Name (Synonyms) | Correlation |
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Name (Synonyms) | Correlation | |
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D002318 | Cardiovascular Diseases NIH | 0.21 |
D018352 | Coronavirus Infections NIH | 0.04 |
Name (Synonyms) | Correlation | |
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HP:0001626 | Abnormality of the cardiovascular system HPO | 0.21 |
There is one clinical trial.
The Covid-19 viral pandemic has caused significant global losses and disruption to all aspects of society. One of the major difficulties in controlling the spread of this coronavirus has been the delayed and mild (or lack of) presentation of symptoms in infected individuals, and the insufficient Covid-19 testing capacity in the UK. This warrants the development of alternative diagnostic tools that reliably assess Covid-19 infection in the early stages of infection, while also being low- cost, low-burden, and easily administered to a wide proportion of the population. This study aims to validate machine learning models as a diagnostic tool that predicts infection with SARS-CoV-2 based on app-reported symptoms and phenotypic data, against the 'gold-standard' swab PCR-test. This study will take place within the Covid Symptom Study app, the free symptom tracking mobile application launched in March 2020.
Description: Likelihood of infection with Covid-19, based on app-reported symptoms
Measure: SARS-CoV-2 infection Time: 3 daysDescription: Active infection with Covid-19 as assessed by PCR swab test
Measure: SARS-CoV-2 infection Time: 1 day