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Summary Background Multiple voluntary surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of population-based COVID-19 epidemiology. During this time, testing criteria broadened and health-care policies matured. We aimed to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three surveillance platforms in three countries (two platforms per country), during periods of testing and policy changes. Methods For this observational study, we used data of observations from three volunteer COVID-19 digital surveillance platforms (Carnegie Mellon University and University of Maryland Facebook COVID-19 Symptom Survey, ZOE COVID Symptom Study app, and the Corona Israel study) targeting communities in three countries (Israel, the UK, and the USA; two platforms per country). The study population included adult respondents (age 18–100 years at baseline) who were not health-care workers. We did logistic regression of self-reported symptoms on self-reported SARS-CoV-2 test status (positive or negative), adjusted for age and sex, in each of the study cohorts. We compared odds ratios (ORs) across platforms and countries, and we did meta-analyses assuming a random effects model. We also evaluated testing policy changes, COVID-19 incidence, and time scales of duration of symptoms and symptom-to-test time. Findings Between April 1 and July 31, 2020, 514 459 tests from over 10 million respondents were recorded in the six surveillance platform datasets. Anosmia–ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test (robust aggregated rank one, meta-analysed random effects OR 16·96, 95% CI 13·13–21·92). Fever (rank two, 6·45, 4·25–9·81), shortness of breath (rank three, 4·69, 3·14–7·01), and cough (rank four, 4·29, 3·13–5·88) were also highly associated with test positivity. The association of symptoms with test status varied by duration of illness, timing of the test, and broader test criteria, as well as over time, by country, and by platform. Interpretation The strong association of anosmia–ageusia with self-reported positive SARS-CoV-2 test was consistently observed, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform, country, phase of illness, or testing policy. These findings show that associations between COVID-19 symptoms and test positivity ranked similarly in a wide range of scenarios. Anosmia, fever, and respiratory symptoms consistently had the strongest effect estimates and were the most appropriate empirical signals for symptom-based public health surveillance in areas with insufficient testing or benchmarking capacity. Collaborative syndromic surveillance could enhance real-time epidemiological investigations and public health utility globally. Funding National Institutes of Health, National Institute for Health Research, Alzheimer's Society, Wellcome Trust, and Massachusetts Consortium on Pathogen Readiness.

Summary Background Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not suitable for the early detection of infection. We aimed to estimate the probability of an individual being infected with SARS-CoV-2 on the basis of early self-reported symptoms to enable timely self-isolation and urgent testing. Methods In this large-scale, prospective, epidemiological surveillance study, we used prospective, observational, longitudinal, self-reported data from participants in the UK on 19 symptoms over 3 days after symptoms onset and COVID-19 PCR test results extracted from the COVID-19 Symptom Study mobile phone app. We divided the study population into a training set (those who reported symptoms between April 29, 2020, and Oct 15, 2020) and a test set (those who reported symptoms between Oct 16, 2020, and Nov 30, 2020), and used three models to analyse the self-reported symptoms: the UK's National Health Service (NHS) algorithm, logistic regression, and the hierarchical Gaussian process model we designed to account for several important variables (eg, specific COVID-19 symptoms, comorbidities, and clinical information). Model performance to predict COVID-19 positivity was compared in terms of sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the test set. For the hierarchical Gaussian process model, we also evaluated the relevance of symptoms in the early detection of COVID-19 in population subgroups stratified according to occupation, sex, age, and body-mass index. Findings The training set comprised 182 991 participants and the test set comprised 15 049 participants. When trained on 3 days of self-reported symptoms, the hierarchical Gaussian process model had a higher prediction AUC (0·80 [95% CI 0·80–0·81]) than did the logistic regression model (0·74 [0·74–0·75]) and the NHS algorithm (0·67 [0·67–0·67]). AUCs for all models increased with the number of days of self-reported symptoms, but were still high for the hierarchical Gaussian process model at day 1 (0·73 [95% CI 0·73–0·74]) and day 2 (0·79 [0·78–0·79]). At day 3, the hierarchical Gaussian process model also had a significantly higher sensitivity, but a non-statistically lower specificity, than did the two other models. The hierarchical Gaussian process model also identified different sets of relevant features to detect COVID-19 between younger and older subgroups, and between health-care workers and non-health-care workers. When used during different pandemic periods, the model was robust to changes in populations. Interpretation Early detection of SARS-CoV-2 infection is feasible with our model. Such early detection is crucial to contain the spread of COVID-19 and efficiently allocate medical resources. Funding ZOE, the UK Government Department of Health and Social Care, the Wellcome Trust, the UK Engineering and Physical Sciences Research Council, the UK National Institute for Health Research, the UK Medical Research Council, the British Heart Foundation, the Alzheimer's Society, the Chronic Disease Research Foundation, and the Massachusetts Consortium on Pathogen Readiness.

BackgroundSelf-reported symptom studies rapidly increased our understanding of SARS-CoV-2 during the pandemic and enabled the monitoring of long-term effects of COVID-19 outside the hospital setting. It is now evident that post-COVID syndrome presents with heterogeneous profiles, which need characterisation to enable personalised care among the most affected survivors. This study describes post-COVID profiles, and how they relate to different viral variants and vaccination status.MethodsIn this prospective longitudinal cohort study, we analysed data from 336,652 subjects, with regular health reports through the Covid Symptom Study (CSS) smartphone application. These subjects had reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2. 9,323 individuals subsequently developed Long-COVID, defined as symptoms lasting longer than 28 days. 1,459 had post-COVID syndrome, defined as more than 12 weeks of symptoms. Clustering analysis of the time-series data was performed to identify distinct symptom profiles for post-COVID patients, across variants of SARS-CoV-2 and vaccination status at the time of infection. Clusters were then characterised based on symptom prevalence, duration, demography, and prior conditions (comorbidities).Using an independent testing sample with additional data (n=140), we investigated the impact of post-COVID symptom clusters on the lives of affected individuals.FindingsWe identified distinct profiles of symptoms for post-COVID syndrome within and across variants: four endotypes were identified for infections due to the wild-type variant; seven for the alpha variant; and five for delta. Across all variants, a cardiorespiratory cluster of symptoms was identified. A second cluster related to central neurological, and a third to cases with the most severe and debilitating multi-organ symptoms. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant. The three main clusters were confirmed in an independent testing sample, and their functional impact was assessed.InterpretationUnsupervised analysis identified different post-COVID profiles, characterised by differing symptom combinations, durations, and functional outcomes. Phenotypes were at least partially concordant with individuals’ reported experiences.Our classification may be useful to understand distinct mechanisms of the post-COVID syndrome, as well as subgroups of individuals at risk of prolonged debilitation.FundingUK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation and Alzheimer’s Society, and ZOE Limited, UK.Research in contextEvidence before this studyWe conducted a search in the PubMed Central database, with keywords: (“Long-COVID*” OR “post?covid*” OR “post?COVID*” OR postCOVID* OR postCovid*) AND (cluster* OR endotype* OR phenotype* OR sub?type* OR subtype).On 15 June 2022, 161 documents were identified, of which 24 either provided descriptions of sub-types or proposed phenotypes of Long-COVID or post-COVID syndrome(s). These included 16 studies attempting manual sub-grouping of phenotypes, 6 deployments of unsupervised methods for patient clustering and automatic semantic phenotyping (unsupervised k-means=2; random forest classification=1; other=2), and two reports of uncommon presentations of Long-COVID/post-COVID syndrome. Overall, two to eight symptom profiles (clusters) were identified, with three recurring clusters. A cardiopulmonary syndrome was the predominant observation, manifesting with exertional intolerance and dyspnoea (n=10), fatigue (n=8), autonomic dysfunction, tachycardia or palpitations (n=5), lung radiological abnormalities including fibrosis (n=2), and chest pain (n=1). A second common presentation consisted in persistent general autoimmune activation and proinflammatory state (n=2), comprising multi-organ mild sequelae (n=2), gastrointestinal symptoms (n=2), dermatological symptoms (n=2), and/or fever (n=1). A third syndrome was reported, with neurological or neuropsychiatric symptoms: brain fog or dizziness (n=2), poor memory or cognition (n=2), and other mental health issues including mood disorders (n=5), headache (n=2), central sensitization (n=1), paresthesia (n=1), autonomic dysfunction (n=1), fibromyalgia (n=2), and chronic pain or myalgias (n=6). Unsupervised clustering methods identified two to six different post-COVID phenotypes, mapping to the ones described above.14 further documents focused on possible causes and/or mechanisms of disease underlying one or more manifestations of Long-COVID or post-COVID and identifying immune response dysregulation as a potential common element. All the other documents were beyond the scope of this work.To our knowledge, there are no studies examining the symptom profile of post-COVID syndrome between different variants and vaccination status. Also, no studies reported the modelling of longitudinally collected symptoms, as time-series data, aiming at the characterisation of post-COVID syndrome.Added-value of this studyOur study aimed to identify symptom profiles for post-COVID syndrome across the dominant variants in 2020 and 2021, and across vaccination status at the time of infection, using a large sample with prospectively collected longitudinal self-reports of symptoms. For individuals developing 12 weeks or more of symptoms, we identified three main symptom profiles which were consistent across variants and by vaccination status, differing only in the ratio of individuals affected by each profile and symptom duration overall.Implications of all the available evidenceWe demonstrate the existence of different post-COVID syndromes, which share commonalities across SARS-CoV-2 variant types in both symptoms themselves and how they evolved through the illness. We describe subgroups of patients with specific post-COVID presentations which might reflect different underlying pathophysiological mechanisms. Given the time-series component, our study is relevant for post-COVID prognostication, indicating how long certain symptoms last. These insights could aid in the development of personalised diagnosis and treatment, as well as helping policymakers plan for the delivery of care for people living with post-COVID syndrome.

Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called ‘long COVID’, are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app1. A total of 558 (13.3%) participants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76-4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavirus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services.

Introduction COVID-19 exhibits a more severe disease course in older adults with frailty. Awareness of atypical presentations is critical to facilitate early disease identification. This study aimed to assess how frailty affects presenting symptoms of COVID-19 in older adults. Methods Observational study of two distinct cohorts: (i) Hospitalised patients aged 65 and over; unscheduled admission to a large London teaching hospital between March 1st, 2020-May 5th, 2020; COVID-19 confirmed by RT-PCR of nasopharyngeal swab (n = 322); (ii) Community-based adults aged 65 and over enrolled in the COVID Symptom Study mobile application between March 24th (application launch)-May 8th, 2020; self-report or report-by-proxy data; reported test-positive for COVID-19 (n = 535). Multivariable logistic regression analysis performed on age-matched samples of both cohorts to determine associations between frailty and symptoms of COVID-19 including delirium, fever and cough. Results Hospital cohort: there was a significantly higher prevalence of delirium amongst the frail sample, with no difference in fever or cough. Of those presenting with delirium, 10/53 (18.9%) presented with delirium as the only documented symptom. Community-based cohort: there was a significantly higher prevalence of probable delirium in the frail sample, and also of fatigue and shortness of breath. Of those reporting probable delirium, 28/84 (33%) did not report fever or cough. Conclusions This study demonstrates a higher prevalence of delirium as a presenting symptom of COVID-19 infection in older adults with frailty compared to their age-matched non-frail counterparts. Clinicians should suspect COVID-19 in frail older adults presenting with delirium. Early detection facilitates infection control measures to mitigate against catastrophic spread and preventable hospitalisations and deaths amongst this population. Our findings emphasise the need for systematic frailty assessment for all acutely ill older patients in both hospital and community settings, as well as systematic evaluation of any change in mental status.

Summary Background In children, SARS-CoV-2 infection is usually asymptomatic or causes a mild illness of short duration. Persistent illness has been reported; however, its prevalence and characteristics are unclear. We aimed to determine illness duration and characteristics in symptomatic UK school-aged children tested for SARS-CoV-2 using data from the COVID Symptom Study, one of the largest UK citizen participatory epidemiological studies to date. Methods In this prospective cohort study, data from UK school-aged children (age 5–17 years) were reported by an adult proxy. Participants were voluntary, and used a mobile application (app) launched jointly by Zoe Limited and King's College London. Illness duration and symptom prevalence, duration, and burden were analysed for children testing positive for SARS-CoV-2 for whom illness duration could be determined, and were assessed overall and for younger (age 5–11 years) and older (age 12–17 years) groups. Children with longer than 1 week between symptomatic reports on the app were excluded from analysis. Data from symptomatic children testing negative for SARS-CoV-2, matched 1:1 for age, gender, and week of testing, were also assessed. Findings 258 790 children aged 5–17 years were reported by an adult proxy between March 24, 2020, and Feb 22, 2021, of whom 75 529 had valid test results for SARS-CoV-2. 1734 children (588 younger and 1146 older children) had a positive SARS-CoV-2 test result and calculable illness duration within the study timeframe (illness onset between Sept 1, 2020, and Jan 24, 2021). The most common symptoms were headache (1079 [62·2%] of 1734 children), and fatigue (954 [55·0%] of 1734 children). Median illness duration was 6 days (IQR 3–11) versus 3 days (2–7) in children testing negative, and was positively associated with age (Spearman's rank-order rs 0·19, p

BackgroundWe aimed to explore the effectiveness of one-dose BNT162b2 vaccination upon SARS-CoV-2 infection, its effect on COVID-19 presentation, and post-vaccination symptoms in children and young people (CYP) in the UK during periods of Delta and Omicron variant predominance.MethodsIn this prospective longitudinal cohort study, we analysed data from 115,775 CYP aged 12-17 years, proxy-reported through the Covid Symptom Study (CSS) smartphone application. We calculated post-vaccination infection risk after one dose of BNT162b2, and described the illness profile of CYP with post-vaccination SARS- CoV-2 infection, compared to unvaccinated CYP, and post-vaccination side-effects.FindingsBetween August 5, 2021 and February 14, 2022, 25,971 UK CYP aged 12-17 years received one dose of BNT162b2 vaccine. Vaccination reduced (proxy-reported) infection risk (-80·4% and -53·7% at 14–30 days with Delta and Omicron variants respectively, and -61·5% and -63·7% after 61–90 days). The probability of remaining infection-free diverged soon after vaccination, and was greater in CYP with prior SARS-CoV-2 infection. Vaccinated CYP who contracted SARS-CoV-2 during the Delta period had milder disease than unvaccinated CYP; during the Omicron period this was only evident in children aged 12-15 years. Overall disease profile was similar in both vaccinated and unvaccinated CYP. Post-vaccination local side-effects were common, systemic side-effects were uncommon, and both resolved quickly.InterpretationOne dose of BNT162b2 vaccine reduced risk of SARS-CoV-2 infection for at least 90 days in CYP aged 12-17 years. Vaccine protection varied for SARS-CoV-2 variant type (lower for Omicron than Delta variant), and was enhanced by pre-vaccination SARS-CoV-2 infection. Severity of COVID-19 presentation after vaccination was generally milder, although unvaccinated CYP also had generally mild disease. Overall, vaccination was well-tolerated.FundingUK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation and Alzheimer’s Society, and ZOE Limited.Research in contextEvidence before this study:We searched PubMed database for peer-reviewed articles and medRxiv for preprint papers, published between January 1, 2021 and February 15, 2022 using keywords (“SARS-CoV-2” OR “COVID-19”) AND (child* OR p?ediatric* OR teenager*) AND (“vaccin*” OR “immunization campaign”) AND (“efficacy” OR “effectiveness” OR “symptoms”) AND (“delta” or “omicron” OR “B.1.617.2” OR “B.1.1.529”). The PubMed search retrieved 36 studies, of which fewer than 30% specifically investigated individuals Eleven studies explored SARS-CoV-2 viral transmission: seroprevalence in children (n=4), including age-dependency of susceptibility to SARS-CoV-2 infection (n=1), SARS-CoV-2 transmission in schools (n=5), and the effect of school closure on viral transmission (n=1).Eighteen documents reported clinical aspects, including manifestation of infection (n=13), symptomatology, disease duration, and severity in children. Other studies estimated emergency department visits, hospitalization, need for intensive care, and/or deaths in children (n=4), and explored prognostic factors (n=1).Thirteen studies explored vaccination-related aspects, including vaccination of children within specific paediatric co-morbidity groups (e.g., children with Down syndrome, inflammatory bowel disease, and cancer survivors, n=4), mRNA vaccine efficacy in children and adolescents from the general population (n=7), and the relation between vaccination and severity of disease and hospitalization cases (n=2). Four clinical trials were conducted using mRNA vaccines in minors, also exploring side effects. Sixty percent of children were found to have side effects after BNT162b2 vaccination, and especially after the second dose; however, most symptoms were mild and transient apart from rare uncomplicated skin ulcers. Two studies focused on severe adverse effects and safety of SARS-CoV-2 vaccines in children, reporting on myocarditis episodes and two cases of Guillain-Barrè syndrome. All other studies were beyond the scope of our research.Added value of this study:We assessed multiple components of the UK vaccination campaign in a cohort of children and young people (CYP) aged 12-17 years drawn from a large UK community-based citizen-science study, who received a first dose of BNT162b2 vaccine. We describe a variant-dependent protective effect of the first dose against both Delta and Omicron, with additional protective effect of pre-vaccination SARS- CoV-2 infection on post-vaccination re-infection. We compare the illness profile in CYP infected post-vaccination with that of unvaccinated CYP, demonstrating overall milder disease with fewer symptoms for vaccinated CYP. We describe local and systemic side-effects during the first week following first-dose vaccination, confirming that local symptoms are common, systemic symptoms uncommon, and both usually transient.Implications of all the available evidence:Our data confirm that first dose BNT162b2 vaccination in CYP reduces risk of infection by SARS-CoV-2 variants, with generally local and brief side-effects. If infected after vaccination, COVID-19 is milder, if manifest at all. The study aims to contribute quantitative evidence to the risk-benefit evaluation of vaccination in CYP to inform discussion regarding rationale for their vaccination and the designing of national immunisation campaigns for this age group; and applies citizen-science approaches in the conduct of epidemiological surveillance and data collection in the UK community.Importantly, this study was conducted during Delta and Omicron predominance in UK; specificity of vaccine efficacy to variants is also illustrated; and results may not be generalizable to future SARS-CoV-2 strains.

PurposeThe Centiloid scale provides a systematic means of harmonising amyloid-β PET measures across different acquisition and processing methodologies. This work explores the Centiloid transformation of [18F]florbetapir PET data acquired on a combined PET/MR scanner and processed with methods that differ from the standard Centiloid pipeline.MethodsThe Standard PiB and Florbetapir Calibration datasets were processed using a standardised uptake value ratio (SUVR) pipeline with MRI parcellations from the Geodesic Information Flow (GIF) algorithm in native PET space. We generated SUVRs using whole cerebellum (GIF_WCSUVR) and eroded white matter (GIF_WMSUVR) reference regions, with and without partial volume correction (PVC). Linear regression was used to calibrate these processing pipelines to the standard Centiloid approach. We then applied the resulting transformation to 432 florbetapir scans from the Insight 46 study of mostly cognitively normal individuals aged ∼70 years, and defined Centiloid cutpoints for amyloid-β positivity using Gaussian-mixture modelling.ResultsGIF-based SUVR processing pipelines were suitable for conversion according to Centiloid criteria. For GIF_WCSUVR, cutpoints translated to 14.2 Centiloids, or 11.8 with PVC. There was a differential relationship between florbetapir uptake in WM and WC regions in Florbetapir Calibration and Insight 46 datasets, causing implausibly low Centiloid values for GIF_WMSUVR. Linear adjustment to account for this difference resulted in Centiloid cutpoints of 18.1 for GIF_WMSUVR (17.0 with PVC).ConclusionOur results show florbetapir SUVRs acquired on PET/MR scanners can be reliably converted to Centiloids. Acquisition or biological factors can have large effects on Centiloid values from different datasets, we propose a correction to account for these effects.

BACKGROUND: The SARS-CoV-2 variant of concern, omicron, appears to be less severe than delta. We aim to quantify the differences in symptom prevalence, risk of hospital admission, and symptom duration among the vaccinated population. METHODS: In this prospective longitudinal observational study, we collected data from participants who were self-reporting test results and symptoms in the ZOE COVID app (previously known as the COVID Symptoms Study App). Eligible participants were aged 16-99 years, based in the UK, with a body-mass index between 15 and 55 kg/m2, had received at least two doses of any SARS-CoV-2 vaccine, were symptomatic, and logged a positive symptomatic PCR or lateral flow result for SARS-CoV-2 during the study period. The primary outcome was the likelihood of developing a given symptom (of the 32 monitored in the app) or hospital admission within 7 days before or after the positive test in participants infected during omicron prevalence compared with those infected during delta prevalence. FINDINGS: Between June 1, 2021, and Jan 17, 2022, we identified 63 002 participants who tested positive for SARS-CoV-2 and reported symptoms in the ZOE app. These patients were matched 1:1 for age, sex, and vaccination dose, across two periods (June 1 to Nov 27, 2021, delta prevalent at >70%; n=4990, and Dec 20, 2021, to Jan 17, 2022, omicron prevalent at >70%; n=4990). Loss of smell was less common in participants infected during omicron prevalence than during delta prevalence (16·7% vs 52·7%, odds ratio [OR] 0·17; 95% CI 0·16-0·19, p