Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing
Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey respo...
Gespeichert in:
| Veröffentlicht in: | Nature human behaviour 2020-09, Vol.4 (9), p.972-982 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 982 |
|---|---|
| container_issue | 9 |
| container_start_page | 972 |
| container_title | Nature human behaviour |
| container_volume | 4 |
| creator | Allen, William E. Altae-Tran, Han Briggs, James Jin, Xin McGee, Glen Shi, Andy Raghavan, Rumya Kamariza, Mireille Nova, Nicole Pereta, Albert Danford, Chris Kamel, Amine Gothe, Patrik Milam, Evrhet Aurambault, Jean Primke, Thorben Li, Weijie Inkenbrandt, Josh Huynh, Tuan Chen, Evan Lee, Christina Croatto, Michael Bentley, Helen Lu, Wendy Murray, Robert Travassos, Mark Coull, Brent A. Openshaw, John Greene, Casey S. Shalem, Ophir King, Gary Probasco, Ryan Cheng, David R. Silbermann, Ben Zhang, Feng Lin, Xihong |
| description | Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.
How We Feel is a web and mobile-phone application for collecting de-identified self-reported COVID-19-related data. These data are used to map a diverse set of symptomatic, demographic, exposure and behavioural factors relevant to the ongoing pandemic. |
| doi_str_mv | 10.1038/s41562-020-00944-2 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7501153</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2437841276</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-4c62d0158ca86f9cc4b3dd07c09c0fea23c9ebd1193a93cb301156d970101a73</originalsourceid><addsrcrecordid>eNqNkV9r1jAYxYMobsx9AS-k4I2g0Sd_2jQ3gtSpg8EUhrchTdN3GW1Sm3Syb790na_TC_EqgfzO4ZwchJ4TeEuA1e8iJ2VFMVDAAJJzTB-hQ8qkwIwJ_vjB_QAdx3gFAEQyLkX1FB0wWvOaMnKIvn0N0zLo5ILH0ejBFkPwO5eWznk9FKOeJud3ReiL5vz76UdMZBFvximFMb4pWnupr11Y5kL7rkg2psw-Q096PUR7fH8eoYtPJxfNF3x2_vm0-XCGDRc8YW4q2gEpa6PrqpfG8JZ1HQgD0kBvNWVG2rYjObSWzLQMSC7cSQEEiBbsCL3fbKelHW1nrE-zHtQ0u1HPNypop_588e5S7cK1EuXqxLLBq3uDOfxYcnY1umjsMGhvwxIV5UzUnFBRZfTlX-hVLp3_Z6U4rSRhgmaKbpSZQ4yz7fdhCKh1M7VtpvJm6m4ztYpePKyxl_xaKAP1Bvy0beijcdYbu8fyqmXFSElqWAduXLqbsgmLT1n6-v-lmWYbHTPhd3b-XfIf-W8BwSLCQQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2442691372</pqid></control><display><type>article</type><title>Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing</title><source>MEDLINE</source><source>Nature</source><source>Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><source>Web of Science - Social Sciences Citation Index – 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><source>Alma/SFX Local Collection</source><creator>Allen, William E. ; Altae-Tran, Han ; Briggs, James ; Jin, Xin ; McGee, Glen ; Shi, Andy ; Raghavan, Rumya ; Kamariza, Mireille ; Nova, Nicole ; Pereta, Albert ; Danford, Chris ; Kamel, Amine ; Gothe, Patrik ; Milam, Evrhet ; Aurambault, Jean ; Primke, Thorben ; Li, Weijie ; Inkenbrandt, Josh ; Huynh, Tuan ; Chen, Evan ; Lee, Christina ; Croatto, Michael ; Bentley, Helen ; Lu, Wendy ; Murray, Robert ; Travassos, Mark ; Coull, Brent A. ; Openshaw, John ; Greene, Casey S. ; Shalem, Ophir ; King, Gary ; Probasco, Ryan ; Cheng, David R. ; Silbermann, Ben ; Zhang, Feng ; Lin, Xihong</creator><creatorcontrib>Allen, William E. ; Altae-Tran, Han ; Briggs, James ; Jin, Xin ; McGee, Glen ; Shi, Andy ; Raghavan, Rumya ; Kamariza, Mireille ; Nova, Nicole ; Pereta, Albert ; Danford, Chris ; Kamel, Amine ; Gothe, Patrik ; Milam, Evrhet ; Aurambault, Jean ; Primke, Thorben ; Li, Weijie ; Inkenbrandt, Josh ; Huynh, Tuan ; Chen, Evan ; Lee, Christina ; Croatto, Michael ; Bentley, Helen ; Lu, Wendy ; Murray, Robert ; Travassos, Mark ; Coull, Brent A. ; Openshaw, John ; Greene, Casey S. ; Shalem, Ophir ; King, Gary ; Probasco, Ryan ; Cheng, David R. ; Silbermann, Ben ; Zhang, Feng ; Lin, Xihong</creatorcontrib><description>Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.
How We Feel is a web and mobile-phone application for collecting de-identified self-reported COVID-19-related data. These data are used to map a diverse set of symptomatic, demographic, exposure and behavioural factors relevant to the ongoing pandemic.</description><identifier>ISSN: 2397-3374</identifier><identifier>EISSN: 2397-3374</identifier><identifier>DOI: 10.1038/s41562-020-00944-2</identifier><identifier>PMID: 32848231</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject><![CDATA[692/308/174 ; 692/499 ; 692/53/2421 ; Adult ; Asymptomatic Diseases - epidemiology ; Behavioral Sciences ; Betacoronavirus ; Biomedical and Life Sciences ; Clinical Laboratory Techniques - statistics & numerical data ; Coronavirus Infections - diagnosis ; Coronavirus Infections - epidemiology ; Coronavirus Infections - prevention & control ; Coronavirus Infections - psychology ; Coronaviruses ; COVID-19 ; COVID-19 Testing ; Demography ; Experimental Psychology ; Female ; Health behavior ; Humans ; Life Sciences ; Life Sciences & Biomedicine ; Longitudinal Studies ; Male ; Mapping ; Microeconomics ; Mobile Applications ; Models, Statistical ; Multidisciplinary Sciences ; Neurosciences ; Neurosciences & Neurology ; Pandemics ; Pandemics - prevention & control ; Pandemics - statistics & numerical data ; Personality and Social Psychology ; Pneumonia, Viral - diagnosis ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - prevention & control ; Pneumonia, Viral - psychology ; Polls & surveys ; Prediction models ; Psychology ; Psychology, Biological ; Psychology, Experimental ; Public health ; Risk factors ; SARS-CoV-2 ; Science & Technology ; Science & Technology - Other Topics ; Severe acute respiratory syndrome coronavirus 2 ; Social Sciences ; Symptoms ; United States - epidemiology]]></subject><ispartof>Nature human behaviour, 2020-09, Vol.4 (9), p.972-982</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Limited 2020</rights><rights>The Author(s), under exclusive licence to Springer Nature Limited 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>70</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000563151800001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c474t-4c62d0158ca86f9cc4b3dd07c09c0fea23c9ebd1193a93cb301156d970101a73</citedby><cites>FETCH-LOGICAL-c474t-4c62d0158ca86f9cc4b3dd07c09c0fea23c9ebd1193a93cb301156d970101a73</cites><orcidid>0000-0002-6133-9169 ; 0000-0002-1319-9333 ; 0000-0002-6045-3322 ; 0000-0001-6552-9536 ; 0000-0001-8713-9213 ; 0000-0001-6849-4156 ; 0000-0002-6168-7761 ; 0000-0002-3674-666X ; 0000-0002-3674-7187 ; 0000-0002-5327-7631 ; 0000-0002-9862-6428 ; 0000-0003-2344-992X ; 0000-0001-7067-7752 ; 0000-0002-7444-1103 ; 0000-0003-1255-0803 ; 0000-0002-5907-6902 ; 0000-0001-8585-1215</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,781,785,886,27929,27930,28253,28254</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32848231$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Allen, William E.</creatorcontrib><creatorcontrib>Altae-Tran, Han</creatorcontrib><creatorcontrib>Briggs, James</creatorcontrib><creatorcontrib>Jin, Xin</creatorcontrib><creatorcontrib>McGee, Glen</creatorcontrib><creatorcontrib>Shi, Andy</creatorcontrib><creatorcontrib>Raghavan, Rumya</creatorcontrib><creatorcontrib>Kamariza, Mireille</creatorcontrib><creatorcontrib>Nova, Nicole</creatorcontrib><creatorcontrib>Pereta, Albert</creatorcontrib><creatorcontrib>Danford, Chris</creatorcontrib><creatorcontrib>Kamel, Amine</creatorcontrib><creatorcontrib>Gothe, Patrik</creatorcontrib><creatorcontrib>Milam, Evrhet</creatorcontrib><creatorcontrib>Aurambault, Jean</creatorcontrib><creatorcontrib>Primke, Thorben</creatorcontrib><creatorcontrib>Li, Weijie</creatorcontrib><creatorcontrib>Inkenbrandt, Josh</creatorcontrib><creatorcontrib>Huynh, Tuan</creatorcontrib><creatorcontrib>Chen, Evan</creatorcontrib><creatorcontrib>Lee, Christina</creatorcontrib><creatorcontrib>Croatto, Michael</creatorcontrib><creatorcontrib>Bentley, Helen</creatorcontrib><creatorcontrib>Lu, Wendy</creatorcontrib><creatorcontrib>Murray, Robert</creatorcontrib><creatorcontrib>Travassos, Mark</creatorcontrib><creatorcontrib>Coull, Brent A.</creatorcontrib><creatorcontrib>Openshaw, John</creatorcontrib><creatorcontrib>Greene, Casey S.</creatorcontrib><creatorcontrib>Shalem, Ophir</creatorcontrib><creatorcontrib>King, Gary</creatorcontrib><creatorcontrib>Probasco, Ryan</creatorcontrib><creatorcontrib>Cheng, David R.</creatorcontrib><creatorcontrib>Silbermann, Ben</creatorcontrib><creatorcontrib>Zhang, Feng</creatorcontrib><creatorcontrib>Lin, Xihong</creatorcontrib><title>Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing</title><title>Nature human behaviour</title><addtitle>Nat Hum Behav</addtitle><addtitle>NAT HUM BEHAV</addtitle><addtitle>Nat Hum Behav</addtitle><description>Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.
How We Feel is a web and mobile-phone application for collecting de-identified self-reported COVID-19-related data. These data are used to map a diverse set of symptomatic, demographic, exposure and behavioural factors relevant to the ongoing pandemic.</description><subject>692/308/174</subject><subject>692/499</subject><subject>692/53/2421</subject><subject>Adult</subject><subject>Asymptomatic Diseases - epidemiology</subject><subject>Behavioral Sciences</subject><subject>Betacoronavirus</subject><subject>Biomedical and Life Sciences</subject><subject>Clinical Laboratory Techniques - statistics & numerical data</subject><subject>Coronavirus Infections - diagnosis</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronavirus Infections - prevention & control</subject><subject>Coronavirus Infections - psychology</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 Testing</subject><subject>Demography</subject><subject>Experimental Psychology</subject><subject>Female</subject><subject>Health behavior</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Life Sciences & Biomedicine</subject><subject>Longitudinal Studies</subject><subject>Male</subject><subject>Mapping</subject><subject>Microeconomics</subject><subject>Mobile Applications</subject><subject>Models, Statistical</subject><subject>Multidisciplinary Sciences</subject><subject>Neurosciences</subject><subject>Neurosciences & Neurology</subject><subject>Pandemics</subject><subject>Pandemics - prevention & control</subject><subject>Pandemics - statistics & numerical data</subject><subject>Personality and Social Psychology</subject><subject>Pneumonia, Viral - diagnosis</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Pneumonia, Viral - prevention & control</subject><subject>Pneumonia, Viral - psychology</subject><subject>Polls & surveys</subject><subject>Prediction models</subject><subject>Psychology</subject><subject>Psychology, Biological</subject><subject>Psychology, Experimental</subject><subject>Public health</subject><subject>Risk factors</subject><subject>SARS-CoV-2</subject><subject>Science & Technology</subject><subject>Science & Technology - Other Topics</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Social Sciences</subject><subject>Symptoms</subject><subject>United States - epidemiology</subject><issn>2397-3374</issn><issn>2397-3374</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><sourceid>ARHDP</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkV9r1jAYxYMobsx9AS-k4I2g0Sd_2jQ3gtSpg8EUhrchTdN3GW1Sm3Syb790na_TC_EqgfzO4ZwchJ4TeEuA1e8iJ2VFMVDAAJJzTB-hQ8qkwIwJ_vjB_QAdx3gFAEQyLkX1FB0wWvOaMnKIvn0N0zLo5ILH0ejBFkPwO5eWznk9FKOeJud3ReiL5vz76UdMZBFvximFMb4pWnupr11Y5kL7rkg2psw-Q096PUR7fH8eoYtPJxfNF3x2_vm0-XCGDRc8YW4q2gEpa6PrqpfG8JZ1HQgD0kBvNWVG2rYjObSWzLQMSC7cSQEEiBbsCL3fbKelHW1nrE-zHtQ0u1HPNypop_588e5S7cK1EuXqxLLBq3uDOfxYcnY1umjsMGhvwxIV5UzUnFBRZfTlX-hVLp3_Z6U4rSRhgmaKbpSZQ4yz7fdhCKh1M7VtpvJm6m4ztYpePKyxl_xaKAP1Bvy0beijcdYbu8fyqmXFSElqWAduXLqbsgmLT1n6-v-lmWYbHTPhd3b-XfIf-W8BwSLCQQ</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Allen, William E.</creator><creator>Altae-Tran, Han</creator><creator>Briggs, James</creator><creator>Jin, Xin</creator><creator>McGee, Glen</creator><creator>Shi, Andy</creator><creator>Raghavan, Rumya</creator><creator>Kamariza, Mireille</creator><creator>Nova, Nicole</creator><creator>Pereta, Albert</creator><creator>Danford, Chris</creator><creator>Kamel, Amine</creator><creator>Gothe, Patrik</creator><creator>Milam, Evrhet</creator><creator>Aurambault, Jean</creator><creator>Primke, Thorben</creator><creator>Li, Weijie</creator><creator>Inkenbrandt, Josh</creator><creator>Huynh, Tuan</creator><creator>Chen, Evan</creator><creator>Lee, Christina</creator><creator>Croatto, Michael</creator><creator>Bentley, Helen</creator><creator>Lu, Wendy</creator><creator>Murray, Robert</creator><creator>Travassos, Mark</creator><creator>Coull, Brent A.</creator><creator>Openshaw, John</creator><creator>Greene, Casey S.</creator><creator>Shalem, Ophir</creator><creator>King, Gary</creator><creator>Probasco, Ryan</creator><creator>Cheng, David R.</creator><creator>Silbermann, Ben</creator><creator>Zhang, Feng</creator><creator>Lin, Xihong</creator><general>Nature Publishing Group UK</general><general>NATURE PORTFOLIO</general><general>Nature Publishing Group</general><scope>17B</scope><scope>AOWDO</scope><scope>ARHDP</scope><scope>BLEPL</scope><scope>DTL</scope><scope>DVR</scope><scope>EGQ</scope><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>0-V</scope><scope>3V.</scope><scope>7XB</scope><scope>88G</scope><scope>88J</scope><scope>8BJ</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>JBE</scope><scope>M2M</scope><scope>M2R</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-6133-9169</orcidid><orcidid>https://orcid.org/0000-0002-1319-9333</orcidid><orcidid>https://orcid.org/0000-0002-6045-3322</orcidid><orcidid>https://orcid.org/0000-0001-6552-9536</orcidid><orcidid>https://orcid.org/0000-0001-8713-9213</orcidid><orcidid>https://orcid.org/0000-0001-6849-4156</orcidid><orcidid>https://orcid.org/0000-0002-6168-7761</orcidid><orcidid>https://orcid.org/0000-0002-3674-666X</orcidid><orcidid>https://orcid.org/0000-0002-3674-7187</orcidid><orcidid>https://orcid.org/0000-0002-5327-7631</orcidid><orcidid>https://orcid.org/0000-0002-9862-6428</orcidid><orcidid>https://orcid.org/0000-0003-2344-992X</orcidid><orcidid>https://orcid.org/0000-0001-7067-7752</orcidid><orcidid>https://orcid.org/0000-0002-7444-1103</orcidid><orcidid>https://orcid.org/0000-0003-1255-0803</orcidid><orcidid>https://orcid.org/0000-0002-5907-6902</orcidid><orcidid>https://orcid.org/0000-0001-8585-1215</orcidid></search><sort><creationdate>20200901</creationdate><title>Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing</title><author>Allen, William E. ; Altae-Tran, Han ; Briggs, James ; Jin, Xin ; McGee, Glen ; Shi, Andy ; Raghavan, Rumya ; Kamariza, Mireille ; Nova, Nicole ; Pereta, Albert ; Danford, Chris ; Kamel, Amine ; Gothe, Patrik ; Milam, Evrhet ; Aurambault, Jean ; Primke, Thorben ; Li, Weijie ; Inkenbrandt, Josh ; Huynh, Tuan ; Chen, Evan ; Lee, Christina ; Croatto, Michael ; Bentley, Helen ; Lu, Wendy ; Murray, Robert ; Travassos, Mark ; Coull, Brent A. ; Openshaw, John ; Greene, Casey S. ; Shalem, Ophir ; King, Gary ; Probasco, Ryan ; Cheng, David R. ; Silbermann, Ben ; Zhang, Feng ; Lin, Xihong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-4c62d0158ca86f9cc4b3dd07c09c0fea23c9ebd1193a93cb301156d970101a73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>692/308/174</topic><topic>692/499</topic><topic>692/53/2421</topic><topic>Adult</topic><topic>Asymptomatic Diseases - epidemiology</topic><topic>Behavioral Sciences</topic><topic>Betacoronavirus</topic><topic>Biomedical and Life Sciences</topic><topic>Clinical Laboratory Techniques - statistics & numerical data</topic><topic>Coronavirus Infections - diagnosis</topic><topic>Coronavirus Infections - epidemiology</topic><topic>Coronavirus Infections - prevention & control</topic><topic>Coronavirus Infections - psychology</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 Testing</topic><topic>Demography</topic><topic>Experimental Psychology</topic><topic>Female</topic><topic>Health behavior</topic><topic>Humans</topic><topic>Life Sciences</topic><topic>Life Sciences & Biomedicine</topic><topic>Longitudinal Studies</topic><topic>Male</topic><topic>Mapping</topic><topic>Microeconomics</topic><topic>Mobile Applications</topic><topic>Models, Statistical</topic><topic>Multidisciplinary Sciences</topic><topic>Neurosciences</topic><topic>Neurosciences & Neurology</topic><topic>Pandemics</topic><topic>Pandemics - prevention & control</topic><topic>Pandemics - statistics & numerical data</topic><topic>Personality and Social Psychology</topic><topic>Pneumonia, Viral - diagnosis</topic><topic>Pneumonia, Viral - epidemiology</topic><topic>Pneumonia, Viral - prevention & control</topic><topic>Pneumonia, Viral - psychology</topic><topic>Polls & surveys</topic><topic>Prediction models</topic><topic>Psychology</topic><topic>Psychology, Biological</topic><topic>Psychology, Experimental</topic><topic>Public health</topic><topic>Risk factors</topic><topic>SARS-CoV-2</topic><topic>Science & Technology</topic><topic>Science & Technology - Other Topics</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Social Sciences</topic><topic>Symptoms</topic><topic>United States - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Allen, William E.</creatorcontrib><creatorcontrib>Altae-Tran, Han</creatorcontrib><creatorcontrib>Briggs, James</creatorcontrib><creatorcontrib>Jin, Xin</creatorcontrib><creatorcontrib>McGee, Glen</creatorcontrib><creatorcontrib>Shi, Andy</creatorcontrib><creatorcontrib>Raghavan, Rumya</creatorcontrib><creatorcontrib>Kamariza, Mireille</creatorcontrib><creatorcontrib>Nova, Nicole</creatorcontrib><creatorcontrib>Pereta, Albert</creatorcontrib><creatorcontrib>Danford, Chris</creatorcontrib><creatorcontrib>Kamel, Amine</creatorcontrib><creatorcontrib>Gothe, Patrik</creatorcontrib><creatorcontrib>Milam, Evrhet</creatorcontrib><creatorcontrib>Aurambault, Jean</creatorcontrib><creatorcontrib>Primke, Thorben</creatorcontrib><creatorcontrib>Li, Weijie</creatorcontrib><creatorcontrib>Inkenbrandt, Josh</creatorcontrib><creatorcontrib>Huynh, Tuan</creatorcontrib><creatorcontrib>Chen, Evan</creatorcontrib><creatorcontrib>Lee, Christina</creatorcontrib><creatorcontrib>Croatto, Michael</creatorcontrib><creatorcontrib>Bentley, Helen</creatorcontrib><creatorcontrib>Lu, Wendy</creatorcontrib><creatorcontrib>Murray, Robert</creatorcontrib><creatorcontrib>Travassos, Mark</creatorcontrib><creatorcontrib>Coull, Brent A.</creatorcontrib><creatorcontrib>Openshaw, John</creatorcontrib><creatorcontrib>Greene, Casey S.</creatorcontrib><creatorcontrib>Shalem, Ophir</creatorcontrib><creatorcontrib>King, Gary</creatorcontrib><creatorcontrib>Probasco, Ryan</creatorcontrib><creatorcontrib>Cheng, David R.</creatorcontrib><creatorcontrib>Silbermann, Ben</creatorcontrib><creatorcontrib>Zhang, Feng</creatorcontrib><creatorcontrib>Lin, Xihong</creatorcontrib><collection>Web of Knowledge</collection><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science - Social Sciences Citation Index – 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Social Sciences Citation Index</collection><collection>Web of Science Primary (SCIE, SSCI & AHCI)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Psychology Database (Alumni)</collection><collection>Social Science Database (Alumni Edition)</collection><collection>International Bibliography of the Social Sciences (IBSS)</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>Social Science Premium Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>International Bibliography of the Social Sciences</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>International Bibliography of the Social Sciences</collection><collection>Psychology Database</collection><collection>Social Science 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 One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nature human behaviour</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Allen, William E.</au><au>Altae-Tran, Han</au><au>Briggs, James</au><au>Jin, Xin</au><au>McGee, Glen</au><au>Shi, Andy</au><au>Raghavan, Rumya</au><au>Kamariza, Mireille</au><au>Nova, Nicole</au><au>Pereta, Albert</au><au>Danford, Chris</au><au>Kamel, Amine</au><au>Gothe, Patrik</au><au>Milam, Evrhet</au><au>Aurambault, Jean</au><au>Primke, Thorben</au><au>Li, Weijie</au><au>Inkenbrandt, Josh</au><au>Huynh, Tuan</au><au>Chen, Evan</au><au>Lee, Christina</au><au>Croatto, Michael</au><au>Bentley, Helen</au><au>Lu, Wendy</au><au>Murray, Robert</au><au>Travassos, Mark</au><au>Coull, Brent A.</au><au>Openshaw, John</au><au>Greene, Casey S.</au><au>Shalem, Ophir</au><au>King, Gary</au><au>Probasco, Ryan</au><au>Cheng, David R.</au><au>Silbermann, Ben</au><au>Zhang, Feng</au><au>Lin, Xihong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing</atitle><jtitle>Nature human behaviour</jtitle><stitle>Nat Hum Behav</stitle><stitle>NAT HUM BEHAV</stitle><addtitle>Nat Hum Behav</addtitle><date>2020-09-01</date><risdate>2020</risdate><volume>4</volume><issue>9</issue><spage>972</spage><epage>982</epage><pages>972-982</pages><issn>2397-3374</issn><eissn>2397-3374</eissn><abstract>Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.
How We Feel is a web and mobile-phone application for collecting de-identified self-reported COVID-19-related data. These data are used to map a diverse set of symptomatic, demographic, exposure and behavioural factors relevant to the ongoing pandemic.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>32848231</pmid><doi>10.1038/s41562-020-00944-2</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-6133-9169</orcidid><orcidid>https://orcid.org/0000-0002-1319-9333</orcidid><orcidid>https://orcid.org/0000-0002-6045-3322</orcidid><orcidid>https://orcid.org/0000-0001-6552-9536</orcidid><orcidid>https://orcid.org/0000-0001-8713-9213</orcidid><orcidid>https://orcid.org/0000-0001-6849-4156</orcidid><orcidid>https://orcid.org/0000-0002-6168-7761</orcidid><orcidid>https://orcid.org/0000-0002-3674-666X</orcidid><orcidid>https://orcid.org/0000-0002-3674-7187</orcidid><orcidid>https://orcid.org/0000-0002-5327-7631</orcidid><orcidid>https://orcid.org/0000-0002-9862-6428</orcidid><orcidid>https://orcid.org/0000-0003-2344-992X</orcidid><orcidid>https://orcid.org/0000-0001-7067-7752</orcidid><orcidid>https://orcid.org/0000-0002-7444-1103</orcidid><orcidid>https://orcid.org/0000-0003-1255-0803</orcidid><orcidid>https://orcid.org/0000-0002-5907-6902</orcidid><orcidid>https://orcid.org/0000-0001-8585-1215</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2397-3374 |
| ispartof | Nature human behaviour, 2020-09, Vol.4 (9), p.972-982 |
| issn | 2397-3374 2397-3374 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7501153 |
| source | MEDLINE; Nature; Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; Web of Science - Social Sciences Citation Index – 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; Alma/SFX Local Collection |
| subjects | 692/308/174 692/499 692/53/2421 Adult Asymptomatic Diseases - epidemiology Behavioral Sciences Betacoronavirus Biomedical and Life Sciences Clinical Laboratory Techniques - statistics & numerical data Coronavirus Infections - diagnosis Coronavirus Infections - epidemiology Coronavirus Infections - prevention & control Coronavirus Infections - psychology Coronaviruses COVID-19 COVID-19 Testing Demography Experimental Psychology Female Health behavior Humans Life Sciences Life Sciences & Biomedicine Longitudinal Studies Male Mapping Microeconomics Mobile Applications Models, Statistical Multidisciplinary Sciences Neurosciences Neurosciences & Neurology Pandemics Pandemics - prevention & control Pandemics - statistics & numerical data Personality and Social Psychology Pneumonia, Viral - diagnosis Pneumonia, Viral - epidemiology Pneumonia, Viral - prevention & control Pneumonia, Viral - psychology Polls & surveys Prediction models Psychology Psychology, Biological Psychology, Experimental Public health Risk factors SARS-CoV-2 Science & Technology Science & Technology - Other Topics Severe acute respiratory syndrome coronavirus 2 Social Sciences Symptoms United States - epidemiology |
| title | Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-16T03%3A25%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Population-scale%20longitudinal%20mapping%20of%20COVID-19%20symptoms,%20behaviour%20and%20testing&rft.jtitle=Nature%20human%20behaviour&rft.au=Allen,%20William%20E.&rft.date=2020-09-01&rft.volume=4&rft.issue=9&rft.spage=972&rft.epage=982&rft.pages=972-982&rft.issn=2397-3374&rft.eissn=2397-3374&rft_id=info:doi/10.1038/s41562-020-00944-2&rft_dat=%3Cproquest_pubme%3E2437841276%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2442691372&rft_id=info:pmid/32848231&rfr_iscdi=true |