Testing the Accuracy of a Digital Test to Diagnose Covid-19
Validation of Machine Learning (ML) Models as Diagnostic Tools to Predict Infection With SARS-CoV-2
1 other identifier
observational
1,000,000
1 country
1
Brief 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jun 2020
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
May 27, 2020
CompletedFirst Posted
Study publicly available on registry
May 29, 2020
CompletedStudy Start
First participant enrolled
June 1, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
May 10, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
May 10, 2023
CompletedMarch 31, 2022
March 1, 2022
2.9 years
May 27, 2020
March 30, 2022
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
SARS-CoV-2 infection
Likelihood of infection with Covid-19, based on app-reported symptoms
3 days
SARS-CoV-2 infection
Active infection with Covid-19 as assessed by PCR swab test
1 day
Study Arms (1)
Covid-19 Symptom Study app-user
UK-based Covid-19 Symptom Study primary app-user completing self-reports in the app
Interventions
Participants satisfying machine learning test criteria will be asked to take a swab test for Covid-19.
Eligibility Criteria
The study population includes individuals are UK-based primary users of the Covid Symptom Study app, who provide informed consent to participate.
You may qualify if:
- Are based in the UK (are using the UK version of the Covid-19 Symptom Study app, and have listed a UK postcode)
- Are the primary app user (are reporting directly for themselves)
- Are at least 18 years of age
- Have not tested positive for a Covid-19 test before (but may have been tested)
You may not qualify if:
- Do not provide informed consent to participate
- Participants will be subject to further screening to identify them as eligible for swab testing during the course of the study.
- Have reported in the app at least once in the previous 3 days (days -2 to 0), and at least two times in the previous 9 days (days -8 to 0). All reports must be healthy (i.e. not experiencing any symptoms).
- On the previous day (day 1), have reported that they are experiencing at least one symptom described in the app. Symptoms in the app are updated when deemed appropriate by study investigators using evidence based reports in the scientific and medical field.
- Have answered the phenotype fields required for the prediction model with physiologically plausible values.
- Are asymptomatic
- Insufficient testing capacity:
- If insufficient testing capacity is available for the study population as described, then recruitment will be prioritised according to:
- Firstly, most recent final healthy report before reporting symptoms
- Secondly, highest number of healthy reports during the previous 9 days before reporting symptoms
- Thirdly, randomised selection to stratify between participants of equal priority according to the first two rules above.
- Excess testing capacity:
- Specifically, on day 7 of each validation phase, investigators will assess:
- What excess testing capacity is available, if any
- Which subgroups are under-represented compared to their proportion in the UK population (as best as can be established given that some participants may not have completed some phenotype fields):
- +2 more criteria
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- King's College Londonlead
- Zoe Global Limitedcollaborator
- Department of Health, United Kingdomcollaborator
Study Sites (1)
King's College London
London, SE1 9NH, United Kingdom
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
May 27, 2020
First Posted
May 29, 2020
Study Start
June 1, 2020
Primary Completion
May 10, 2023
Study Completion
May 10, 2023
Last Updated
March 31, 2022
Record last verified: 2022-03
Data Sharing
- IPD Sharing
- Will not share