Reliability of COVID-19 data: An evaluation and reflection
The rapid proliferation of COVID-19 has left governments scrambling, and several data aggregators are now assisting in the reporting of county cases and deaths. The different variables affecting reporting (e.g., time delays in reporting) necessitates a well-documented reliability study examining the...
Gespeichert in:
| Veröffentlicht in: | PloS one 2022-11, Vol.17 (11), p.e0251470 |
|---|---|
| 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 | 11 |
| container_start_page | e0251470 |
| container_title | PloS one |
| container_volume | 17 |
| creator | Miller, April R Charepoo, Samin Yan, Erik Frost, Ryan W Sturgeon, Zachary J Gibbon, Grace Balius, Patrick N Thomas, Cedonia S Schmitt, Melanie A Sass, Daniel A Walters, James B Flood, Tracy L Schmitt, Thomas A |
| description | The rapid proliferation of COVID-19 has left governments scrambling, and several data aggregators are now assisting in the reporting of county cases and deaths. The different variables affecting reporting (e.g., time delays in reporting) necessitates a well-documented reliability study examining the data methods and discussion of possible causes of differences between aggregators.
To statistically evaluate the reliability of COVID-19 data across aggregators using case fatality rate (CFR) estimates and reliability statistics.
Cases and deaths were collected daily by volunteers via state and local health departments, as primary sources and newspaper reports, as secondary sources. In an effort to begin comparison for reliability statistical analysis, BroadStreet collected data from other COVID-19 aggregator sources, including USAFacts, Johns Hopkins University, New York Times, The COVID Tracking Project.
COVID-19 cases and death counts at the county and state levels.
Lower levels of inter-rater agreement were observed across aggregators associated with the number of deaths, which manifested itself in state level Bayesian estimates of COVID-19 fatality rates.
A national, publicly available data set is needed for current and future disease outbreaks and improved reliability in reporting. |
| doi_str_mv | 10.1371/journal.pone.0251470 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2731704751</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A724977636</galeid><doaj_id>oai_doaj_org_article_aa48ca639fd148c1b7152de90b366d0a</doaj_id><sourcerecordid>A724977636</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-7478831677ec133c2e64816a11e0bb18d3058393eef8211ef4bc7404d12545d53</originalsourceid><addsrcrecordid>eNqNkk2P0zAQhiMEYpfCP0AQCQnBIcVfsZM9IFXlq9JKlRbYqzWxndaVG5c4WbH_HodmVw3aA_IhzviZ1zPjN0leYjTHVOAPO9-3Dbj5wTdmjkiOmUCPknNcUpJxgujjk_1Z8iyEHUI5LTh_mpxRTokggp4nF1fGWaiss91t6ut0ub5efcpwmWro4CJdNKm5AddDZ32TQqPT1tTOqOH3efKkBhfMi_E7S35--fxj-S27XH9dLReXmeIl6TLBRFFQzIUwClOqiOGswBwwNqiqcKEpygtaUmPqgsRgzSolGGIak5zlOqez5PVR9-B8kGPbQcbysUBM5DgSqyOhPezkobV7aG-lByv_Bny7kdB2VjkjAVihgNOy1jjucCVwTrQpUUU51wii1sfxtr7aG61M07XgJqLTk8Zu5cbfyDLOtGBDMe9Ggdb_6k3o5N4GZZyDxvh-rBsLygf0zT_ow92N1AZiA7apfbxXDaJyIQgrheDxPWfJ_AEqLm32VkWP1DbGJwnvJwmR6czvbgN9CHL1_er_2fX1lH17wm4NuG4bvOsHy4QpyI6gan0I0Vf3Q8ZIDha_m4YcLC5Hi8e0V6cPdJ9052n6B89U8lM</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2731704751</pqid></control><display><type>article</type><title>Reliability of COVID-19 data: An evaluation and reflection</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>Miller, April R ; Charepoo, Samin ; Yan, Erik ; Frost, Ryan W ; Sturgeon, Zachary J ; Gibbon, Grace ; Balius, Patrick N ; Thomas, Cedonia S ; Schmitt, Melanie A ; Sass, Daniel A ; Walters, James B ; Flood, Tracy L ; Schmitt, Thomas A</creator><creatorcontrib>Miller, April R ; Charepoo, Samin ; Yan, Erik ; Frost, Ryan W ; Sturgeon, Zachary J ; Gibbon, Grace ; Balius, Patrick N ; Thomas, Cedonia S ; Schmitt, Melanie A ; Sass, Daniel A ; Walters, James B ; Flood, Tracy L ; Schmitt, Thomas A ; COVID-19 Data Project ; on behalf of the COVID-19 Data Project</creatorcontrib><description>The rapid proliferation of COVID-19 has left governments scrambling, and several data aggregators are now assisting in the reporting of county cases and deaths. The different variables affecting reporting (e.g., time delays in reporting) necessitates a well-documented reliability study examining the data methods and discussion of possible causes of differences between aggregators.
To statistically evaluate the reliability of COVID-19 data across aggregators using case fatality rate (CFR) estimates and reliability statistics.
Cases and deaths were collected daily by volunteers via state and local health departments, as primary sources and newspaper reports, as secondary sources. In an effort to begin comparison for reliability statistical analysis, BroadStreet collected data from other COVID-19 aggregator sources, including USAFacts, Johns Hopkins University, New York Times, The COVID Tracking Project.
COVID-19 cases and death counts at the county and state levels.
Lower levels of inter-rater agreement were observed across aggregators associated with the number of deaths, which manifested itself in state level Bayesian estimates of COVID-19 fatality rates.
A national, publicly available data set is needed for current and future disease outbreaks and improved reliability in reporting.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0251470</identifier><identifier>PMID: 36327273</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Bayes Theorem ; Bayesian analysis ; Biology and Life Sciences ; Colleges & universities ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; Data collection ; Disease control ; Disease Outbreaks ; Engineering and Technology ; Epidemics ; Estimates ; Evaluation ; Fatalities ; Geospatial data ; Health surveillance ; Humans ; Infectious diseases ; Medical research ; Medicine and Health Sciences ; New York ; Pandemics ; People and places ; Physical Sciences ; Public health ; Quality control ; Reliability analysis ; Reproducibility of Results ; Statistical analysis ; Statistics ; Timing ; United States ; Viral diseases</subject><ispartof>PloS one, 2022-11, Vol.17 (11), p.e0251470</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Miller 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>2022 Miller et al 2022 Miller et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-7478831677ec133c2e64816a11e0bb18d3058393eef8211ef4bc7404d12545d53</citedby><cites>FETCH-LOGICAL-c692t-7478831677ec133c2e64816a11e0bb18d3058393eef8211ef4bc7404d12545d53</cites><orcidid>0000-0003-2612-993X</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/PMC9632841/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9632841/$$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/36327273$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Miller, April R</creatorcontrib><creatorcontrib>Charepoo, Samin</creatorcontrib><creatorcontrib>Yan, Erik</creatorcontrib><creatorcontrib>Frost, Ryan W</creatorcontrib><creatorcontrib>Sturgeon, Zachary J</creatorcontrib><creatorcontrib>Gibbon, Grace</creatorcontrib><creatorcontrib>Balius, Patrick N</creatorcontrib><creatorcontrib>Thomas, Cedonia S</creatorcontrib><creatorcontrib>Schmitt, Melanie A</creatorcontrib><creatorcontrib>Sass, Daniel A</creatorcontrib><creatorcontrib>Walters, James B</creatorcontrib><creatorcontrib>Flood, Tracy L</creatorcontrib><creatorcontrib>Schmitt, Thomas A</creatorcontrib><creatorcontrib>COVID-19 Data Project</creatorcontrib><creatorcontrib>on behalf of the COVID-19 Data Project</creatorcontrib><title>Reliability of COVID-19 data: An evaluation and reflection</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The rapid proliferation of COVID-19 has left governments scrambling, and several data aggregators are now assisting in the reporting of county cases and deaths. The different variables affecting reporting (e.g., time delays in reporting) necessitates a well-documented reliability study examining the data methods and discussion of possible causes of differences between aggregators.
To statistically evaluate the reliability of COVID-19 data across aggregators using case fatality rate (CFR) estimates and reliability statistics.
Cases and deaths were collected daily by volunteers via state and local health departments, as primary sources and newspaper reports, as secondary sources. In an effort to begin comparison for reliability statistical analysis, BroadStreet collected data from other COVID-19 aggregator sources, including USAFacts, Johns Hopkins University, New York Times, The COVID Tracking Project.
COVID-19 cases and death counts at the county and state levels.
Lower levels of inter-rater agreement were observed across aggregators associated with the number of deaths, which manifested itself in state level Bayesian estimates of COVID-19 fatality rates.
A national, publicly available data set is needed for current and future disease outbreaks and improved reliability in reporting.</description><subject>Analysis</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Biology and Life Sciences</subject><subject>Colleges & universities</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>Data collection</subject><subject>Disease control</subject><subject>Disease Outbreaks</subject><subject>Engineering and Technology</subject><subject>Epidemics</subject><subject>Estimates</subject><subject>Evaluation</subject><subject>Fatalities</subject><subject>Geospatial data</subject><subject>Health surveillance</subject><subject>Humans</subject><subject>Infectious diseases</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>New York</subject><subject>Pandemics</subject><subject>People and places</subject><subject>Physical Sciences</subject><subject>Public health</subject><subject>Quality control</subject><subject>Reliability analysis</subject><subject>Reproducibility of Results</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Timing</subject><subject>United States</subject><subject>Viral diseases</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNkk2P0zAQhiMEYpfCP0AQCQnBIcVfsZM9IFXlq9JKlRbYqzWxndaVG5c4WbH_HodmVw3aA_IhzviZ1zPjN0leYjTHVOAPO9-3Dbj5wTdmjkiOmUCPknNcUpJxgujjk_1Z8iyEHUI5LTh_mpxRTokggp4nF1fGWaiss91t6ut0ub5efcpwmWro4CJdNKm5AddDZ32TQqPT1tTOqOH3efKkBhfMi_E7S35--fxj-S27XH9dLReXmeIl6TLBRFFQzIUwClOqiOGswBwwNqiqcKEpygtaUmPqgsRgzSolGGIak5zlOqez5PVR9-B8kGPbQcbysUBM5DgSqyOhPezkobV7aG-lByv_Bny7kdB2VjkjAVihgNOy1jjucCVwTrQpUUU51wii1sfxtr7aG61M07XgJqLTk8Zu5cbfyDLOtGBDMe9Ggdb_6k3o5N4GZZyDxvh-rBsLygf0zT_ow92N1AZiA7apfbxXDaJyIQgrheDxPWfJ_AEqLm32VkWP1DbGJwnvJwmR6czvbgN9CHL1_er_2fX1lH17wm4NuG4bvOsHy4QpyI6gan0I0Vf3Q8ZIDha_m4YcLC5Hi8e0V6cPdJ9052n6B89U8lM</recordid><startdate>20221103</startdate><enddate>20221103</enddate><creator>Miller, April R</creator><creator>Charepoo, Samin</creator><creator>Yan, Erik</creator><creator>Frost, Ryan W</creator><creator>Sturgeon, Zachary J</creator><creator>Gibbon, Grace</creator><creator>Balius, Patrick N</creator><creator>Thomas, Cedonia S</creator><creator>Schmitt, Melanie A</creator><creator>Sass, Daniel A</creator><creator>Walters, James B</creator><creator>Flood, Tracy L</creator><creator>Schmitt, Thomas A</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>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-2612-993X</orcidid></search><sort><creationdate>20221103</creationdate><title>Reliability of COVID-19 data: An evaluation and reflection</title><author>Miller, April R ; Charepoo, Samin ; Yan, Erik ; Frost, Ryan W ; Sturgeon, Zachary J ; Gibbon, Grace ; Balius, Patrick N ; Thomas, Cedonia S ; Schmitt, Melanie A ; Sass, Daniel A ; Walters, James B ; Flood, Tracy L ; Schmitt, Thomas A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-7478831677ec133c2e64816a11e0bb18d3058393eef8211ef4bc7404d12545d53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Analysis</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Biology and Life Sciences</topic><topic>Colleges & universities</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>Data collection</topic><topic>Disease control</topic><topic>Disease Outbreaks</topic><topic>Engineering and Technology</topic><topic>Epidemics</topic><topic>Estimates</topic><topic>Evaluation</topic><topic>Fatalities</topic><topic>Geospatial data</topic><topic>Health surveillance</topic><topic>Humans</topic><topic>Infectious diseases</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>New York</topic><topic>Pandemics</topic><topic>People and places</topic><topic>Physical Sciences</topic><topic>Public health</topic><topic>Quality control</topic><topic>Reliability analysis</topic><topic>Reproducibility of Results</topic><topic>Statistical analysis</topic><topic>Statistics</topic><topic>Timing</topic><topic>United States</topic><topic>Viral diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Miller, April R</creatorcontrib><creatorcontrib>Charepoo, Samin</creatorcontrib><creatorcontrib>Yan, Erik</creatorcontrib><creatorcontrib>Frost, Ryan W</creatorcontrib><creatorcontrib>Sturgeon, Zachary J</creatorcontrib><creatorcontrib>Gibbon, Grace</creatorcontrib><creatorcontrib>Balius, Patrick N</creatorcontrib><creatorcontrib>Thomas, Cedonia S</creatorcontrib><creatorcontrib>Schmitt, Melanie A</creatorcontrib><creatorcontrib>Sass, Daniel A</creatorcontrib><creatorcontrib>Walters, James B</creatorcontrib><creatorcontrib>Flood, Tracy L</creatorcontrib><creatorcontrib>Schmitt, Thomas A</creatorcontrib><creatorcontrib>COVID-19 Data Project</creatorcontrib><creatorcontrib>on behalf of the COVID-19 Data Project</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>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>Miller, April R</au><au>Charepoo, Samin</au><au>Yan, Erik</au><au>Frost, Ryan W</au><au>Sturgeon, Zachary J</au><au>Gibbon, Grace</au><au>Balius, Patrick N</au><au>Thomas, Cedonia S</au><au>Schmitt, Melanie A</au><au>Sass, Daniel A</au><au>Walters, James B</au><au>Flood, Tracy L</au><au>Schmitt, Thomas A</au><aucorp>COVID-19 Data Project</aucorp><aucorp>on behalf of the COVID-19 Data Project</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reliability of COVID-19 data: An evaluation and reflection</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-11-03</date><risdate>2022</risdate><volume>17</volume><issue>11</issue><spage>e0251470</spage><pages>e0251470-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The rapid proliferation of COVID-19 has left governments scrambling, and several data aggregators are now assisting in the reporting of county cases and deaths. The different variables affecting reporting (e.g., time delays in reporting) necessitates a well-documented reliability study examining the data methods and discussion of possible causes of differences between aggregators.
To statistically evaluate the reliability of COVID-19 data across aggregators using case fatality rate (CFR) estimates and reliability statistics.
Cases and deaths were collected daily by volunteers via state and local health departments, as primary sources and newspaper reports, as secondary sources. In an effort to begin comparison for reliability statistical analysis, BroadStreet collected data from other COVID-19 aggregator sources, including USAFacts, Johns Hopkins University, New York Times, The COVID Tracking Project.
COVID-19 cases and death counts at the county and state levels.
Lower levels of inter-rater agreement were observed across aggregators associated with the number of deaths, which manifested itself in state level Bayesian estimates of COVID-19 fatality rates.
A national, publicly available data set is needed for current and future disease outbreaks and improved reliability in reporting.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36327273</pmid><doi>10.1371/journal.pone.0251470</doi><tpages>e0251470</tpages><orcidid>https://orcid.org/0000-0003-2612-993X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2022-11, Vol.17 (11), p.e0251470 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2731704751 |
| 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 | Analysis Bayes Theorem Bayesian analysis Biology and Life Sciences Colleges & universities Coronaviruses COVID-19 COVID-19 - epidemiology Data collection Disease control Disease Outbreaks Engineering and Technology Epidemics Estimates Evaluation Fatalities Geospatial data Health surveillance Humans Infectious diseases Medical research Medicine and Health Sciences New York Pandemics People and places Physical Sciences Public health Quality control Reliability analysis Reproducibility of Results Statistical analysis Statistics Timing United States Viral diseases |
| title | Reliability of COVID-19 data: An evaluation and reflection |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T23%3A56%3A15IST&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=Reliability%20of%20COVID-19%20data:%20An%20evaluation%20and%20reflection&rft.jtitle=PloS%20one&rft.au=Miller,%20April%20R&rft.aucorp=COVID-19%20Data%20Project&rft.date=2022-11-03&rft.volume=17&rft.issue=11&rft.spage=e0251470&rft.pages=e0251470-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0251470&rft_dat=%3Cgale_plos_%3EA724977636%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=2731704751&rft_id=info:pmid/36327273&rft_galeid=A724977636&rft_doaj_id=oai_doaj_org_article_aa48ca639fd148c1b7152de90b366d0a&rfr_iscdi=true |