Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples
Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. During the first emergency phase of the epidemic, RT-qPCR on nasopharyngeal (NP) swabs, which is the most reliable technique to detect ongoing infections, exhibited limitation...
Gespeichert in:
Veröffentlicht in: | PloS one 2021-05, Vol.16 (5), p.e0251589 |
---|---|
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 | 5 |
container_start_page | e0251589 |
container_title | PloS one |
container_volume | 16 |
creator | Baccini, Michela Rocco, Emilia Paganini, Irene Mattei, Alessandra Sani, Cristina Vannucci, Giulia Bisanzi, Simonetta Burroni, Elena Peluso, Marco Munnia, Armelle Cellai, Filippo Pompeo, Giampaolo Micio, Laura Viti, Jessica Mealli, Fabrizia Carozzi, Francesca Maria |
description | Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. During the first emergency phase of the epidemic, RT-qPCR on nasopharyngeal (NP) swabs, which is the most reliable technique to detect ongoing infections, exhibited limitations due to availability of reagents and budget constraints. This stressed the need to develop screening procedures that require fewer resources and are suitable to be extended to larger portions of the population. RT-qPCR on pooled samples from individual NP swabs seems to be a promising technique to improve surveillance. We performed preliminary experimental analyses aimed to investigate the performance of pool testing on samples with low viral load and we evaluated through Monte Carlo (MC) simulations alternative screening protocols based on sample pooling, tailored to contexts characterized by different infection prevalence. We focused on the role of pool size and the opportunity to develop strategies that take advantage of natural clustering structures in the population, e.g. families, school classes, hospital rooms. Despite the use of a limited number of specimens, our results suggest that, while high viral load samples seem to be detectable even in a pool with 29 negative samples, positive specimens with low viral load may be masked by the negative samples, unless smaller pools are used. The results of MC simulations confirm that pool testing is useful in contexts where the infection prevalence is low. The gain of pool testing in saving resources can be very high, and can be optimized by selecting appropriate group sizes. Exploiting natural groups makes the definition of larger pools convenient and potentially overcomes the issue of low viral load samples by increasing the probability of identifying more than one positive in the same pool. |
doi_str_mv | 10.1371/journal.pone.0251589 |
format | Article |
fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_pubmed_primary_34003878</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A662172438</galeid><doaj_id>oai_doaj_org_article_1993935c0b5a4819a7c68575cef7ff9d</doaj_id><sourcerecordid>A662172438</sourcerecordid><originalsourceid>FETCH-LOGICAL-c585t-929ba097bd233c3fcf2ccac9763b5ca4e006f994d6eccd2e76dcec53c203fa123</originalsourceid><addsrcrecordid>eNptkl2P1CAUhhujcdfVf2CUxMR405FCKeCFyWTixyabrHHVW0IpzDBhShfoGH-E_1nqdDczxhs-n_NyzsspiucVXFSYVm-3fgy9dIvB93oBEakI4w-K84pjVDYI4odH67PiSYxbCAlmTfO4OMM1hJhRdl78_uK9A0nHZPs18D0Isu_8DuQR9DKNQTqg3BiTDhF4A2zf2b3tRuniO3A9JLuzUSabA_PlzfLrTbnyP0oE4hj22jone6VzEEgbDYago572GXX-J9jbSd152YEod4PT8WnxyGRl_WyeL4rvHz98W30ur64_Xa6WV6UijKSSI95KyGnbIYwVNsogpaTitMEtUbLWEDaG87prtFId0rTplFYEq2yFkRXCF8XLg-7gfBSzk1EgghijGDKeicsD0Xm5FUOwOxl-CS-t-Hvgw1rIkKxyWlScY46Jgi2RNau4pKphhBKlDTWGd1nr_fza2O50TqVPufAT0dOb3m7E2u8FqzDkDckCb2aB4G_H_Fciu6705K724yFvDhmlLKOv_kH_X91MrWUuwPbG53fVJCqWTYMqimo8ab0-ojZaurSJ3o3Td8dTsD6AKvgYgzb3tVVQTM16l4SYmlXMzZrDXhz7ch901534Dz2H6NE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2528873089</pqid></control><display><type>article</type><title>Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples</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>Baccini, Michela ; Rocco, Emilia ; Paganini, Irene ; Mattei, Alessandra ; Sani, Cristina ; Vannucci, Giulia ; Bisanzi, Simonetta ; Burroni, Elena ; Peluso, Marco ; Munnia, Armelle ; Cellai, Filippo ; Pompeo, Giampaolo ; Micio, Laura ; Viti, Jessica ; Mealli, Fabrizia ; Carozzi, Francesca Maria</creator><creatorcontrib>Baccini, Michela ; Rocco, Emilia ; Paganini, Irene ; Mattei, Alessandra ; Sani, Cristina ; Vannucci, Giulia ; Bisanzi, Simonetta ; Burroni, Elena ; Peluso, Marco ; Munnia, Armelle ; Cellai, Filippo ; Pompeo, Giampaolo ; Micio, Laura ; Viti, Jessica ; Mealli, Fabrizia ; Carozzi, Francesca Maria</creatorcontrib><description>Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. During the first emergency phase of the epidemic, RT-qPCR on nasopharyngeal (NP) swabs, which is the most reliable technique to detect ongoing infections, exhibited limitations due to availability of reagents and budget constraints. This stressed the need to develop screening procedures that require fewer resources and are suitable to be extended to larger portions of the population. RT-qPCR on pooled samples from individual NP swabs seems to be a promising technique to improve surveillance. We performed preliminary experimental analyses aimed to investigate the performance of pool testing on samples with low viral load and we evaluated through Monte Carlo (MC) simulations alternative screening protocols based on sample pooling, tailored to contexts characterized by different infection prevalence. We focused on the role of pool size and the opportunity to develop strategies that take advantage of natural clustering structures in the population, e.g. families, school classes, hospital rooms. Despite the use of a limited number of specimens, our results suggest that, while high viral load samples seem to be detectable even in a pool with 29 negative samples, positive specimens with low viral load may be masked by the negative samples, unless smaller pools are used. The results of MC simulations confirm that pool testing is useful in contexts where the infection prevalence is low. The gain of pool testing in saving resources can be very high, and can be optimized by selecting appropriate group sizes. Exploiting natural groups makes the definition of larger pools convenient and potentially overcomes the issue of low viral load samples by increasing the probability of identifying more than one positive in the same pool.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0251589</identifier><identifier>PMID: 34003878</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Asymptomatic ; Biology and Life Sciences ; Cancer ; Computer programs ; Computer science ; Coronaviruses ; COVID-19 ; COVID-19 - diagnosis ; COVID-19 - virology ; COVID-19 Nucleic Acid Testing - methods ; Disease prevention ; DNA-directed RNA polymerase ; Drafting software ; Editing ; Electronic mail ; Evaluation ; Humans ; Infections ; Laboratories ; Mail ; Medical screening ; Medicine and Health Sciences ; Methodology ; Monte Carlo Method ; Nasopharynx - virology ; Nucleocapsids ; Optimization ; Pandemics ; Performance evaluation ; Physical sciences ; Polymerase chain reaction ; Prevention ; Real-Time Polymerase Chain Reaction ; Research and analysis methods ; Respiratory diseases ; Respiratory tract ; Reviews ; Ribonucleic acid ; RNA ; RNA polymerase ; RNA, Viral - analysis ; RNA-directed RNA polymerase ; Samples ; SARS-CoV-2 - genetics ; SARS-CoV-2 - isolation & purification ; Severe acute respiratory syndrome ; Severe acute respiratory syndrome coronavirus 2 ; Software ; Specimen Handling ; Statistical analysis ; Statistical methods ; Statistics ; Viral diseases ; Viral Load</subject><ispartof>PloS one, 2021-05, Vol.16 (5), p.e0251589</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Baccini 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 Baccini et al 2021 Baccini et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c585t-929ba097bd233c3fcf2ccac9763b5ca4e006f994d6eccd2e76dcec53c203fa123</citedby><cites>FETCH-LOGICAL-c585t-929ba097bd233c3fcf2ccac9763b5ca4e006f994d6eccd2e76dcec53c203fa123</cites><orcidid>0000-0002-1076-9249 ; 0000-0002-4365-427X ; 0000-0001-6867-0244</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/PMC8130965/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130965/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34003878$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Baccini, Michela</creatorcontrib><creatorcontrib>Rocco, Emilia</creatorcontrib><creatorcontrib>Paganini, Irene</creatorcontrib><creatorcontrib>Mattei, Alessandra</creatorcontrib><creatorcontrib>Sani, Cristina</creatorcontrib><creatorcontrib>Vannucci, Giulia</creatorcontrib><creatorcontrib>Bisanzi, Simonetta</creatorcontrib><creatorcontrib>Burroni, Elena</creatorcontrib><creatorcontrib>Peluso, Marco</creatorcontrib><creatorcontrib>Munnia, Armelle</creatorcontrib><creatorcontrib>Cellai, Filippo</creatorcontrib><creatorcontrib>Pompeo, Giampaolo</creatorcontrib><creatorcontrib>Micio, Laura</creatorcontrib><creatorcontrib>Viti, Jessica</creatorcontrib><creatorcontrib>Mealli, Fabrizia</creatorcontrib><creatorcontrib>Carozzi, Francesca Maria</creatorcontrib><title>Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. During the first emergency phase of the epidemic, RT-qPCR on nasopharyngeal (NP) swabs, which is the most reliable technique to detect ongoing infections, exhibited limitations due to availability of reagents and budget constraints. This stressed the need to develop screening procedures that require fewer resources and are suitable to be extended to larger portions of the population. RT-qPCR on pooled samples from individual NP swabs seems to be a promising technique to improve surveillance. We performed preliminary experimental analyses aimed to investigate the performance of pool testing on samples with low viral load and we evaluated through Monte Carlo (MC) simulations alternative screening protocols based on sample pooling, tailored to contexts characterized by different infection prevalence. We focused on the role of pool size and the opportunity to develop strategies that take advantage of natural clustering structures in the population, e.g. families, school classes, hospital rooms. Despite the use of a limited number of specimens, our results suggest that, while high viral load samples seem to be detectable even in a pool with 29 negative samples, positive specimens with low viral load may be masked by the negative samples, unless smaller pools are used. The results of MC simulations confirm that pool testing is useful in contexts where the infection prevalence is low. The gain of pool testing in saving resources can be very high, and can be optimized by selecting appropriate group sizes. Exploiting natural groups makes the definition of larger pools convenient and potentially overcomes the issue of low viral load samples by increasing the probability of identifying more than one positive in the same pool.</description><subject>Asymptomatic</subject><subject>Biology and Life Sciences</subject><subject>Cancer</subject><subject>Computer programs</subject><subject>Computer science</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - diagnosis</subject><subject>COVID-19 - virology</subject><subject>COVID-19 Nucleic Acid Testing - methods</subject><subject>Disease prevention</subject><subject>DNA-directed RNA polymerase</subject><subject>Drafting software</subject><subject>Editing</subject><subject>Electronic mail</subject><subject>Evaluation</subject><subject>Humans</subject><subject>Infections</subject><subject>Laboratories</subject><subject>Mail</subject><subject>Medical screening</subject><subject>Medicine and Health Sciences</subject><subject>Methodology</subject><subject>Monte Carlo Method</subject><subject>Nasopharynx - virology</subject><subject>Nucleocapsids</subject><subject>Optimization</subject><subject>Pandemics</subject><subject>Performance evaluation</subject><subject>Physical sciences</subject><subject>Polymerase chain reaction</subject><subject>Prevention</subject><subject>Real-Time Polymerase Chain Reaction</subject><subject>Research and analysis methods</subject><subject>Respiratory diseases</subject><subject>Respiratory tract</subject><subject>Reviews</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA polymerase</subject><subject>RNA, Viral - analysis</subject><subject>RNA-directed RNA polymerase</subject><subject>Samples</subject><subject>SARS-CoV-2 - genetics</subject><subject>SARS-CoV-2 - isolation & purification</subject><subject>Severe acute respiratory syndrome</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Software</subject><subject>Specimen Handling</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Viral diseases</subject><subject>Viral Load</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>eNptkl2P1CAUhhujcdfVf2CUxMR405FCKeCFyWTixyabrHHVW0IpzDBhShfoGH-E_1nqdDczxhs-n_NyzsspiucVXFSYVm-3fgy9dIvB93oBEakI4w-K84pjVDYI4odH67PiSYxbCAlmTfO4OMM1hJhRdl78_uK9A0nHZPs18D0Isu_8DuQR9DKNQTqg3BiTDhF4A2zf2b3tRuniO3A9JLuzUSabA_PlzfLrTbnyP0oE4hj22jone6VzEEgbDYago572GXX-J9jbSd152YEod4PT8WnxyGRl_WyeL4rvHz98W30ur64_Xa6WV6UijKSSI95KyGnbIYwVNsogpaTitMEtUbLWEDaG87prtFId0rTplFYEq2yFkRXCF8XLg-7gfBSzk1EgghijGDKeicsD0Xm5FUOwOxl-CS-t-Hvgw1rIkKxyWlScY46Jgi2RNau4pKphhBKlDTWGd1nr_fza2O50TqVPufAT0dOb3m7E2u8FqzDkDckCb2aB4G_H_Fciu6705K724yFvDhmlLKOv_kH_X91MrWUuwPbG53fVJCqWTYMqimo8ab0-ojZaurSJ3o3Td8dTsD6AKvgYgzb3tVVQTM16l4SYmlXMzZrDXhz7ch901534Dz2H6NE</recordid><startdate>20210518</startdate><enddate>20210518</enddate><creator>Baccini, Michela</creator><creator>Rocco, Emilia</creator><creator>Paganini, Irene</creator><creator>Mattei, Alessandra</creator><creator>Sani, Cristina</creator><creator>Vannucci, Giulia</creator><creator>Bisanzi, Simonetta</creator><creator>Burroni, Elena</creator><creator>Peluso, Marco</creator><creator>Munnia, Armelle</creator><creator>Cellai, Filippo</creator><creator>Pompeo, Giampaolo</creator><creator>Micio, Laura</creator><creator>Viti, Jessica</creator><creator>Mealli, Fabrizia</creator><creator>Carozzi, Francesca Maria</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>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>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQGLB</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/0000-0002-1076-9249</orcidid><orcidid>https://orcid.org/0000-0002-4365-427X</orcidid><orcidid>https://orcid.org/0000-0001-6867-0244</orcidid></search><sort><creationdate>20210518</creationdate><title>Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples</title><author>Baccini, Michela ; Rocco, Emilia ; Paganini, Irene ; Mattei, Alessandra ; Sani, Cristina ; Vannucci, Giulia ; Bisanzi, Simonetta ; Burroni, Elena ; Peluso, Marco ; Munnia, Armelle ; Cellai, Filippo ; Pompeo, Giampaolo ; Micio, Laura ; Viti, Jessica ; Mealli, Fabrizia ; Carozzi, Francesca Maria</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c585t-929ba097bd233c3fcf2ccac9763b5ca4e006f994d6eccd2e76dcec53c203fa123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Asymptomatic</topic><topic>Biology and Life Sciences</topic><topic>Cancer</topic><topic>Computer programs</topic><topic>Computer science</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - diagnosis</topic><topic>COVID-19 - virology</topic><topic>COVID-19 Nucleic Acid Testing - methods</topic><topic>Disease prevention</topic><topic>DNA-directed RNA polymerase</topic><topic>Drafting software</topic><topic>Editing</topic><topic>Electronic mail</topic><topic>Evaluation</topic><topic>Humans</topic><topic>Infections</topic><topic>Laboratories</topic><topic>Mail</topic><topic>Medical screening</topic><topic>Medicine and Health Sciences</topic><topic>Methodology</topic><topic>Monte Carlo Method</topic><topic>Nasopharynx - virology</topic><topic>Nucleocapsids</topic><topic>Optimization</topic><topic>Pandemics</topic><topic>Performance evaluation</topic><topic>Physical sciences</topic><topic>Polymerase chain reaction</topic><topic>Prevention</topic><topic>Real-Time Polymerase Chain Reaction</topic><topic>Research and analysis methods</topic><topic>Respiratory diseases</topic><topic>Respiratory tract</topic><topic>Reviews</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>RNA polymerase</topic><topic>RNA, Viral - analysis</topic><topic>RNA-directed RNA polymerase</topic><topic>Samples</topic><topic>SARS-CoV-2 - genetics</topic><topic>SARS-CoV-2 - isolation & purification</topic><topic>Severe acute respiratory syndrome</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Software</topic><topic>Specimen Handling</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Viral diseases</topic><topic>Viral Load</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baccini, Michela</creatorcontrib><creatorcontrib>Rocco, Emilia</creatorcontrib><creatorcontrib>Paganini, Irene</creatorcontrib><creatorcontrib>Mattei, Alessandra</creatorcontrib><creatorcontrib>Sani, Cristina</creatorcontrib><creatorcontrib>Vannucci, Giulia</creatorcontrib><creatorcontrib>Bisanzi, Simonetta</creatorcontrib><creatorcontrib>Burroni, Elena</creatorcontrib><creatorcontrib>Peluso, Marco</creatorcontrib><creatorcontrib>Munnia, Armelle</creatorcontrib><creatorcontrib>Cellai, Filippo</creatorcontrib><creatorcontrib>Pompeo, Giampaolo</creatorcontrib><creatorcontrib>Micio, Laura</creatorcontrib><creatorcontrib>Viti, Jessica</creatorcontrib><creatorcontrib>Mealli, Fabrizia</creatorcontrib><creatorcontrib>Carozzi, Francesca Maria</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</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>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest Health & Medical Research Collection</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Health & Nursing</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</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>Baccini, Michela</au><au>Rocco, Emilia</au><au>Paganini, Irene</au><au>Mattei, Alessandra</au><au>Sani, Cristina</au><au>Vannucci, Giulia</au><au>Bisanzi, Simonetta</au><au>Burroni, Elena</au><au>Peluso, Marco</au><au>Munnia, Armelle</au><au>Cellai, Filippo</au><au>Pompeo, Giampaolo</au><au>Micio, Laura</au><au>Viti, Jessica</au><au>Mealli, Fabrizia</au><au>Carozzi, Francesca Maria</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-05-18</date><risdate>2021</risdate><volume>16</volume><issue>5</issue><spage>e0251589</spage><pages>e0251589-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. During the first emergency phase of the epidemic, RT-qPCR on nasopharyngeal (NP) swabs, which is the most reliable technique to detect ongoing infections, exhibited limitations due to availability of reagents and budget constraints. This stressed the need to develop screening procedures that require fewer resources and are suitable to be extended to larger portions of the population. RT-qPCR on pooled samples from individual NP swabs seems to be a promising technique to improve surveillance. We performed preliminary experimental analyses aimed to investigate the performance of pool testing on samples with low viral load and we evaluated through Monte Carlo (MC) simulations alternative screening protocols based on sample pooling, tailored to contexts characterized by different infection prevalence. We focused on the role of pool size and the opportunity to develop strategies that take advantage of natural clustering structures in the population, e.g. families, school classes, hospital rooms. Despite the use of a limited number of specimens, our results suggest that, while high viral load samples seem to be detectable even in a pool with 29 negative samples, positive specimens with low viral load may be masked by the negative samples, unless smaller pools are used. The results of MC simulations confirm that pool testing is useful in contexts where the infection prevalence is low. The gain of pool testing in saving resources can be very high, and can be optimized by selecting appropriate group sizes. Exploiting natural groups makes the definition of larger pools convenient and potentially overcomes the issue of low viral load samples by increasing the probability of identifying more than one positive in the same pool.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34003878</pmid><doi>10.1371/journal.pone.0251589</doi><orcidid>https://orcid.org/0000-0002-1076-9249</orcidid><orcidid>https://orcid.org/0000-0002-4365-427X</orcidid><orcidid>https://orcid.org/0000-0001-6867-0244</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2021-05, Vol.16 (5), p.e0251589 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_pubmed_primary_34003878 |
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 | Asymptomatic Biology and Life Sciences Cancer Computer programs Computer science Coronaviruses COVID-19 COVID-19 - diagnosis COVID-19 - virology COVID-19 Nucleic Acid Testing - methods Disease prevention DNA-directed RNA polymerase Drafting software Editing Electronic mail Evaluation Humans Infections Laboratories Medical screening Medicine and Health Sciences Methodology Monte Carlo Method Nasopharynx - virology Nucleocapsids Optimization Pandemics Performance evaluation Physical sciences Polymerase chain reaction Prevention Real-Time Polymerase Chain Reaction Research and analysis methods Respiratory diseases Respiratory tract Reviews Ribonucleic acid RNA RNA polymerase RNA, Viral - analysis RNA-directed RNA polymerase Samples SARS-CoV-2 - genetics SARS-CoV-2 - isolation & purification Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Software Specimen Handling Statistical analysis Statistical methods Statistics Viral diseases Viral Load |
title | Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-20T16%3A16%3A22IST&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=Pool%20testing%20on%20random%20and%20natural%20clusters%20of%20individuals:%20Optimisation%20of%20SARS-CoV-2%20surveillance%20in%20the%20presence%20of%20low%20viral%20load%20samples&rft.jtitle=PloS%20one&rft.au=Baccini,%20Michela&rft.date=2021-05-18&rft.volume=16&rft.issue=5&rft.spage=e0251589&rft.pages=e0251589-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0251589&rft_dat=%3Cgale_plos_%3EA662172438%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=2528873089&rft_id=info:pmid/34003878&rft_galeid=A662172438&rft_doaj_id=oai_doaj_org_article_1993935c0b5a4819a7c68575cef7ff9d&rfr_iscdi=true |