The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning
[Image omitted - see PDF] Methods The current PVI model integrates multiple data streams into an overall score derived from 12 key indicators—including well-established, general vulnerability factors for public health, plus emerging factors relevant to the pandemic—distributed across four domains: c...
Gespeichert in:
| Veröffentlicht in: | Environmental health perspectives 2021-01, Vol.129 (1), p.17701 |
|---|---|
| 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 | 1 |
| container_start_page | 17701 |
| container_title | Environmental health perspectives |
| container_volume | 129 |
| creator | Marvel, Skylar W House, John S Wheeler, Matthew Song, Kuncheng Zhou, Yi-Hui Wright, Fred A Chiu, Weihsueh A Rusyn, Ivan Motsinger-Reif, Alison Reif, David M |
| description | [Image omitted - see PDF] Methods The current PVI model integrates multiple data streams into an overall score derived from 12 key indicators—including well-established, general vulnerability factors for public health, plus emerging factors relevant to the pandemic—distributed across four domains: current infection rates, baseline population concentration, current interventions, and health and environmental vulnerabilities. Data sources in the current model (version 11.2.1) include the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC) for emergency response and hazard mitigation planning (Horney et al. 2017), testing rates from the COVID Tracking Project (Atlantic Monthly Group 2020), social distancing metrics from mobile device data ( https://www.unacast.com/covid19/social-distancing-scoreboard), and dynamic measures of disease spread and case numbers ( https://usafacts.org/issues/coronavirus/). Acknowledgments We thank the information technology and web services staff at the National Institute of Environmental Health Sciences (NIEHS)/National Institutes of Health (NIH) for their help and support, as well as J.K. Cetina and D.J. Reif for their useful technical input and advice. |
| doi_str_mv | 10.1289/EHP8690 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7785295</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A649251146</galeid><sourcerecordid>A649251146</sourcerecordid><originalsourceid>FETCH-LOGICAL-c502t-e4606dacac3ff25c2eed5a892848f23683f42821e3b9960cd2e53fec6d77e07f3</originalsourceid><addsrcrecordid>eNptktFuFCEUhonR2FqNb2BIvFCTTgVmYAYvTJpttZts001s95awzJldGhZamGlcn8ZHlbVrtWbDBeTnOz9w-BF6TckRZY38eHo2bYQkT9A-5ZwVUrLqKdonRNJC1ILvoRcpXRNCaCPEc7RXlhUhXIp99PNyCXh0MRufFFTiqfYtrKzBs8F5iHpune3XeJzV7_j9dDb-gE90Ws6Dju0nfB687UO0foFHYfD9upjAHbj_iq_SBpjZNGhnf-jeBn-Iv_V5kXprtMs2LbjMHOJ8Oj7XZmk94Ano6LP6Ej3rtEvwajsfoKsvp5ejs2Jy8XU8Op4UhhPWF1AJIlpttCm7jnHDAFquG8maqulYKZqyq1jDKJRzKQUxLQNedmBEW9dA6q48QJ_vfW-G-QpaA76P2qmbaFc6rlXQVj3e8XapFuFO1XXDmeTZ4O3WIIbbAVKvrsMQfb6zYoJVsuaUs7_UQjtQ1nchm5mVTUYdi0oyTmklMlXsoBawaasLHjqb5Uf80Q4-j9-_ubPg3X2BiSGlCN3DQylRm0SpbaIy-ebfvjxwfyJU_gK1XMY5</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2624975152</pqid></control><display><type>article</type><title>The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>JSTOR Archive Collection A-Z Listing</source><source>PubMed Central</source><creator>Marvel, Skylar W ; House, John S ; Wheeler, Matthew ; Song, Kuncheng ; Zhou, Yi-Hui ; Wright, Fred A ; Chiu, Weihsueh A ; Rusyn, Ivan ; Motsinger-Reif, Alison ; Reif, David M</creator><creatorcontrib>Marvel, Skylar W ; House, John S ; Wheeler, Matthew ; Song, Kuncheng ; Zhou, Yi-Hui ; Wright, Fred A ; Chiu, Weihsueh A ; Rusyn, Ivan ; Motsinger-Reif, Alison ; Reif, David M</creatorcontrib><description>[Image omitted - see PDF] Methods The current PVI model integrates multiple data streams into an overall score derived from 12 key indicators—including well-established, general vulnerability factors for public health, plus emerging factors relevant to the pandemic—distributed across four domains: current infection rates, baseline population concentration, current interventions, and health and environmental vulnerabilities. Data sources in the current model (version 11.2.1) include the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC) for emergency response and hazard mitigation planning (Horney et al. 2017), testing rates from the COVID Tracking Project (Atlantic Monthly Group 2020), social distancing metrics from mobile device data ( https://www.unacast.com/covid19/social-distancing-scoreboard), and dynamic measures of disease spread and case numbers ( https://usafacts.org/issues/coronavirus/). Acknowledgments We thank the information technology and web services staff at the National Institute of Environmental Health Sciences (NIEHS)/National Institutes of Health (NIH) for their help and support, as well as J.K. Cetina and D.J. Reif for their useful technical input and advice.</description><identifier>ISSN: 0091-6765</identifier><identifier>EISSN: 1552-9924</identifier><identifier>DOI: 10.1289/EHP8690</identifier><identifier>PMID: 33400596</identifier><language>eng</language><publisher>United States: National Institute of Environmental Health Sciences</publisher><subject>Control ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; Dashboards ; Data transmission ; Data Visualization ; Decision making ; Disease control ; Disease spread ; Electronic devices ; Emergency preparedness ; Emergency response ; Environmental health ; Epidemics ; Evaluation ; Generalized linear models ; Hazard mitigation ; Health aspects ; Health Status Indicators ; Humans ; Information technology ; Learning algorithms ; Machine Learning ; Mathematical models ; Models, Statistical ; Pandemics ; Population ; Public health ; Public health administration ; Quality of life ; Research Letter ; Social distancing ; Statistical models ; United States ; Vulnerable Populations ; Web services</subject><ispartof>Environmental health perspectives, 2021-01, Vol.129 (1), p.17701</ispartof><rights>COPYRIGHT 2021 National Institute of Environmental Health Sciences</rights><rights>Reproduced from Environmental Health Perspectives. This article is published under https://ehp.niehs.nih.gov/about-ehp/copyright-permissions (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c502t-e4606dacac3ff25c2eed5a892848f23683f42821e3b9960cd2e53fec6d77e07f3</citedby><cites>FETCH-LOGICAL-c502t-e4606dacac3ff25c2eed5a892848f23683f42821e3b9960cd2e53fec6d77e07f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785295/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785295/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33400596$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Marvel, Skylar W</creatorcontrib><creatorcontrib>House, John S</creatorcontrib><creatorcontrib>Wheeler, Matthew</creatorcontrib><creatorcontrib>Song, Kuncheng</creatorcontrib><creatorcontrib>Zhou, Yi-Hui</creatorcontrib><creatorcontrib>Wright, Fred A</creatorcontrib><creatorcontrib>Chiu, Weihsueh A</creatorcontrib><creatorcontrib>Rusyn, Ivan</creatorcontrib><creatorcontrib>Motsinger-Reif, Alison</creatorcontrib><creatorcontrib>Reif, David M</creatorcontrib><title>The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning</title><title>Environmental health perspectives</title><addtitle>Environ Health Perspect</addtitle><description>[Image omitted - see PDF] Methods The current PVI model integrates multiple data streams into an overall score derived from 12 key indicators—including well-established, general vulnerability factors for public health, plus emerging factors relevant to the pandemic—distributed across four domains: current infection rates, baseline population concentration, current interventions, and health and environmental vulnerabilities. Data sources in the current model (version 11.2.1) include the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC) for emergency response and hazard mitigation planning (Horney et al. 2017), testing rates from the COVID Tracking Project (Atlantic Monthly Group 2020), social distancing metrics from mobile device data ( https://www.unacast.com/covid19/social-distancing-scoreboard), and dynamic measures of disease spread and case numbers ( https://usafacts.org/issues/coronavirus/). Acknowledgments We thank the information technology and web services staff at the National Institute of Environmental Health Sciences (NIEHS)/National Institutes of Health (NIH) for their help and support, as well as J.K. Cetina and D.J. Reif for their useful technical input and advice.</description><subject>Control</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>Dashboards</subject><subject>Data transmission</subject><subject>Data Visualization</subject><subject>Decision making</subject><subject>Disease control</subject><subject>Disease spread</subject><subject>Electronic devices</subject><subject>Emergency preparedness</subject><subject>Emergency response</subject><subject>Environmental health</subject><subject>Epidemics</subject><subject>Evaluation</subject><subject>Generalized linear models</subject><subject>Hazard mitigation</subject><subject>Health aspects</subject><subject>Health Status Indicators</subject><subject>Humans</subject><subject>Information technology</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Mathematical models</subject><subject>Models, Statistical</subject><subject>Pandemics</subject><subject>Population</subject><subject>Public health</subject><subject>Public health administration</subject><subject>Quality of life</subject><subject>Research Letter</subject><subject>Social distancing</subject><subject>Statistical models</subject><subject>United States</subject><subject>Vulnerable Populations</subject><subject>Web services</subject><issn>0091-6765</issn><issn>1552-9924</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNptktFuFCEUhonR2FqNb2BIvFCTTgVmYAYvTJpttZts001s95awzJldGhZamGlcn8ZHlbVrtWbDBeTnOz9w-BF6TckRZY38eHo2bYQkT9A-5ZwVUrLqKdonRNJC1ILvoRcpXRNCaCPEc7RXlhUhXIp99PNyCXh0MRufFFTiqfYtrKzBs8F5iHpune3XeJzV7_j9dDb-gE90Ws6Dju0nfB687UO0foFHYfD9upjAHbj_iq_SBpjZNGhnf-jeBn-Iv_V5kXprtMs2LbjMHOJ8Oj7XZmk94Ano6LP6Ej3rtEvwajsfoKsvp5ejs2Jy8XU8Op4UhhPWF1AJIlpttCm7jnHDAFquG8maqulYKZqyq1jDKJRzKQUxLQNedmBEW9dA6q48QJ_vfW-G-QpaA76P2qmbaFc6rlXQVj3e8XapFuFO1XXDmeTZ4O3WIIbbAVKvrsMQfb6zYoJVsuaUs7_UQjtQ1nchm5mVTUYdi0oyTmklMlXsoBawaasLHjqb5Uf80Q4-j9-_ubPg3X2BiSGlCN3DQylRm0SpbaIy-ebfvjxwfyJU_gK1XMY5</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Marvel, Skylar W</creator><creator>House, John S</creator><creator>Wheeler, Matthew</creator><creator>Song, Kuncheng</creator><creator>Zhou, Yi-Hui</creator><creator>Wright, Fred A</creator><creator>Chiu, Weihsueh A</creator><creator>Rusyn, Ivan</creator><creator>Motsinger-Reif, Alison</creator><creator>Reif, David M</creator><general>National Institute of Environmental Health Sciences</general><general>Environmental Health Perspectives</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>4T-</scope><scope>4U-</scope><scope>7RV</scope><scope>7ST</scope><scope>7T7</scope><scope>7U7</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AN0</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>L.-</scope><scope>L6V</scope><scope>M0C</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7S</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>SOI</scope><scope>5PM</scope></search><sort><creationdate>20210101</creationdate><title>The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning</title><author>Marvel, Skylar W ; House, John S ; Wheeler, Matthew ; Song, Kuncheng ; Zhou, Yi-Hui ; Wright, Fred A ; Chiu, Weihsueh A ; Rusyn, Ivan ; Motsinger-Reif, Alison ; Reif, David M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c502t-e4606dacac3ff25c2eed5a892848f23683f42821e3b9960cd2e53fec6d77e07f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Control</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>Dashboards</topic><topic>Data transmission</topic><topic>Data Visualization</topic><topic>Decision making</topic><topic>Disease control</topic><topic>Disease spread</topic><topic>Electronic devices</topic><topic>Emergency preparedness</topic><topic>Emergency response</topic><topic>Environmental health</topic><topic>Epidemics</topic><topic>Evaluation</topic><topic>Generalized linear models</topic><topic>Hazard mitigation</topic><topic>Health aspects</topic><topic>Health Status Indicators</topic><topic>Humans</topic><topic>Information technology</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Mathematical models</topic><topic>Models, Statistical</topic><topic>Pandemics</topic><topic>Population</topic><topic>Public health</topic><topic>Public health administration</topic><topic>Quality of life</topic><topic>Research Letter</topic><topic>Social distancing</topic><topic>Statistical models</topic><topic>United States</topic><topic>Vulnerable Populations</topic><topic>Web services</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Marvel, Skylar W</creatorcontrib><creatorcontrib>House, John S</creatorcontrib><creatorcontrib>Wheeler, Matthew</creatorcontrib><creatorcontrib>Song, Kuncheng</creatorcontrib><creatorcontrib>Zhou, Yi-Hui</creatorcontrib><creatorcontrib>Wright, Fred A</creatorcontrib><creatorcontrib>Chiu, Weihsueh A</creatorcontrib><creatorcontrib>Rusyn, Ivan</creatorcontrib><creatorcontrib>Motsinger-Reif, Alison</creatorcontrib><creatorcontrib>Reif, David M</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>Docstoc</collection><collection>University Readers</collection><collection>Nursing & Allied Health Database</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Toxicology Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</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>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</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>British Nursing Database</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</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 Central Korea</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM Global</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</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>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><collection>Environment Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Environmental health perspectives</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Marvel, Skylar W</au><au>House, John S</au><au>Wheeler, Matthew</au><au>Song, Kuncheng</au><au>Zhou, Yi-Hui</au><au>Wright, Fred A</au><au>Chiu, Weihsueh A</au><au>Rusyn, Ivan</au><au>Motsinger-Reif, Alison</au><au>Reif, David M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning</atitle><jtitle>Environmental health perspectives</jtitle><addtitle>Environ Health Perspect</addtitle><date>2021-01-01</date><risdate>2021</risdate><volume>129</volume><issue>1</issue><spage>17701</spage><pages>17701-</pages><issn>0091-6765</issn><eissn>1552-9924</eissn><abstract>[Image omitted - see PDF] Methods The current PVI model integrates multiple data streams into an overall score derived from 12 key indicators—including well-established, general vulnerability factors for public health, plus emerging factors relevant to the pandemic—distributed across four domains: current infection rates, baseline population concentration, current interventions, and health and environmental vulnerabilities. Data sources in the current model (version 11.2.1) include the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC) for emergency response and hazard mitigation planning (Horney et al. 2017), testing rates from the COVID Tracking Project (Atlantic Monthly Group 2020), social distancing metrics from mobile device data ( https://www.unacast.com/covid19/social-distancing-scoreboard), and dynamic measures of disease spread and case numbers ( https://usafacts.org/issues/coronavirus/). Acknowledgments We thank the information technology and web services staff at the National Institute of Environmental Health Sciences (NIEHS)/National Institutes of Health (NIH) for their help and support, as well as J.K. Cetina and D.J. Reif for their useful technical input and advice.</abstract><cop>United States</cop><pub>National Institute of Environmental Health Sciences</pub><pmid>33400596</pmid><doi>10.1289/EHP8690</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0091-6765 |
| ispartof | Environmental health perspectives, 2021-01, Vol.129 (1), p.17701 |
| issn | 0091-6765 1552-9924 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7785295 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; JSTOR Archive Collection A-Z Listing; PubMed Central |
| subjects | Control Coronaviruses COVID-19 COVID-19 - epidemiology Dashboards Data transmission Data Visualization Decision making Disease control Disease spread Electronic devices Emergency preparedness Emergency response Environmental health Epidemics Evaluation Generalized linear models Hazard mitigation Health aspects Health Status Indicators Humans Information technology Learning algorithms Machine Learning Mathematical models Models, Statistical Pandemics Population Public health Public health administration Quality of life Research Letter Social distancing Statistical models United States Vulnerable Populations Web services |
| title | The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T12%3A14%3A08IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20COVID-19%20Pandemic%20Vulnerability%20Index%20(PVI)%20Dashboard:%20Monitoring%20County-Level%20Vulnerability%20Using%20Visualization,%20Statistical%20Modeling,%20and%20Machine%20Learning&rft.jtitle=Environmental%20health%20perspectives&rft.au=Marvel,%20Skylar%20W&rft.date=2021-01-01&rft.volume=129&rft.issue=1&rft.spage=17701&rft.pages=17701-&rft.issn=0091-6765&rft.eissn=1552-9924&rft_id=info:doi/10.1289/EHP8690&rft_dat=%3Cgale_pubme%3EA649251146%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2624975152&rft_id=info:pmid/33400596&rft_galeid=A649251146&rfr_iscdi=true |