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Testing the Accuracy of a Digital Test to Diagnose Covid-19

NCT04407585 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1000000

Last updated 2022-03-31

No results posted yet for this study

Summary

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.

Conditions

Interventions

DIAGNOSTIC_TEST

Covid-19 swab PCR test

Participants satisfying machine learning test criteria will be asked to take a swab test for Covid-19.

Sponsors & Collaborators

  • Zoe Global Limited

    collaborator OTHER

  • Department of Health, United Kingdom

    collaborator OTHER_GOV

  • King's College London

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-06-01
Primary Completion
2023-05-10
Completion
2023-05-10

Countries

  • United Kingdom

Study Locations

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Entities

Diseases
Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04407585 on ClinicalTrials.gov