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Seven symptoms jointly predict COVID-19 diagnosis

Peer-Reviewed Publication


Seven symptoms jointly predict COVID-19 diagnosis

image: RT-PCR testing for SARS-CoV-2 infection view more 

Credit: Imperial College London, CC BY 4.0 (

A set of 7 symptoms, considered together, can be used to maximize detection of COVID-19 in the community, according to a new paper published this week in PLOS Medicine by Marc Chadeau-Hyam and Paul Elliott of Imperial College London, UK, and colleagues.


The rapid detection of SARS-CoV-2 infection in the community is key to ensuring efficient control of transmission. When testing capacity is limited, it is important to use tests in the most efficient way possible, including using the most informative symptoms for test allocation. In the new study, researchers obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,345 volunteers in England aged 5 years and above. The data were collected over 8 testing rounds conducted between June 2020 and January 2021 as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Participants were asked about symptoms they experienced in the week prior to testing.


A model was developed based on the data obtained during rounds 2 to 7, with 7 symptoms selected as jointly positively predictive of PCR positivity: loss or change of smell, loss or change of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The first 4 of those symptoms are currently used in the UK to determine eligibility for community PCR testing. In round 8 of testing, the resulting model predicted PCR positivity with an area under the curve of 0.77, and testing people in the community with at least 1 of the 7 selected positively predictive symptoms gave sensitivity, specificity, and positive predictive values of 74%, 64%, and 9.7%, respectively. Modeling suggested that the use of the 7 symptoms identified for PCR test allocation would result in 30% to 40% of symptomatic individuals in England being eligible for a test (versus 10% currently) and, if all those eligible were tested, would result in the detection of 70% to 75% of positive cases.


“In order to improve PCR positivity detection rates and consequently improve control of viral transmission via isolation measures, we would propose to extend the list of symptoms used for triage to all 7 symptoms we identified,” the authors say.


“These findings suggest many people with COVID-19 won't be getting tested – and therefore won't be self-isolating – because their symptoms don’t match those used in current public health guidance to help identify infected people,” Elliott adds. “We understand that there is a need for clear testing criteria, and that including lots of symptoms which are commonly found in other illnesses like seasonal flu could risk people self-isolating unnecessarily. I hope that our findings on the most informative symptoms mean that the testing programme can take advantage of the available evidence, helping to optimise the detection of infected people.”





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Citation: Elliott J, Whitaker M, Bodinier B, Eales O, Riley S, Ward H, et al. (2021) Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people. PLoS Med 18(9): e1003777.


Funding: This work was funded by the Department of Health and Social Care in England. MC-H and MW acknowledge support from the H2020-EXPANSE project (Horizon 2020 grant No 874627). MC-H, JE, and BB acknowledge support from Cancer Research UK, Population Research Committee Project grant 'Mechanomics’ (grant No 22184 to MC-H). HW is a NIHR Senior Investigator and acknowledges support from NIHR Biomedical Research Centre of Imperial College NHS Trust, NIHR School of Public Health Research, NIHR Applied Research Collaborative North West London, Wellcome Trust (UNS32973). SR acknowledges support from MRC Centre for Global Infectious Disease Analysis, National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU), Wellcome Trust (200861/Z/16/Z, 200187/Z/15/Z), and Centres for Disease Control and Prevention (US, 442 U01CK0005-01-02). GC is supported by an NIHR Professorship. PE is Director of the MRC Centre for Environment and Health (MR/L01341X/1, MR/S019669/1), and BB received a studentship from this Centre. PE acknowledges support from the National Institute for Health Research Imperial Biomedical Research Centre and the NIHR Health Protection Research Units in Chemical and Radiation Threats and Hazards, and in Environmental Exposures and Health, the British Heart Foundation (BHF) Centre for Research Excellence at Imperial College London (RE/18/4/34215) and the UK Dementia Research Institute at Imperial (MC_PC_17114). We thank The Huo Family Foundation for their support of our work on COVID-19. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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