COVID RADAR app : Description and validation of population surveillance of symptoms and behavior in relation to COVID-19
BACKGROUND: Monitoring of symptoms and behavior may enable prediction of emerging COVID-19 hotspots. The COVID Radar smartphone app, active in the Netherlands, allows users to self-report symptoms, social distancing behaviors, and COVID-19 status daily. The objective of this study is to describe the validation of the COVID Radar.
METHODS: COVID Radar users are asked to complete a daily questionnaire consisting of 20 questions assessing their symptoms, social distancing behavior, and COVID-19 status. We describe the internal and external validation of symptoms, behavior, and both user-reported COVID-19 status and state-reported COVID-19 case numbers.
RESULTS: Since April 2nd, 2020, over 6 million observations from over 250,000 users have been collected using the COVID Radar app. Almost 2,000 users reported having tested positive for SARS-CoV-2. Amongst users testing positive for SARS-CoV-2, the proportion of observations reporting symptoms was higher than that of the cohort as a whole in the week prior to a positive SARS-CoV-2 test. Likewise, users who tested positive for SARS-CoV-2 showed above average risk social-distancing behavior. Per-capita user-reported SARS-CoV-2 positive tests closely matched government-reported per-capita case counts in provinces with high user engagement.
DISCUSSION: The COVID Radar app allows voluntarily self-reporting of COVID-19 related symptoms and social distancing behaviors. Symptoms and risk behavior increase prior to a positive SARS-CoV-2 test, and user-reported case counts match closely with nationally-reported case counts in regions with high user engagement. These results suggest the COVID Radar may be a valid instrument for future surveillance and potential predictive analytics to identify emerging hotspots.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
PloS one - 16(2021), 6 vom: 30., Seite e0253566 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
van Dijk, Willian J [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 08.07.2021 Date Revised 13.07.2021 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0253566 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM327386525 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Monitoring of symptoms and behavior may enable prediction of emerging COVID-19 hotspots. The COVID Radar smartphone app, active in the Netherlands, allows users to self-report symptoms, social distancing behaviors, and COVID-19 status daily. The objective of this study is to describe the validation of the COVID Radar | ||
| 520 | |a METHODS: COVID Radar users are asked to complete a daily questionnaire consisting of 20 questions assessing their symptoms, social distancing behavior, and COVID-19 status. We describe the internal and external validation of symptoms, behavior, and both user-reported COVID-19 status and state-reported COVID-19 case numbers | ||
| 520 | |a RESULTS: Since April 2nd, 2020, over 6 million observations from over 250,000 users have been collected using the COVID Radar app. Almost 2,000 users reported having tested positive for SARS-CoV-2. Amongst users testing positive for SARS-CoV-2, the proportion of observations reporting symptoms was higher than that of the cohort as a whole in the week prior to a positive SARS-CoV-2 test. Likewise, users who tested positive for SARS-CoV-2 showed above average risk social-distancing behavior. Per-capita user-reported SARS-CoV-2 positive tests closely matched government-reported per-capita case counts in provinces with high user engagement | ||
| 520 | |a DISCUSSION: The COVID Radar app allows voluntarily self-reporting of COVID-19 related symptoms and social distancing behaviors. Symptoms and risk behavior increase prior to a positive SARS-CoV-2 test, and user-reported case counts match closely with nationally-reported case counts in regions with high user engagement. These results suggest the COVID Radar may be a valid instrument for future surveillance and potential predictive analytics to identify emerging hotspots | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
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| 700 | 1 | |a Numans, Mattijs E |e verfasserin |4 aut | |
| 700 | 1 | |a Aardoom, Jiska J |e verfasserin |4 aut | |
| 700 | 1 | |a Bonten, Tobias N |e verfasserin |4 aut | |
| 700 | 1 | |a Brandjes, Menno |e verfasserin |4 aut | |
| 700 | 1 | |a Brust, Michelle |e verfasserin |4 aut | |
| 700 | 1 | |a le Cessie, Saskia |e verfasserin |4 aut | |
| 700 | 1 | |a Chavannes, Niels H |e verfasserin |4 aut | |
| 700 | 1 | |a Middelburg, Rutger A |e verfasserin |4 aut | |
| 700 | 1 | |a Rosendaal, Frits |e verfasserin |4 aut | |
| 700 | 1 | |a Visser, Leo G |e verfasserin |4 aut | |
| 700 | 1 | |a Kiefte-de Jong, Jessica |e verfasserin |4 aut | |
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