TY - JOUR
T1 - Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets
AU - Fyles, Martyn
AU - Vihta, Karina-Doris
AU - Sudre, Carole H.
AU - Long, Harry
AU - Das, Rajenki
AU - Jay, Caroline
AU - Wingfield, Tom
AU - Cumming, Fergus
AU - Green, William
AU - Hadjipantelis, Pantelis
AU - Kirk, Joni
AU - Steves, Claire J.
AU - Ourselin, Sebastien
AU - Medley, Graham F.
AU - Fearon, Elizabeth
AU - House, Thomas
N1 - Funding Information: CSS funding: ZOE provided in-kind support for all aspects of building, running, and supporting the ZOE app and service to all users worldwide. Support for this study was provided by the National Institute for Health Research (NIHR)-funded Biomedical Research Centre based at Guy’s and St Thomas’ (GSTT) NHS Foundation Trust. This work was supported by the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare (104691). Investigators also received support from the Wellcome Trust (WT203148/Z/16/Z, WT213038/Z/18/Z, and W212904/Z/18/Z), Medical Research Council (MRC; MR/V005030/1 and MR/M004422/1), British Heart Foundation, Alzheimer’s Society, EU, NIHR, COVID-19 Driver Relief Fund, Innovate UK, the NIHR-funded BioResource, and the Clinical Research Facility and Biomedical Research Centre based at GSTT NHS Foundation Trust, in partnership with Kings College London. This work was also supported by the National Core Studies, an initiative funded by UK Research and Innovation, NIHR, and the Health and Safety Executive. The COVID-19 Longitudinal Health and Wellbeing National Core Study was funded by the MRC (MC_PC_20030). CIS funding: The ONS CIS is funded by the Department of Health and Social Care with in-kind support from the Welsh Government, the Department of Health on behalf of the Northern Ireland Government and the Scottish Government. Individual funding: MF is supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1. K-DV is supported by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with Public Health England (PHE) (NIHR200915). RD and TH are supported by the Engineering and Physical Sciences Research council (Award numbers 2373157 and EP/V027468/1). EF is supported by the Medical Research Council award MR/S020462/1; MF, EF, TW and TH are supported by the Medical Research Council award MR/V028618/1; TH is supported by the JUNIPER consortium (MR/V038613/1), the Royal Society (INF/R2/180067) and Alan Turing Institute for Data Science and Artificial Intelligence. SO was supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR-19-P3IA-0002). CHS was supported by the Alzheimer’s Society Junior Fellowship (AS-JF-17-011). TW is supported by grants from the Wellcome Trust, the Medical Research Council, and the Foreign Commonwealth and Development Office Joint Global Health Trials (MR/V004832/1 and 209075/Z/17/Z), the Medical Research Foundation (MRF-131-0006-RG-KHOS-C0942), and the Swedish Research Council. The views expressed are those of the authors and not necessarily those of the National Institute for Health Research, UK Health Security Agency or the Department of Health and Social Care. The funders/sponsors did not have any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Publisher Copyright: © 2023, The Author(s).
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.
AB - Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.
UR - http://www.scopus.com/inward/record.url?scp=85178875698&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41598-023-47488-9
DO - https://doi.org/10.1038/s41598-023-47488-9
M3 - Article
C2 - 38065987
SN - 2045-2322
VL - 13
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 21705
ER -