Real-time tracking of self-reported symptoms to predict potential COVID-19
King's College London · Nottingham City Hospital · +4 more institutions
Indexed incrossrefpubmed
Abstract
No abstract available for this paper.
Citation impact
1,512
total citations
- FWCI
- 119.64
- Percentile
- 100%
- References
- 13
Citations per year
Authors
20Topics & keywords
Topics
Keywords
- Coronavirus disease 2019 (COVID-19)
- 2019-20 coronavirus outbreak
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
- Tracking (education)
- Medicine
- Betacoronavirus
- Pandemic
- Virology
No related works found for this paper.
Funding
- SFSteele Foundation
- WWellcomeAward: WT203148/Z/16/Z
- MGMassachusetts General Hospital
- WTWellcome TrustAwards: WT203148/Z/16/Z, 212904/Z/18/Z, WT213038/Z/18/Z
- CDChronic Disease Research FoundationAward: 212904/Z/18/Z
- URUK Research and Innovation
- ASAlzheimer's SocietyAward: AS-JF-17-011
- MCMassachusetts Consortium on Pathogen Readiness
- NINational Institute for Health and Care Research
- BHBritish Heart FoundationAward: MR/M016560/1
- KCKing's College London
- ECEuropean CommissionAward: COVID-19
- SUStand Up To Cancer
- MRMedical Research CouncilAwards: MR/M004422/1, MR/M016560/1, AIMHY; MR/M016560/1, MR/M016560/1, COVID-19
- EAEngineering and Physical Sciences Research CouncilAwards: WT203148/Z/16/Z, WT213038/Z/18/Z
- HSHealth Services Research Programme
- NNNIHR Nottingham Biomedical Research Centre
- CFCentre For Medical Engineering, King’s College LondonAward: WT203148/Z/16/Z