Profiling post-COVID syndrome across different variants of SARS-CoV-2 (preprint)

Background: Self-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. Methods: In 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. Findings: We 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. Interpretation: Unsupervised 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. Funding: UK 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 Alzheimers Society, and ZOE Limited, UK.

Vaccination against SARS-CoV-2 in UK school-aged children and young people decreases infection rates and reduces COVID-19 symptoms (preprint)

Background We aim to explore the effectiveness of one-dose BNT162b2 vaccination upon SARS-CoV-2 infection rates in children and young people (CYP) during Delta and Omicron variant predominance in the UK, and study its effect on COVID-19 presentation and post-vaccination symptoms. Methods In 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. We described the illness profile of CYP with post-vaccination SARS-CoV-2 infection, compared to unvaccinated CYP. Findings Between August 5, 2021 and February 14, 2022, 25,971 UK CYP aged 12-17 years received one dose of BNT162b2 vaccine. Vaccination reduced infection (reporting) 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 after vaccination, and was more robust with prior infection. Vaccinated CYP who contracted SARS-CoV-2 during the Delta period had milder disease than unvaccinated CYP; however, during the Omicron period this was only evident in children aged 12-15 years, and 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. Interpretation One dose of BNT162b2 vaccine reduced risk of SARS-CoV-2 infection for at least 90 days in CYP aged 12-17 years. Vaccine protection was modulated by 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 is generally milder, although unvaccinated CYP also have an uncomplicated course. Overall, vaccination was well-tolerated.

COVID-19 due to the B.1.617.2 (Delta) variant compared to B.1.1.7 (Alpha) variant of SARS-CoV-2: two prospective observational cohort studies (preprint)

Background The Delta (B.1.617.2) variant became the predominant UK circulating SARS-CoV-2 strain in May 2021. How Delta infection compares with previous variants is unknown. Methods This prospective observational cohort study assessed symptomatic adults participating in the app-based COVID Symptom Study who tested positive for SARS-CoV-2 from May 26 to July 1, 2021 (Delta overwhelmingly predominant circulating UK variant), compared (1:1, age- and sex-matched) with individuals presenting from December 28, 2020 to May 6, 2021 (Alpha (B.1.1.7) predominant variant). We assessed illness (symptoms, duration, presentation to hospital) during Alpha- and Delta-predominant timeframes; and transmission, reinfection, and vaccine effectiveness during the Delta-predominant period. Findings 3,581 individuals (aged 18 to 100 years) from each timeframe were assessed. The seven most frequent symptoms were common to both variants. Within the first 28 days of illness, some symptoms were more common with Delta vs. Alpha infection (including fever, sore throat and headache) and vice versa (dyspnoea). Symptom burden in the first week was higher with Delta vs. Alpha infection; however, the odds of any given symptom lasting [≥]7 days was either lower or unchanged. Illness duration [≥]28 days was lower with Delta vs. Alpha infection, though unchanged in unvaccinated individuals. Hospitalisation for COVID-19 was unchanged. The Delta variant appeared more (1.47) transmissible than Alpha. Re-infections were low in all UK regions. Vaccination markedly (69-84%) reduced risk of Delta infection. Interpretation COVID-19 from Delta or Alpha infections is clinically similar. The Delta variant is more transmissible than Alpha; however, current vaccines show good efficacy against disease. Funding UK Government Department of Health and Social Care, 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, Alzheimer's Society, and ZOE Limited.

Illness characteristics of COVID-19 in children infected with the SARS-CoV-2 Delta variant (preprint)

Background The Delta (B.1.617.2) SARSCoV2 variant became the predominant UK circulating strain in May 2021. Whether COVID19 from Delta infection differs to infection with other variants in children is unknown. Methods Through the prospective COVID Symptom Study, 109,626 UK school-aged children were proxy-reported between December 28, 2020 and July 8, 2021. We selected all symptomatic children who tested positive for SARS-CoV-2 and were proxy-reported at least weekly, within two timeframes: December 28, 2020 to May 6, 2021 (Alpha (B.1.1.7) the main UK circulating variant); and May 26 to July 8, 2021 (Delta the main UK circulating variant). We assessed illness profiles (symptom prevalence, duration, and burden), hospital presentation, and presence of long (>28 day) illness; and calculated odds ratios for symptoms presenting within the first 28 days of illness. Findings 694 (276 younger [5 11 years], 418 older [12 17 years]) symptomatic children tested positive for SARS-CoV-2 with Alpha infection and 706 (227 younger and 479 older) children with Delta infection. Median illness duration was short with either variant (overall cohort: 5 days (IQR 2 9.75) with Alpha, 5 days (IQR 2 9) with Delta). The seven most prevalent symptoms were common to both variants. Symptom burden over the first 28 days was slightly greater with Delta compared with Alpha infection (in younger children, 3 (IQR 2 5) with Alpha, 4 (IQR 2 7) with Delta; in older children 5 (IQR 3 8) with Alpha and 6 (IQR 3 9) with Delta infection in older children). The odds of several symptoms were higher with Delta than Alpha infection, including headache and fever. Few children presented to hospital, and long illness duration was uncommon, with either variant. Interpretation COVID-19 in UK school-aged children due to SARSCoV2 Delta strain B.1.617.2 resembles illness due to the Alpha variant B.1.1.7., with short duration and similar symptom burden.

App-based COVID-19 surveillance and prediction: The COVID Symptom Study Sweden (preprint)

Background The response of the Swedish authorities to the COVID-19 pandemic was less restrictive than in most countries during the first year, with infection and death rates substantially higher than in neighbouring Nordic countries. Because access to PCR testing was limited during the first wave (February to June 2020) and regional data were reported with delay, adequate monitoring of community disease spread was hampered. The app-based COVID Symptom Study was launched in Sweden to disseminate real-time estimates of disease spread and to collect prospective data for research. The aim of this study was to describe the research project, develop models for estimation of COVID-19 prevalence and to evaluate it for prediction of hospital admissions for COVID-19. Methods We enrolled 143 531 study participants ([≥]18 years) throughout Sweden, who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Data from 19 161 self-reported PCR tests were used to create a symptom-based algorithm to estimate daily prevalence of symptomatic COVID-19. The prediction model was validated using external datasets. We further utilized the model estimates to forecast subsequent new hospital admissions. Findings A prediction model for symptomatic COVID-19 based on 17 symptoms, age, and sex yielded an area under the ROC curve of 0.78 (95% CI 0.74-0.83) in an external validation dataset of 943 PCR-tested symptomatic individuals. App-based surveillance proved particularly useful for predicting hospital trends in times of insufficient testing capacity and registration delays. During the first wave, our prediction model estimates demonstrated a lower mean error (0.38 average new daily hospitalizations per 100 000 inhabitants per week (95% CI 0.32, 0.45)) for subsequent hospitalizations in the ten most populated counties, than a model based on confirmed case data (0.72 (0.64, 0.81)). The model further correctly identified on average three out of five counties (95% CI 2.3, 3.7) with the highest rates of hospitalizations the following week during the first wave and four out of five (3.0, 4.6) during the second wave. Interpretation The experience of the COVID Symptom Study highlights the important role citizens can play in real-time monitoring of infectious diseases, and how app-based data collection may be used for data-driven rapid responses to public health challenges.

Illness duration and symptom profile in a large cohort of symptomatic UK school-aged children tested for SARS-CoV-2 (preprint)

Background In children, SARS-CoV-2 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, the largest citizen participatory epidemiological study to date. Methods Data from 258,790 children aged 5-17 years were reported by an adult proxy between 24 March 2020 and 22 February 2021. Illness duration and symptom profiles were analysed for all children testing positive for SARS-CoV-2 for whom illness duration could be determined, considered overall and within younger (5-11 years) and older (12-17 years) age groups. Data from symptomatic children testing negative for SARS-CoV-2, matched 1:1 for age, gender, and week of testing, were also assessed. Findings 1,734 children (588 younger children, 1,146 older children) had a positive SARS-CoV-2 test result and calculable duration of illness with the study time frame. The commonest symptoms were headache (62.2%) and fatigue (55.0%). Median illness duration was six days (vs. three days in children testing negative); and was positively associated with age (rs 0.19, p<1.e-4) with median duration seven days in older vs. five days in younger children. Seventy-seven (4.4%) children had illness duration =>28 days (LC28); LC28 was more common in older compared with younger children (59 (5.1%) vs. 18 (3.1%), p=0.046). The commonest symptoms experienced by children with LC28 were fatigue (84.4%), headache and anosmia (both 77.9%); however, by day 28 the median symptom burden was two. Only 25 (1.8%) of 1,379 children experienced symptoms for [≥]56 days. Few children (15 children, 0.9%) in the negatively-tested cohort experienced prolonged symptom duration; however, these children experienced greater symptom burden (both throughout their illness and at day 28) than children positive for SARS-CoV-2. Interpretation Some children with COVID-19 experience prolonged illness duration; reassuringly, symptom burden in these children did not increase with time, and most recovered by day 56. Some children who tested negative for SARS-CoV-2 also had persistent and burdensome illness. Thus, a holistic approach for all children with persistent illness during the pandemic is required.

The effect of SARS-CoV-2 variant B.1.1.7 on symptomatology, re-infection and transmissibility (preprint)

The new SARS-CoV-2 variant B.1.1.7 was identified in December 2020 in the South-East of England, and rapidly increased in frequency and geographic spread. While there is some evidence for increased transmissibility of this variant, it is not known if the new variant presents with variation in symptoms or disease course, or if previously infected individuals may become reinfected with the new variant. Using longitudinal symptom and test reports of 36,920 users of the Covid Symptom Study app testing positive for COVID-19 between 28 September and 27 December 2020, we examined the association between the regional proportion of B.1.1.7 and reported symptoms, disease course, rates of reinfection, and transmissibility. We found no evidence for changes in reported symptoms, disease severity and disease duration associated with B.1.1.7. We found a likely reinfection rate of around 0.7% (95% CI 0.6-0.8), but no evidence that this was higher compared to older strains. We found an increase in R(t) by a factor of 1.35 (95% CI 1.02-1.69). Despite this, we found that regional and national lockdowns have reduced R(t) below 1 in regions with very high proportions of B.1.1.7.

Anosmia and other SARS-CoV-2 positive test-associated symptoms, across three national, digital surveillance platforms as the COVID-19 pandemic and response unfolded: an observation study (preprint)

Background: Multiple participatory surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of community-wide COVID-19 epidemiology. During this time, testing criteria broadened and healthcare policies matured. We sought to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three national surveillance platforms, during periods of testing and policy changes, and whether inconsistencies could better inform our understanding and future studies as the COVID-19 pandemic progresses. Methods: Four months (1st April 2020 to 31st July 2020) of observation through three volunteer COVID-19 digital surveillance platforms targeting communities in three countries (Israel, United Kingdom, and United States). Logistic regression of self-reported symptom on self-reported SARS-CoV-2 test status (or test access), adjusted for age and sex, in each of the study cohorts. Odds ratios over time were compared to known changes in testing policies and fluctuations in COVID-19 incidence. Findings: Anosmia/ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test, based on 658325 tests (5% positive) from over 10 million respondents in three digital surveillance platforms using longitudinal and cross-sectional survey methodologies. During higher-incidence periods with broader testing criteria, core COVID-19 symptoms were more strongly associated with test status. Lower incidence periods had, overall, larger confidence intervals. Interpretation: The strong association of anosmia/ageusia with self-reported SARS-CoV-2 test positivity is omnipresent, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform or testing policy. This analysis highlights that precise effect estimates, as well as an understanding of test access patterns to interpret differences, are best done only when incidence is high. These findings strongly support the need for testing access to be as open as possible both for real-time epidemiologic investigation and public health utility.

Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App (preprint)

Reports of "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 analysed data from 4182 incident cases of COVID-19 who logged their symptoms prospectively in the COVID Symptom Study app. 558 (13.3%) had symptoms lasting >28 days, 189 (4.5%) for >8 weeks and 95 (2.3%) for >12 weeks. Long-COVID was characterised by symptoms of fatigue, headache, dyspnoea and anosmia and was more likely with increasing age, BMI and female sex. Experiencing more than five symptoms during the first week of illness was associated with Long-COVID, OR=3.53 [2.76;4.50]. Our model to predict long-COVID at 7 days, which gained a ROC-AUC of 76%, was replicated in an independent sample of 2472 antibody positive individuals. This model could be used to identify individuals for clinical trials to reduce long-term symptoms and target education and rehabilitation services.

Estrogen and COVID-19 symptoms: associations in women from the COVID Symptom Study (preprint)

Background: Men and older women have been shown to be at higher risk of adverse COVID-19 outcomes. Animal model studies of SARS-CoV and MERS suggest that the age and sex difference in COVID-19 symptom severity may be due to a protective effect of the female sex hormone estrogen. Females have shown an ability to mount a stronger immune response to a variety of viral infections because of more robust humoral and cellular immune responses. Objectives: We sought to determine whether COVID-19 positivity increases in women entering menopause. We also aimed to identify whether premenopausal women taking exogenous hormones in the form of the combined oral contraceptive pill (COCP) and post-menopausal women taking hormone replacement therapy (HRT) have lower predicted rates of COVID-19, using our published symptom-based model. Design: The COVID Symptom Study developed by Kings College London and Zoe Global Limited was launched in the UK on 24th March 2020. It captured self-reported information related to COVID-19 symptoms. Data used for this study included records collected between 7th May - 15th June 2020. Main outcome measures: We investigated links between COVID-19 rates and 1) menopausal status, 2) COCP use and 3) HRT use, using symptom-based predicted COVID-19, tested COVID-19, and disease severity based on requirement for hospital attendance or respiratory support. Participants: Female users of the COVID Symptom Tracker Application in the UK, including 152,637 women for menopause status, 295,689 for COCP use, and 151,193 for HRT use. Analyses were adjusted for age, smoking and BMI. Results: Post-menopausal women aged 40-60 years had a higher rate of predicted COVID (P=0.003) and a corresponding range of symptoms, with consistent, but not significant trends observed for tested COVID-19 and disease severity. Women aged 18-45 years taking COCP had a significantly lower predicted COVID-19 (P=8.03E-05), with a reduction in hospital attendance (P=0.023). Post-menopausal women using HRT or hormonal therapies did not exhibit consistent associations, including increased rates of predicted COVID-19 (P=2.22E-05) for HRT users alone. Conclusions: Our findings support a protective effect of estrogen on COVID-19, based on positive association between predicted COVID-19 and menopausal status, and a negative association with COCP use. HRT use was positively associated with COVID-19 symptoms; however, the results should be considered with caution due to lack of data on HRT type, route of administration, duration of treatment, and potential comorbidities. Trial registration: The App Ethics has been approved by KCL ethics Committee REMAS ID 18210, review reference LRS-19/20-18210

Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app (preprint)

As no one symptom can predict disease severity or the need for dedicated medical support in COVID-19, we asked if documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between May 1- May 28th, 2020. Using the first 5 days of symptom logging, the ROC-AUC of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.

Key predictors of attending hospital with COVID19: An association study from the COVID Symptom Tracker App in 2,618,948 individuals (preprint)

Objectives: We aimed to identify key demographic risk factors for hospital attendance with COVID-19 infection. Design: Community survey Setting: The COVID Symptom Tracker mobile application co-developed by physicians and scientists at Kings College London, Massachusetts General Hospital, Boston and Zoe Global Limited was launched in the UK and US on 24th and 29th March 2020 respectively. It captured self-reported information related to COVID-19 symptoms and testing. Participants: 2,618,948 users of the COVID Symptom Tracker App. UK (95.7%) and US (4.3%) population. Data cut-off for this analysis was 21st April 2020. Main outcome measures: Visit to hospital and for those who attended hospital, the need for respiratory support in three subgroups (i) self-reported COVID-19 infection with classical symptoms (SR-COVID-19), (ii) self-reported positive COVID-19 test results (T-COVID-19), and (iii) imputed/predicted COVID-19 infection based on symptomatology (I-COVID-19). Multivariate logistic regressions for each outcome and each subgroup were adjusted for age and gender, with sensitivity analyses adjusted for comorbidities. Classical symptoms were defined as high fever and persistent cough for several days. Results: Older age and all comorbidities tested were found to be associated with increased odds of requiring hospital care for COVID-19. Obesity (BMI >30) predicted hospital care in all models, with odds ratios (OR) varying from 1.20 [1.11; 1.31] to 1.40 [1.23; 1.60] across population groups. Pre-existing lung disease and diabetes were consistently found to be associated with hospital visit with a maximum OR of 1.79 [1.64,1.95] and 1.72 [1.27; 2.31]) respectively. Findings were similar when assessing the need for respiratory support, for which age and male gender played an additional role. Conclusions: Being older, obese, diabetic or suffering from pre-existing lung, heart or renal disease placed participants at increased risk of visiting hospital with COVID-19. It is of utmost importance for governments and the scientific and medical communities to work together to find evidence-based means of protecting those deemed most vulnerable from COVID-19. Trial registration: The App Ethics have been approved by KCL ethics Committee REMAS ID 18210, review reference LRS-19/20-18210