摘要
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.
| 源语言 | 英语 |
|---|---|
| 页(从-至) | 972-982 |
| 页数 | 11 |
| 期刊 | Nature Human Behaviour |
| 卷 | 4 |
| 期 | 9 |
| DOI | |
| 出版状态 | 已出版 - 1 9月 2020 |
| 已对外发布 | 是 |
联合国可持续发展目标
此成果有助于实现下列可持续发展目标:
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可持续发展目标 3 良好健康与福祉
指纹
探究 'Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing' 的科研主题。它们共同构成独一无二的指纹。引用此
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