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.

Aspirin and NSAID use and the risk of COVID-19 (preprint)

Early reports raised concern that use of non-steroidal anti-inflammatory drugs (NSAIDs) may increase risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19). Users of the COVID Symptom Study smartphone application reported use of aspirin and other NSAIDs between March 24 and May 8, 2020. Users were queried daily about symptoms, COVID-19 testing, and healthcare seeking behavior. Cox proportional hazards regression was used to determine the risk of COVID-19 among according to aspirin or non-aspirin NSAID users. Among 2,736,091 individuals in the U.S., U.K., and Sweden, we documented 8,966 incident reports of a positive COVID-19 test over 60,817,043 person-days of follow-up. Compared to non-users and after stratifying by age, sex, country, day of study entry, and race/ethnicity, non-aspirin NSAID use was associated with a modest risk for testing COVID-19 positive (HR 1.23 [1.09, 1.32]), but no significant association was observed among aspirin users (HR 1.13 [0.92, 1.38]). After adjustment for lifestyle factors, comorbidities and baseline symptoms, any NSAID use was not associated with risk (HR 1.02 [0.94, 1.10]). Results were similar for those seeking healthcare for COVID-19 and were not substantially different according to lifestyle and sociodemographic factors or after accounting for propensity to receive testing. Our results do not support an association of NSAID use, including aspirin, with COVID-19 infection. Previous reports of a potential association may be due to higher rates of comorbidities or use of NSAIDs to treat symptoms associated with COVID-19. One Sentence Summary NSAID use is not associated with COVID-19 risk.

Racial and ethnic differences in COVID-19 vaccine hesitancy and uptake (preprint)

Background Racial and ethnic minorities have been disproportionately impacted by COVID-19. In the initial phase of population-based vaccination in the United States (U.S.) and United Kingdom (U.K.), vaccine hesitancy and limited access may result in disparities in uptake. Methods We performed a cohort study among U.S. and U.K. participants in the smartphone-based COVID Symptom Study (March 24, 2020-February 16, 2021). We used logistic regression to estimate odds ratios (ORs) of COVID-19 vaccine hesitancy (unsure/not willing) and receipt. Results In the U.S. ( n =87,388), compared to White non-Hispanic participants, the multivariable ORs of vaccine hesitancy were 3.15 (95% CI: 2.86 to 3.47) for Black participants, 1.42 (1.28 to 1.58) for Hispanic participants, 1.34 (1.18 to 1.52) for Asian participants, and 2.02 (1.70 to 2.39) for participants reporting more than one race/other. In the U.K. ( n =1,254,294), racial and ethnic minorities had similarly elevated hesitancy: compared to White participants, their corresponding ORs were 2.84 (95% CI: 2.69 to 2.99) for Black participants, 1.66 (1.57 to 1.76) for South Asian participants, 1.84 (1.70 to 1.98) for Middle East/East Asian participants, and 1.48 (1.39 to 1.57) for participants reporting more than one race/other. Among U.S. participants, the OR of vaccine receipt was 0.71 (0.64 to 0.79) for Black participants, a disparity that persisted among individuals who specifically endorsed a willingness to obtain a vaccine. In contrast, disparities in uptake were not observed in the U.K. Conclusions COVID-19 vaccine hesitancy was greater among racial and ethnic minorities, and Black participants living in the U.S. were less likely to receive a vaccine than White participants. Lower uptake among Black participants in the U.S. during the initial vaccine rollout is attributable to both hesitancy and disparities in access.

Identifying optimal combinations of symptoms to trigger diagnostic work-up of suspected COVID-19 cases in vaccine trials: analysis from a community-based, prospective, observational cohort (preprint)

Background: Several COVID-19 vaccine efficacy trials are ongoing with others predicted to start soon. Diagnostic work-up of trial participants following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify an efficient symptom combination to capture most cases using the lowest possible number of tests. Methods: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms between March-September 2020 and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one RT-PCR positive case were calculated for individual symptoms and symptom combinations. A multi-objective evolutionary algorithm was applied to generate symptom combinations with good trade-offs between sensitivity and specificity. Findings: The UK dataset included 122,305 individuals (1,202 RT-PCR positive). Findings were replicated in a US dataset including 3,162 individuals (79 RT-PCR positive). Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 RT-PCR tests per positive case. The symptom combination with the highest sensitivity was fatigue, anosmia, cough, diarrhoea, headache, and sore throat, identifying 96% of cases and requiring 96 tests. Interpretation: We confirm the significance of COVID-19 specific symptoms widely recommended for triggering RT-PCR. By using the data-driven optimization technique we identified additional symptoms (fatigue, sore throat, headache, diarrhoea) that enabled many more positive cases to be captured efficiently. By providing a set of solutions with optimal trade-offs between sensitivity and specificity, we produced a selection of symptom subsets that maximise the capture of cases given different laboratory capacities. The methodology may be of particular use for COVID-19 vaccine developers across a range of resource settings and have more far-reaching public health implications for detection of symptomatic SARS CoV2 infection.

Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets (preprint)

The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and 189 million unique assessments have been logged since its introduction in March 2020. The success of the Covid Symptom Study creates technical challenges around effective data curation for two reasons. Firstly, the scale of the dataset means that it can no longer be easily processed using standard software on commodity hardware. Secondly, the size of the research group means that replicability and consistency of key analytics used across multiple publications becomes an issue. We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset.

Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application (preprint)

Background As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods We performed modelling on longitudinal, self-reported data from users of the COVID Symptom Study app in England between 24 March and 29 September, 2020. Combining a symptom-based predictive model for COVID-19 positivity and RT-PCR tests provided by the Department of Health we were able to estimate disease incidence, prevalence and effective reproduction number. Geographically granular estimates were used to highlight regions with rapidly increasing case numbers, or hotspots. Findings More than 2.6 million app users in England provided 115 million daily reports of their symptoms, and recorded the results of 170,000 PCR tests. On a national level our estimates of incidence and prevalence showed similar sensitivity to changes as two national community surveys: the ONS and REACT studies. On a geographically granular level, our estimates were able to highlight regions before they were subject to local government lockdowns. Between 12 May and 29 September we were able to flag between 35-80% of regions appearing in the Government's hotspot list. Interpretation Self-reported data from mobile applications can provide a cost-effective and agile resource to inform a fast-moving pandemic, serving as an independent and complementary resource to more traditional instruments for disease surveillance.

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.

SARS-CoV-2 (COVID-19) infection in pregnant women: characterization of symptoms and syndromes predictive of disease and severity through real-time, remote participatory epidemiology. (preprint)

Background: From the beginning of COVID-19 pandemic, pregnant women have been considered at greater risk of severe morbidity and mortality. However, data on hospitalized pregnant women show that the symptom profile and risk factors for severe disease are similar to those among women who are not pregnant, although preterm birth, Cesarean delivery, and stillbirth may be more frequent and vertical transmission is possible. Limited data are available for the cohort of pregnant women that gave rise to these hospitalized cases, hindering our ability to quantify risk of COVID-19 sequelae for pregnant women in the community. Objective: To test the hypothesis that pregnant women in community differ in their COVID-19 symptoms profile and disease severity compared to non-pregnant women. This was assessed in two community-based cohorts of women aged 18-44 years in the United Kingdom, Sweden and the United States of America. Study design: This observational study used prospectively collected longitudinal (smartphone application interface) and cross-sectional (web-based survey) data. Participants in the discovery cohort were drawn from 400,750 UK, Sweden and US women (79 pregnant who tested positive) who self-reported symptoms and events longitudinally via their smartphone, and a replication cohort drawn from 1,344,966 USA women (162 pregnant who tested positive) cross-sectional self-reports samples from the social media active user base. The study compared frequencies of symptoms and events, including self-reported SARS-CoV-2 testing and differences between pregnant and non-pregnant women who were hospitalized and those who recovered in the community. Multivariable regression was used to investigate disease severity and comorbidity effects. Results: Pregnant and non-pregnant women positive for SARS-CoV-2 infection drawn from these community cohorts were not different with respect to COVID-19-related severity. Pregnant women were more likely to have received SARS-CoV-2 testing than non-pregnant, despite reporting fewer clinical symptoms. Pre-existing lung disease was most closely associated with the severity of symptoms in pregnant hospitalized women. Heart and kidney diseases and diabetes were additional factors of increased risk. The most frequent symptoms among all non-hospitalized women were anosmia [63% in pregnant, 92% in non-pregnant] and headache [72%, 62%]. Cardiopulmonary symptoms, including persistent cough [80%] and chest pain [73%], were more frequent among pregnant women who were hospitalized. Gastrointestinal symptoms, including nausea and vomiting, were different among pregnant and non-pregnant women who developed severe outcomes. Conclusions: Although pregnancy is widely considered a risk factor for SARS-CoV-2 infection and outcomes, and was associated with higher propensity for testing, the profile of symptom characteristics and severity in our community-based cohorts were comparable to those observed among non-pregnant women, except for the gastrointestinal symptoms. Consistent with observations in non-pregnant populations, comorbidities such as lung disease and diabetes were associated with an increased risk of more severe SARS-CoV-2 infection during pregnancy. Pregnant women with pre-existing conditions require careful monitoring for the evolution of their symptoms during SARS-CoV-2 infection.

The effects of personal protective equipment on airway management: An in-situ simulation

The novel coronavirus disease (COVID-19) was declared a pandemic by the World Health Organisation on 11th March and has led to over 41,000 deaths in the UK. Public Health England guidance for aerosol generating procedures (AGP) requires the donning of personal protective equipment (PPE). We evaluated airway management skills using an in-situ emergency simulation. The scenarios were video recorded and scored by two independent assessors using a skill specific checklist. A total of 34 airway management procedures were evaluated. The checklist involved 13 steps with a maximum score of 26. The median (IQR [range]) checklist score was 25 (24-25 [20-26]). Four teams failed to intubate the trachea and proceeded to manage the airway using a supraglottic airway device. The mean (SD) intubation time was 47.9 (16.5) seconds and two anaesthetists (7%) required a second attempt. Our results show that airway management can be carried out successfully whilst donned in PPE. However, additional training in using newly introduced devices such as a McGrath® video laryngoscope is of paramount importance.

Racial and ethnic determinants of Covid-19 risk (preprint)

BackgroundRacial and ethnic minorities have disproportionately high hospitalization rates and mortality related to the novel coronavirus disease 2019 (Covid-19). There are comparatively scant data on race and ethnicity as determinants of infection risk. MethodsWe used a smartphone application (beginning March 24, 2020 in the United Kingdom [U.K.] and March 29, 2020 in the United States [U.S.]) to recruit 2,414,601 participants who reported their race/ethnicity through May 25, 2020 and employed logistic regression to determine the adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for a positive Covid-19 test among racial and ethnic groups. ResultsWe documented 8,858 self-reported cases of Covid-19 among 2,259,841 non-Hispanic white; 79 among 9,615 Hispanic; 186 among 18,176 Black; 598 among 63,316 Asian; and 347 among 63,653 other racial minority participants. Compared with non-Hispanic white participants, the risk for a positive Covid-19 test was increased across racial minorities (aORs ranging from 1.24 to 3.51). After adjustment for socioeconomic indices and Covid-19 exposure risk factors, the associations (aOR [95% CI]) were attenuated but remained significant for Hispanic (1.58 [1.24-2.02]) and Black participants (2.56 [1.93-3.39]) in the U.S. and South Asian (1.52 [1.38-1.67]) and Middle Eastern participants (1.56 [1.25-1.95]) in the U.K. A higher risk of Covid-19 and seeking or receiving treatment was also observed for several racial/ethnic minority subgroups. ConclusionsOur results demonstrate an increase in Covid-19 risk among racial and ethnic minorities not completely explained by other risk factors for Covid-19, comorbidities, and sociodemographic characteristics. Further research investigating these disparities are needed to inform public health measures.

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.

Current tobacco smoking and risk from COVID-19: results from a population symptom app in over 2.4 million people (preprint)

BackgroundThe association between current tobacco smoking, the risk of developing COVID-19 and the severity of illness is an important information gap. MethodsUK users of the COVID Symptom Study app provided baseline data including demographics, anthropometrics, smoking status and medical conditions, were asked to log symptoms daily from 24th March 2020 to 23rd April 2020. Participants reporting that they did not feel physically normal were taken through a series of questions, including 14 potential COVID-19 symptoms and any hospital attendance. The main study outcome was the association between current smoking and the development of "classic" symptoms of COVID-19 during the pandemic defined as fever, new persistent cough and breathlessness. The number of concurrent COVID-19 symptoms was used as a proxy for severity. In addition, association of subcutaneous adipose tissue expression of ACE2, both the receptor for SARS-CoV-2 and a potential mediator of disease severity, with smoking status was assessed in a subset of 541 twins from the TwinsUK cohort. ResultsData were available on 2,401,982 participants, mean(SD) age 43.6(15.1) years, 63.3% female, overall smoking prevalence 11.0%. 834,437 (35%) participants reported being unwell and entered one or more symptoms. Current smokers were more likely to develop symptoms suggesting a diagnosis of COVID-19; classic symptoms adjusted OR[95%CI] 1.14[1.10 to 1.18]; >5 symptoms 1.29[1.26 to 1.31]; >10 symptoms 1.50[1.42 to 1.58]. Smoking was associated with reduced ACE2 expression in adipose tissue (Beta(SE)=-0.395(0.149); p=7.01x10-3). InterpretationThese data are consistent with smokers having an increased risk from COVID-19. FundingZoe provided in kind support for all aspects of building, running and supporting the app and service to all users worldwide. The study was also supported by grants from the Wellcome Trust, UK Research and Innovation and British Heart Foundation. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSThe interaction between current smoking and COVID-19 is unclear. Smoking is known to increase susceptibility to viral infections and appears to be associated with worse outcomes in people admitted to hospital with COVID-19. However, case series have reported relatively low levels of current smoking among individuals admitted to hospital with the condition, raising the possibility that smoking has a protective effect against the disease. Added value of this studyData from a large UK population who are users of a symptom reporting app during the pandemic supports the hypothesis that smokers are more likely to develop symptoms consistent with COVID-19 and that they have an increased symptom burden. Implications of all the available evidenceThese population data, combined with evidence of a worse outcome in smokers hospitalised with the condition, support the contention that smoking increases individual risk from COVID-19. Support to help people to quit smoking should therefore form part of efforts to deal with the pandemic.

Risk of symptomatic Covid-19 among frontline healthcare workers (preprint)

BackgroundData for frontline healthcare workers (HCWs) and risk of SARS-CoV-2 infection are limited and whether personal protective equipment (PPE) mitigates this risk is unknown. We evaluated risk for COVID-19 among frontline HCWs compared to the general community and the influence of PPE. MethodsWe performed a prospective cohort study of the general community, including frontline HCWs, who reported information through the COVID Symptom Study smartphone application beginning on March 24 (United Kingdom, U.K.) and March 29 (United States, U.S.) through April 23, 2020. We used Cox proportional hazards modeling to estimate multivariate-adjusted hazard ratios (aHRs) of a positive COVID-19 test. FindingsAmong 2,035,395 community individuals and 99,795 frontline HCWs, we documented 5,545 incident reports of a positive COVID-19 test over 34,435,272 person-days. Compared with the general community, frontline HCWs had an aHR of 11{middle dot}6 (95% CI: 10{middle dot}9 to 12{middle dot}3) for reporting a positive test. The corresponding aHR was 3{middle dot}40 (95% CI: 3{middle dot}37 to 3{middle dot}43) using an inverse probability weighted Cox model adjusting for the likelihood of receiving a test. A symptom-based classifier of predicted COVID-19 yielded similar risk estimates. Compared with HCWs reporting adequate PPE, the aHRs for reporting a positive test were 1{middle dot}46 (95% CI: 1{middle dot}21 to 1{middle dot}76) for those reporting PPE reuse and 1{middle dot}31 (95% CI: 1{middle dot}10 to 1{middle dot}56) for reporting inadequate PPE. Compared with HCWs reporting adequate PPE who did not care for COVID-19 patients, HCWs caring for patients with documented COVID-19 had aHRs for a positive test of 4{middle dot}83 (95% CI: 3{middle dot}99 to 5{middle dot}85) if they had adequate PPE, 5{middle dot}06 (95% CI: 3{middle dot}90 to 6{middle dot}57) for reused PPE, and 5{middle dot}91 (95% CI: 4{middle dot}53 to 7{middle dot}71) for inadequate PPE. InterpretationFrontline HCWs had a significantly increased risk of COVID-19 infection, highest among HCWs who reused PPE or had inadequate access to PPE. However, adequate supplies of PPE did not completely mitigate high-risk exposures. FundingZoe Global Ltd., Wellcome Trust, EPSRC, NIHR, UK Research and Innovation, Alzheimers Society, NIH, NIOSH, Massachusetts Consortium on Pathogen Readiness RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSThe prolonged course of the coronavirus disease 2019 (COVID-19) pandemic, coupled with sustained challenges supplying adequate personal protective equipment (PPE) for frontline healthcare workers (HCW), have strained global healthcare systems in an unprecedented fashion. Despite growing awareness of this problem, there are few data to inform policy makers on the risk of COVID-19 among HCWs and the impact of PPE on their disease burden. Prior reports of HCW infections are based on cross sectional data with limited individual-level information on risk factors for infection. A PubMed search for articles published between January 1, 2020 and May 5, 2020 using the terms "covid-19", "healthcare workers", and "personal protective equipment," yielded no population-scale investigations exploring this topic. Added value of this studyIn a prospective study of 2,135,190 individuals, frontline HCWs may have up to a 12-fold increased risk of reporting a positive COVID-19 test. Compared with those who reported adequate availability of PPE, frontline HCWs with inadequate PPE had a 31% increase in risk. However, adequate availability of PPE did not completely reduce risk among HCWs caring for COVID-19 patients. Implications of all the available evidenceBeyond ensuring adequate availability of PPE, additional efforts to protect HCWs from COVID-19 are needed, particularly as lockdown is lifted in many regions of the world.

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

Geo-social gradients in predicted COVID-19 prevalence and severity in Great Britain: results from 2,266,235 users of the COVID-19 Symptoms Tracker app (preprint)

Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 2,266,235 unique GB users of the COVID Symptom Tracker app, we find that COVID-19 prevalence and severity became rapidly distributed across the UK within a month of the WHO declaration of the pandemic, with significant evidence of "urban hot-spots". We found a geo-social gradient associated with disease severity and prevalence suggesting resources should focus on urban areas and areas of higher deprivation. Our results demonstrate use of self-reported data to inform public health policy and resource allocation.

Self-reported symptoms of covid-19 including symptoms most predictive of SARS-CoV-2 infection, are heritable (preprint)

Susceptibility to infection such as SARS-CoV-2 may be influenced by host genotype. TwinsUK volunteers (n=2633) completing the C-19 Covid symptom tracker app allowed classical twin studies of covid-19 symptoms including predicted covid-19, a symptom-based algorithm predicting true infection derived in app users tested for SARS-CoV-2. We found heritability for fever = 41 (95% confidence intervals 12-70)%; anosmia 47 (27-67)%; delirium 49 (24-75)%; and predicted covid-19 gave heritability = 50 (29-70)%.