and may be sufficient to stem the epidemic if the vaccine prevents transmission as well as disease.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33956791</pmid><doi>10.1371/journal.pcbi.1008849</doi><orcidid>https://orcid.org/0000-0001-7786-5342</orcidid><orcidid>https://orcid.org/0000-0002-2992-2004</orcidid><orcidid>https://orcid.org/0000-0003-4639-4765</orcidid><orcidid>https://orcid.org/0000-0002-6875-7232</orcidid><orcidid>https://orcid.org/0000-0001-9788-4858</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1553-7358 |
| ispartof | PLoS computational biology, 2021-05, Vol.17 (5), p.e1008849-e1008849 |
| issn | 1553-7358 1553-734X 1553-7358 |
| language | eng |
| recordid | cdi_plos_journals_2541865373 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Age Asymptomatic Biology and Life Sciences Communicable Disease Control Computer Simulation Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - immunology COVID-19 - prevention & control COVID-19 - transmission COVID-19 vaccines COVID-19 Vaccines - administration & dosage Disease control Disease transmission Hospitals Human performance Human populations Humans Immunity, Herd Infections Mathematical models Mathematical optimization Medicine and Health Sciences Mortality Ordinary differential equations Pandemics People and Places Public health Severe acute respiratory syndrome coronavirus 2 Signs and symptoms United Kingdom - epidemiology Vaccination Vaccines Viral diseases |
| title | Modelling optimal vaccination strategy for SARS-CoV-2 in the UK |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T04%3A06%3A47IST&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%20optimal%20vaccination%20strategy%20for%20SARS-CoV-2%20in%20the%20UK&rft.jtitle=PLoS%20computational%20biology&rft.au=Moore,%20Sam&rft.date=2021-05-01&rft.volume=17&rft.issue=5&rft.spage=e1008849&rft.epage=e1008849&rft.pages=e1008849-e1008849&rft.issn=1553-7358&rft.eissn=1553-7358&rft_id=info:doi/10.1371/journal.pcbi.1008849&rft_dat=%3Cgale_plos_%3EA665274642%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=2541865373&rft_id=info:pmid/33956791&rft_galeid=A665274642&rft_doaj_id=oai_doaj_org_article_69dfb5730a2b4204a7b0abdf7620c0af&rfr_iscdi=true |