COVID-19 Gateway Resources

Total COVID-19 Datasets: 9
Publisher Dataset Title Abstract
OTHER > COG-UK COG-UK Viral Genome Sequences COG-UK Consortium has published a dataset which contains over 20K SARS-CoV-2 viral genome sequences available as open access.
ALLIANCE > SAIL COVID-19 Shielded People list List of people notified of vulnerable status and instructed to self-isolate during Covid pandemic.
ALLIANCE > SAIL COVID-19 Symptom Tracker Dataset The COVID Symptom Tracker was designed by doctors and scientists at King's College London (KCL), Guys and St Thomas’ Hospital working in partnership with ZOE Global. Led by Dr Tim Spector, professor of genetic epidemiology at KCL and director of TwinsUK.
ALLIANCE > SAIL COVID-19 Test results Test results from Laboratory Information Management System for COVID19 tests (coronavirus SARS CoV2 PCR and coronavirus PCR tests).
HUBS > DISCOVER NOW North West London COVID-19 Patient Level Situation Report (NWL COVID19 PLD SITREP) The NWL COVID19 PLD SITREP linked table is a direct daily feed from NWL providers. The table provides the patient level data related to COVID admissions in hospital since the outbreak of the pandemic, includes bed status/ventilation status etc.
HUBS > DISCOVER NOW North West London Pathology (NWL PATH) The NWL Pathology linked table is a direct feed from The Doctors Laboratory and North West London Pathology for patients registered within NWL. Some of the data items included are test dates and times, test codes and names and test results.
HUBS > DISCOVER NOW North West London Vulnerable Patient List (NWL VPL) The NWL Vunerable Patient List linked table is for patients previously defined as vulnerable and now called shielded patient list by NHS England. These are patients who are registered with a North West London General Practice.
HUBS > DISCOVER NOW North West London population data (NWL POP) The NWL POP table holds the NWL registered patients and key demographic information about them i.e. age, gender, ethinicity etc.
ALLIANCE > NHSX The National COVID-19 Chest Imaging Database The National COVID-19 Chest Imaging Database is a joint collaboration between NHSX, BSTI and Royal Surrey NHS Foundation Trust to create a centralised UK database of X-Ray, CT and MRI images from hospital patients to inform the COVID-19 response.
Total COVID-19 Papers/Preprints: 105
Author Title Journal Title Year Published
Karla A Lee; Weinjie Ma; Daniel R Sikavi; Jonathon Wolf; Claire J Steves; Tim D Spector; Andrew T Chan Cancer and risk of COVID-19 through a general community survey Background: Data are limited on the risk of coronavirus disease 2019 (COVID-19) among individuals with cancer and whether cancer-related therapy exacerbates this risk. Methods: We evaluated the risk for coronavirus disease 2019 (COVID-19) among patients living with cancer compared to the general community and whether cancer-related treatments influence this risk. Data were collected from the COVID Symptom Study smartphone application since March 24, 2020 (United Kingdom), March 29 (U.S.), and April 29, 2020 (Sweden) through May 8, 2020. We used multivariate-adjusted odds ratios (aORs) of a positive COVID-19 test as well as predicted COVID-19 infection using a validated symptom model. Results: Among 23,266 participants with cancer and 1,784,293 without cancer, we documented 10,404 reports of a positive COVID-19 test. Compared to participants without cancer, those living with cancer had 62% increased risk of a positive COVID-19 test (95% CI: 1.37-1.91). Among patients with cancer, current treatment with chemotherapy/immunotherapy was associated with a nearly 2.5-fold increased risk of a positive test (aOR: 2.42; 95% CI: 1.81-3.25). The association between cancer and COVID-19+ was stronger among participants >65 years (aOR: 1.93; 95%CI: 1.51-2.46) compared to younger participants (aOR: 1.32; 95%CI: 1.06-1.64; Pinteraction<0.001); and among males (aOR: 1.71; 95%CI: 1.36-2.15) compared to females (aOR: 1.43; 95%CI: 1.14-1.79; Pinteraction=0.02). Conclusions: Individuals with cancer had a significantly increased risk of infection compared to the general community. Those treated with chemotherapy or immunotherapy were particularly at-risk of infection. Trial Registration: ClinicalTrials.gov NCT04331509 2020-05-26
Neil SN Graham; Cornelia Junghans; Rawlda Downes; Catherine Sendall; Helen Lai; Annie McKirdy; Paul Elliott; Robert Howard; David Wingfield; Miles Priestman; Marta Ciechonska; Loren Cameron; Marko Storch; Michael Crone; Paul Freemont; Paul Randell; Robert McLaren; Nicola Lang; Shamez Ladhani; Frances Sanderson; David J Sharp SARS-CoV-2 infection, clinical features and outcome of COVID-19 in United Kingdom nursing homes Objectives: To understand SARS-Co-V-2 infection and transmission in UK nursing homes in order to develop preventive strategies for protecting the frail elderly residents. Design: An outbreak investigation. Setting: 4 nursing homes affected by COVID-19 outbreaks in central London. Participants: 394 residents and 70 staff in nursing homes. Interventions: Two point-prevalence surveys one week apart where residents underwent SARS-CoV-2 testing and had relevant symptoms documented. Asymptomatic staff from three of the four homes were also offered SARS-CoV-2 testing. Main outcome measures: All-cause mortality, and mortality attributed to COVID-19 on death certificates. Prevalence of SARS-CoV-2 infection and symptoms in residents and staff. Results: Overall, 26% (95% confidence interval 22 to 31) of residents died over the two-month period. All-cause mortality increased by 203% (95% CI 70 to 336). Systematic testing identified 40% (95% CI 35 to 46) of residents, of whom 43% (95% CI 34 to 52) were asymptomatic and 18% (95% CI 11 to 24) had atypical symptoms, as well as 4% (95% CI -1 to 9) of asymptomatic staff who tested positive for SARS-CoV-2. Conclusions: The SARS-CoV-2 outbreak was associated with a very high mortality rate in residents of nursing homes. Systematic testing of all residents and a representative sample of staff identified high rates of SARS-CoV-2 positivity across the four nursing homes, highlighting a potential for regular screening to prevent future outbreaks. 2020-05-26
Xiao Jiang; James M. Eales; David Scannali; Alicja Nazgiewicz; Priscilla Prestes; Michelle Maier; Matthew J. Denniff; Xiaoguang Xu; Sushant Saluja; Eddie Cano-Gamez; Wojciech Wystrychowski; Monika Szulinska; Andrzej Antczak; Sean Byars; Maciej Glyda; Robert Krol; Joanna Zywiec; Ewa Zukowska-Szczechowska; Louise M. Burrell; Adrian S. Woolf; Adam Greenstein; Pawel Bogdanski; Bernard Keavney; Andrew P. Morris; Anthony Heagerty; Bryan Williams; Stephen B. Harrap; Gosia Trynka; Nilesh J. Samani; Tomasz J. Guzik; Fadi J. Charchar; Maciej Tomaszewski Hypertension and renin-angiotensin system blockers are not associated with expression of Angiotensin Converting Enzyme 2 (ACE2) in the kidney Angiotensin converting enzyme 2 (ACE2) is the cellular entry point for severe acute respiratory syndrome coronavirus (SARS-CoV-2) - the cause of COVID-19 disease. It has been hypothesized that use of renin-angiotensin system (RAS) inhibiting medications in patients with hypertension, increases the expression of ACE2 and thereby increases the risk of COVID-19 infection and severe outcomes or death. However, the effect of RAS-inhibition on ACE2 expression in human tissues of key relevance to blood pressure regulation and COVID-19 infection has not previously been reported. We examined how hypertension, its major metabolic co-phenotypes and antihypertensive medications relate to ACE2 renal expression using information from up to 436 patients whose kidney transcriptomes were characterised by RNA-sequencing. We further validated some of the key observations in other human tissues and/or a controlled experimental model. Our data reveal increasing expression of ACE2 with age in both human lungs and the kidney. We show no association between renal expression of ACE2 and either hypertension or common types of RAS inhibiting drugs. We demonstrate that renal abundance of ACE2 is positively associated with a biochemical index of kidney function and show a strong enrichment for genes responsible for kidney health and disease in ACE2 co-expression analysis. Collectively, our data indicate that neither hypertension nor antihypertensive treatment are likely to alter individual risk of SARS-CoV-2 infection or influence clinical outcomes in COVID-19 through changes of ACE2 expression. Our data further suggest that in the absence of SARS-CoV-2 infection, kidney ACE2 is most likely nephro-protective but the age-related increase in its expression within lungs and kidneys may be relevant to the risk of SARS-CoV-2 infection. 2020-05-26
Priscilla Mathewson; Ben Gordon; Kay Snowley; Clara Fennessy; Alastair Denniston; Neil Sebire Study Data Element Mapping: Feasibility of Defining Common Data Elements Across COVID-19 Studies Background: Numerous clinical studies are now underway investigating aspects of COVID-19. The aim of this study was to identify a selection of national and/or multicentre clinical COVID-19 studies in the United Kingdom to examine the feasibility and outcomes of documenting the most frequent data elements common across studies to rapidly inform future study design and demonstrate proof-of-concept for further subject-specific study data element mapping to improve research data management. Methods: 25 COVID-19 studies were included. For each, information regarding the specific data elements being collected was recorded. Data elements collated were arbitrarily divided into categories for ease of visualisation. Elements which were most frequently and consistently recorded across studies are presented in relation to their relative commonality. Results: Across the 25 studies, 261 data elements were recorded in total. The most frequently recorded 100 data elements were identified across all studies and are presented with relative frequencies. Categories with the largest numbers of common elements included demographics, admission criteria, medical history and investigations. Mortality and need for specific respiratory support were the most common outcome measures, but with specific studies including a range of other outcome measures. Conclusion: The findings of this study have demonstrated that it is feasible to collate specific data elements recorded across a range of studies investigating a specific clinical condition in order to identify those elements which are most common among studies. These data may be of value for those establishing new studies and to allow researchers to rapidly identify studies collecting data of potential use hence minimising duplication and increasing data re-use and interoperability 2020-05-26
Vasilis Kontis; James E Bennett; Robbie M Parks; Theo Rashid; Jonathan Pearson-Stuttard; Perviz Asaria; Michel Guillot; Marta Blangiardo; Majid Ezzati Age- and sex-specific total mortality impacts of the early weeks of the Covid-19 pandemic in England and Wales: Application of a Bayesian model ensemble to mortality statistics Background: The Covid-19 pandemic affects mortality directly through infection as well as through changes in the social, environmental and healthcare determinants of health. The impacts on mortality are likely to vary, in both magnitude and timing, by age and sex. Our aim was to estimate the total mortality impacts of the pandemic, by sex, age group and week. Methods: We developed an ensemble of 16 Bayesian models that probabilistically estimate the weekly number of deaths that would be expected had the Covid-19 pandemic not occurred. The models account for seasonality of death rates, medium-long-term trends in death rates, the impact of temperature on death rates, association of death rates in each week on those in preceding week(s), and the impact of bank holidays. We used data from January 2010 through mid-February 2020 (i.e., week starting 15th February 2020) to estimate the parameters of each model, which was then used to predict the number of deaths for subsequent weeks as estimates of death rates if the pandemic had not occurred. We subtracted these estimates from the actual reported number of deaths to measure the total mortality impact of the pandemic. Results: In the week that began on 21st March, the same week that a national lockdown was put in place, there was a >92% probability that there were more deaths in men and women aged [&ge;]45 years than would occur in the absence of the pandemic; the probability was 100% from the subsequent week. Taken over the entire period from mid-February to 8th May 2020, there were an estimated [~] 49,200 (44,700-53,300) or 43% (37-48) more deaths than would be expected had the pandemic not taken place. 22,900 (19,300-26,100) of these deaths were in females (40% (32-48) higher than if there had not been a pandemic), and 26,300 (23,800-28,700) in males (46% (40-52) higher). The largest number of excess deaths occurred among women aged >85 years (12,400; 9,300-15,300), followed by men aged >85 years (9,600; 7,800-11,300) and 75-84 years (9,000; 7,500-10,300). The cause of death assigned to the majority (37,295) of these excess deaths was Covid-19. There was nonetheless a >99.99% probability that there has been an increase in deaths assigned to other causes in those aged [&ge;]45 years. However, by the 8th of May, the all-cause excess mortality had become virtually equal to deaths assigned to Covid-19, and non-Covid excess deaths had diminished to close to zero, or possibly become negative, in all age-sex groups. Interpretation: The death toll of Covid-19 pandemic, in middle and older ages, is substantially larger than the number of deaths reported as a result of confirmed infection, and was visible in vital statistics when the national lockdown was put in place. When all-cause mortality is considered, the mortality impact of the pandemic on men and women is more similar than when comparing deaths assigned to Covid-19 as underlying cause of death. 2020-05-25
Richard Issitt; John Booth; William Bryant; Anastasia Spiridou; Andrew Taylor; Pascale DuPre; Padmanabhan Ramnarayan; John Hartley; Mario Cortino Borja; Karyn Moshal; Helen Dunn; Harry Hemingway; Neil Sebire Coronavirus (COVID-19) infection in children at a specialist centre: outcome and implications of underlying high-risk comorbidities in a paediatric population Background: There is evolving evidence of significant differences in severity and outcomes of coronavirus disease 2019 (COVID-19) in children compared to adults. Underlying medical conditions associated with increased risk of severe disease are based on adult data, but have been applied across all ages resulting in large numbers of families undertaking social shielding (vulnerable group). We conducted a retrospective analysis of children with suspected COVID-19 at a Specialist Childrens Hospital to determine outcomes based on COVID-19 testing status and underlying health vulnerabilities. Methods: Routine clinical data were extracted retrospectively from the Institutions Electronic Health Record system and Digital Research Environment for patients with suspected and confirmed COVID-19 diagnoses. Data were compared between Sars-CoV-2 positive and negative patients (CoVPos / CoVNeg respectively), and in relation to presence of underlying health vulnerabilities based on Public Health England guidance. Findings: Between 1st March and 15th May 2020, 166 children (<18 years of age) presented to a specialist childrens hospital with clinical features of possible COVID-19 infection. 65 patients (39.2%) tested positive for SARS-CoV-2 virus. CoVPos patients were older (median 9 [0.9-14] years vs median 1 [0.1-5.7.5] years respectively, p<0.001). There was a significantly reduced proportion of vulnerable cases (47.7% vs 72.3%, p=0.002), but no difference in proportion of vulnerable patients requiring ventilation (61% vs 64.3%, p = 0.84) between CoVPos and CoVNeg groups. However, a significantly lower proportion of CoVPos patients required mechanical ventilation support compared to CoVNeg patients (27.7 vs 57.4%, p<0.001). Mortality was not significantly different between CoVPos and CoVNeg groups (1.5 vs 4% respectively, p=0.67) although there were no direct COVID-19 related deaths in this highly preselected paediatric population. Interpretation: COVID-19 infection may be associated with severe disease in childhood presenting to a specialist hospital, but does not appear significantly different in severity to other causes of similar clinical presentations. In children presenting with pre-existing COVID-19 vulnerable medical conditions at a specialist centre, there does not appear to be significantly increased risk of either contracting COVID-19 or severe complications, apart from those undergoing chemotherapy, who are over-represented. 2020-05-25
Russell M Viner; Oliver T Mytton; Chris Bonell; G.J. Melendez-Torres; Joseph L Ward; Lee Hudson; Claire Waddington; James Thomas; Simon Russell; Fiona van der Klis; Jasmina Panovska-Griffiths; Nicholas G Davies; Robert Booy; Rosalind Eggo Susceptibility to and transmission of COVID-19 amongst children and adolescents compared with adults: a systematic review and meta-analysis Background The degree to which children and young people are infected by and transmit the SARS-CoV-2 virus is unclear. Clinical series and testing cohorts based upon screening of symptomatic cases provide biased estimates of susceptibility in children. The role of children and young people in transmission of SARS-CoV-2 is dependent on susceptibility, symptoms, viral load, social contact patterns and behaviour. Methods We undertook a rapid systematic review of contact-tracing studies and population-screening studies to address the question What is the susceptibility to and transmission of SARS-CoV-2 by children and adolescents compared with adults? We searched PubMed and medRxiv on 16 May 2020 and identified 6327 studies, with additional studies identified through handsearching of cited references (2) and professional contacts (4). We assessed quality, summarized findings and undertook a random effects meta-analysis of contact-tracing studies. Results 18 studies met inclusion criteria; 9 contact-tracing, 8 population-screening and 1 systematic-review. Meta-analysis of contact tracing studies showed that the pooled odds ratio of being an infected contact in children compared with adults for all contact tracing studies was 0.44 (0.29, 0.69) with substantial heterogeneity (63%). Findings from a systematic review of household clusters of COVID-19 found 3/31 (10%) were due to a child index case and a population-based school contact tracing study found minimal transmission by child or teacher index cases. Findings from population-screening studies were heterogenous and not suitable for meta-analysis. Large studies from Iceland, the Netherlands and Spain and an Italian municipal study showed markedly lower SARS-CoV-2 prevalence amongst children and young people, however studies from Stockholm, England and municipalities in Switzerland and Germany showed showed no difference in infection prevalence between adults and children. Conclusions There is preliminary evidence that children and young people have lower susceptibility to SARS-CoV-2, with a 56% lower odds of being an infected contact. There is weak evidence that children and young people play a lesser role in transmission of SARS-CoV-2 at a population level. Our study provides no information on the infectivity of children. 2020-05-24
Miriam Mutambudzi; Claire L Niedzwiedz; Ewan B Macdonald; Alastair H Leyland; Frances S Mair; Jana J Anderson; Carlos A Celis-Morales; John Cleland; John Forbes; Jason MR Gill; Claire Hastie; Frederick K Ho; Bhautesh D Jani; Daniel F Mackay; Barbara I Nicholl; Naveed I Sattar; Paul I Welsh; Jill P Pell; Srinivasa Vittal Katikireddi; Evangelia Demou Occupation and risk of COVID-19: prospective cohort study of 120,621 UK Biobank participants Objectives: To investigate COVID-19 risk by occupational group. Design: Prospective study of linked population-based and administrative data. Setting: UK Biobank data linked to SARS-CoV-2 test results from Public Health England from 16 March to 3 May 2020. Participants: 120,621 UK Biobank participants who were employed or self-employed at baseline (2006-2010) and were 65 years or younger in March 2020. Overall, 29% (n=37,890) were employed in essential occupational groups, which included healthcare workers, social and education workers, and other essential workers comprising of police and protective service, food, and transport workers. Poisson regression models, adjusted for baseline sociodemographic, work-related, health, and lifestyle-related risk factors were used to assess risk ratios (RRs) of testing positive in hospital by occupational group as reported at baseline relative to non-essential workers. Main outcome measures: Positive SARS-CoV-2 test within a hospital setting (i.e. as an inpatient or in an Emergency Department). Results: 817 participants were tested for SARS-CoV-2 and of these, 206 (0.2%) individuals had a positive test in a hospital setting. Relative to non-essential workers, healthcare workers (RR 7.59, 95% CI: 5.43 to 10.62) and social and education workers (RR 2.17, 95% CI: 1.37 to 3.46) had a higher risk of testing positive for SARS-CoV-2 in hospital. Using more detailed groupings, medical support staff (RR 8.57, 95% CI: 4.35 to 16.87) and social care workers (RR 2.99, 95% CI: 1.71 to 5.24) had highest risk within the healthcare worker and social and education worker categories, respectively. In general, adjustment for covariates did not substantially change the pattern of occupational differences in risk. Conclusions: Essential workers in health and social care have a higher risk of severe SARS-CoV-2 infection. These findings underscore the need for national and organisational policies and practices that protect and support workers with elevated risk of SARS-CoV-2 infection. 2020-05-23
Sarah Beale; Andrew Hayward; Laura Shallcross; Robert W Aldridge; Ellen Fragaszy A Rapid Review of the Asymptomatic Proportion of PCR-Confirmed SARS-CoV-2 Infections in Community Settings Background: Up to 80% of active SARS-CoV-2 infections are proposed to be asymptomatic based on cross-sectional studies. However, accurate estimates of the asymptomatic proportion require systematic detection and follow-up to differentiate between truly asymptomatic and pre-symptomatic cases. We conducted a rapid review and meta-analysis of current evidence regarding the asymptomatic proportion of PCR-confirmed SARS-CoV-2 infections based on methodologically-appropriate studies in community settings. Methods: We searched Medline and EMBASE for peer-reviewed articles, and BioRxiv and MedRxiv for pre-prints published prior to 05/05/2020. We included studies based in community settings that involved systematic PCR testing on participants and follow-up symptom monitoring regardless of symptom status. We extracted data on study characteristics, frequencies of PCR-confirmed infections by symptom status, and (if available) cycle threshold values and/or duration of viral shedding by symptom status. We computed estimates of the asymptomatic proportion and 95% confidence intervals for each study and overall using random effect meta-analysis. Findings: We screened 270 studies and included 6. The pooled estimate for the asymptomatic proportion of SARS-CoV-2 infections was 11% (95% CI 4%-18%). Estimates of baseline viral load appeared to be similar for asymptomatic and symptomatic cases based on available data in three studies, though detailed reporting of cycle threshold values and natural history of viral shedding by symptom status was limited. Interpretation: The asymptomatic proportion of SARS-CoV-2 infections is relatively low when estimated from methodologically-appropriate studies. Further investigation into the degree and duration of infectiousness for asymptomatic infections is warranted. Funding: Medical Research Council 2020-05-23
Nicola L Boddington; Andre Charlett; Suzanne Elgohari; Jemma L Walker; Helen Mcdonald; Chloe Byers; Laura Coughlan; Tatiana Garcia Vilaplana; Rosie Whillock; Mary Sinnathamby; Nikolaos Panagiotopoulos; Louise Letley; Pauline MacDonald; Roberto Vivancos; Obaghe Edeghere; Joseph Shingleton; Emma Bennett; Daniel J Grint; Helen Strongman; Kathryn E Mansfield; Christopher Rentsch; Caroline Minassian; Ian J Douglas; Rohini Mathur; Maria Peppa; Simon Cottrell; Jim McMenamin; Maria Zambon; Mary Ramsay; Gavin Dabrera; Vanessa Saliba; Jamie Lopez Bernal COVID-19 in Great Britain: epidemiological and clinical characteristics of the first few hundred (FF100) cases: a descriptive case series and case control analysis Objectives: Following detection of the first virologically-confirmed cases of COVID-19 in Great Britain, an enhanced surveillance study was initiated by Public Health England to describe the clinical presentation, course of disease and identify risk factors for infection of the first few hundred cases. Methods: Information was collected on the first COVID-19 cases according to the First Few X WHO protocol. Case-control analyses of the sensitivity, specificity and predictive value of symptoms and risk factors for infection were conducted. Point prevalences of underlying health conditions among the UK general population were presented. Findings: The majority of FF100 cases were imported (51.4%), of which the majority had recent travel to Italy (71.4%). 24.7% were secondary cases acquired mainly through household contact (40.4%). Children had lower odds of COVID-19 infection compared with the general population. The clinical presentation of cases was dominated by cough, fever and fatigue. Non-linear relationships with age were observed for fever, and sensitivity and specificity of symptoms varied by age. Conditions associated with higher odds of COVID-19 infection (after adjusting for age and sex) were chronic heart disease, immunosuppression and multimorbidity. Conclusion: This study presents the first epidemiological and clinical summary of COVID-19 cases in Great Britain. The FFX study design enabled systematic data collection. The study was able to characterize the risk factors for infection with population prevalence estimates setting these relative risks into a public health context. It also provides important evidence for generating case definitions to support public health risk assessment, clinical triage and diagnostic algorithms. 2020-05-22
Nicholas S Hopkinson; Niccolo Rossi; Julia El-Sayed Moustafa; Anthony A Laverty; Jennifer K Quint; Maxim B Freydin; Alessia Visconti; Benjamin Murray; Marc Modat; Sebastien Ourselin; Kerrin Small; Richard Davies; Jonathan Wolf; Timothy Spector; Claire J Steves; Mario Falchi Current tobacco smoking and risk from COVID-19: results from a population symptom app in over 2.4 million people Background: The association between current tobacco smoking, the risk of developing COVID-19 and the severity of illness is an important information gap. Methods: UK 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. Results: Data 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). Interpretation: These data are consistent with smokers having an increased risk from COVID-19. 2020-05-21
Claudio Fronterre; Jonathan M Read; Barry Rowlingson; Jessica Bridgen; Simon Alderton; Peter J Diggle; Chris P Jewell COVID-19 in England: spatial patterns and regional outbreaks Aims: to investigate the spatiotemporal distribution of COVID-19 cases in England; to provide spatial quantification of risk at a high resolution; to provide information for prospective antigen and serological testing. Approach: We fit a spatiotemporal Negative Binomial generalised linear model to Public Health England SARS-CoV-2 testing data at the Lower Tier Local Authority region level. We assume an order-1 autoregressive model for case progression within regions, coupling discrete spatial units via observed commuting data and time-varying measures of traffic flow. We fit the model via maximum likelihood estimation in order to calculate region-specific risk of ongoing transmission, as well as measuring regional uncertainty in incidence. Results: We detect marked heterogeneity across England in COVID-19 incidence, not only in raw estimated incidence, but in the characteristics of within-region and between-region dynamics of PHE testing data. There is evidence for a spatially diverse set of regions having a higher daily increase of cases than others, having accounted for current case numbers, population size, and human mobility. Uncertainty in model estimates is generally greater in rural regions. Conclusions: A wide range of spatial heterogeneity in COVID-19 epidemic distribution and infection rate exists in England currently. Future work should incorporate fine-scaled demographic and health covariates, with continued improvement in spatially-detailed case reporting data. The method described here may be used to measure heterogeneity in real-time as behavioural and social interventions are relaxed, serving to identify "hotspots" of resurgent cases occurring in diverse areas of the country, and triggering locally-intensive surveillance and interventions as needed. Caveats: There is general concern over the ability of PHE testing data to capture the true prevalence of infection within the population, though this approach is designed to provide measures of spatial prevalence based on testing that can be used to guide further future testing effort. Now-casts of epidemic characteristics are presented based on testing data alone (as opposed to "true" prevalence in any one area). The model used in this analysis is phenomenological for ease and speed of principled parameter inference; we choose the model which best fits the current spatial case timeseries, without attempting to enforce "SIR"-type epidemic dynamics. 2020-05-20
Hamish Gibbs; Yang Liu; Carl AB Pearson; Christopher I Jarvis; Chris Grundy; Billy J Quilty; Charlie Diamond; Rosalind M Eggo Changing travel patterns in China during the early stages of the COVID-19 pandemic Understanding changes in human mobility in the early stages of the COVID-19 pandemic is crucial for assessing the impacts of travel restrictions designed to reduce disease spread. Here, relying on data from mainland China, we investigated the spatio-temporal characteristics of human mobility between 1st January and 1st March 2020 and discussed their public health implications. An outbound travel surge from Wuhan before travel restrictions were implemented was also observed across China due to the Lunar New Year, indicating that holiday travel may have played a larger role in mobility changes compared to impending travel restrictions. Holiday travel also shifted healthcare pressure related to COVID-19 towards locations with lower access to care. Network analyses showed no sign of major changes in the transportation network after Lunar New Year. Changes observed were temporary and have not yet led to structural reorganisation of the transportation network at the time of this study. 2020-05-19
Vanesa Bellou; Ioanna Tzoulaki; Evangelos Evangelou; Lazaros Belbasis Risk factors for adverse clinical outcomes in patients with COVID-19: A systematic review and meta-analysis Importance: COVID-19 is a clinically heterogeneous disease of varying severity and prognosis. Clinical characteristics that impact disease course could offer guidance for clinical decision making and future research endeavors and unveil disease pathways. Objective: To examine risk factors associated with adverse clinical outcomes in patients with COVID-19. Data sources: We performed a systematic review in PubMed from January 1 until April 19, 2020. Study selection: Observational studies that examined the association of any clinical characteristic with an adverse clinical outcome were considered eligible. We scrutinized studies for potential overlap. Data extraction and synthesis: Information on the effect of clinical factors on clinical endpoints of patients with COVID-19 was independently extracted by two researchers. When an effect size was not reported, crude odds ratios were calculated based on the available information from the eligible articles. Study-specific effect sizes from non-overlapping studies were synthesized applying the random-effects model. Main outcome and measure: The examined outcomes were severity and progression of disease, admission to ICU, need for mechanical ventilation, mortality, or a composite outcome. Results: We identified 88 eligible articles, and we performed a total of 256 meta-analyses on the association of 98 unique risk factors with five clinical outcomes. Seven meta-analyses presented the strongest epidemiological evidence in terms of statistical significance (P-value <0.005), between-study heterogeneity (I2 <50%), sample size (more than 1000 COVID-19 patients), 95% prediction interval excluded the null value, and absence of small-study effects. Elevated C-reactive protein (OR, 6.46; 95% CI, 4.85 - 8.60), decreased lymphocyte count (OR, 4.16; 95% CI, 3.17 - 5.45), cerebrovascular disease (OR, 2.84; 95% CI, 1.55 - 5.20), chronic obstructive pulmonary disease (OR, 4.44; 95% CI, 2.46 - 8.02), diabetes mellitus (OR, 2.04; 95% CI, 1.54 - 2.70), hemoptysis (OR, 7.03; 95% CI, 4.57 - 10.81), and male sex (OR, 1.51; 95% CI, 1.30 - 1.75) were associated with risk of severe COVID-19. Conclusions and relevance: Our results highlight factors that could be useful for prognostic model building, help guide patients' selection for randomized clinical trials, as well as provide alternative treatment targets by shedding light to disease pathophysiology. 2020-05-19
Nina Trivedy Rogers; Naomi Waterlow; Hannah E Brindle; Luisa Enria; Rosalind M Eggo; Shelley Lees; Chrissy h Roberts Behavioural change towards reduced intensity physical activity is disproportionately prevalent among adults with serious health issues or self-perception of high risk during the UK COVID-19 lockdown. Importance: There are growing concerns that the UK COVID-19 lockdown has reduced opportunities to maintain health through physical activity, placing individuals at higher risk of chronic disease and leaving them more vulnerable to severe sequelae of COVID-19. Objective: To examine whether the UK's lockdown measures have had disproportionate impacts on intensity of physical activity in groups who are, or who perceive themselves to be, at heightened risk from COVID-19. Designs, Setting, Participants: UK-wide survey of adults aged over 20, data collected between 2020-04-06 and 2020-04-22. Exposures: Self-reported doctor-diagnosed obesity, hypertension, type I/II diabetes, lung disease, cancer, stroke, heart disease. Self-reported disabilities and depression. Sex, gender, educational qualifications, household income, caring for school-age children. Narrative data on coping strategies. Main Outcomes and Measures: Change in physical activity intensity after implementation of UK COVID-19 lockdown (self-reported). Results: Most (60%) participants achieved the same level of intensity of physical activity during the lockdown as before the epidemic. Doing less intensive physical activity during the lockdown was associated with obesity (OR 1.21, 95% CI 1.02-1.41), hypertension (OR 1.52, 1.33-1.71), lung disease (OR 1.31,1.13-1.49), depression (OR 2.02, 1.82-2.22) and disability (OR 2.34, 1.99-2.69). Participants who reduced their physical activity intensity also had higher odds of being female, living alone or having no garden, and more commonly expressed sentiments about personal or household risks in narratives on coping. Conclusions and relevance: Groups who reduced physical activity intensity included disproportionate numbers of people with either heightened objective clinical risks or greater tendency to express subjective perceptions of risk. Policy on exercise for health during lockdowns should include strategies to facilitate health promoting levels of physical activity in vulnerable groups, including those with both objective and subjective risks. 2020-05-18
Niloofar Shoari; Majid Ezzati; Jill Baumgartner; Diego Malacarne; Daniela Fecht Accessibility and allocation of public parks and gardens during COVID-19 social distancing in England and Wales Visiting parks and gardens may attenuate the adverse physical and mental health impacts of social distancing implemented to reduce the spread of COVID-19. We quantified access to public parks and gardens in urban areas of England and Wales, and the potential for park crowdedness during periods of high use. We combined data from the Office for National Statistics and Ordnance Survey to quantified (i) the number of parks within 500 and 1,000 metres of urban postcodes (i.e., availability), (ii) the distance of postcodes to the nearest park (i.e., accessibility), and (iii) per-capita space in each park for people living within 1,000m. We examined variability by city and share of flats. Around 25.4 million people can access public parks or gardens within a ten-minute walk, while 3.8 million residents live farther away; of these 21% are children and 13% are elderly. Areas with a higher share of flats on average are closer to a park but people living in these areas are potentially less able to meet social distancing requirements while in parks during periods of high use. Cities in England and Wales can provide residents with access to green space that enables outdoor exercise and play during social distancing. Keeping public parks and gardens open, might require measures such as dedicated park times for different age groups or entry allocation systems that, combined with smartphone apps or drones, can monitor and manage the total number of people using the park. 2020-05-18
Elisa Gremese; Antonella Cingolani; Silvia Laura Bosello; Stefano Alivernini; Barbara Tolusso; Simone Perniola; Francesco Landi; Maurizio Pompili; Rita Murri; Angelo Santoliquido; Matteo Garcovich; Michela Sali; Gennaro De Pascale; Maurizio Gabrielli; Federico Biscetti; Massimo Montalto; Alberto Tosoni; Giovanni Gambassi; Gian Ludovico Rapaccini; Amerigo Iaconelli; Lorenzo Zileri Dal Verme; Luca Petricca; Anna Laura Fedele; Marco Maria Lizzio; Enrica Tamburrini; Gerlando Natalello; Laura Gigante; Dario Bruno; Lucrezia Verardi; Manuela Taddeo; Angelo Calabrese; Francesco Lombardi; Roberto Bernabei; Roberto Cauda; Francesco Franceschi; Raffaele Landolfi; Luca Richeldi; Maurizio Sanguinetti; Massimo Fantoni; Massimo Antonelli; Antonio Gasbarrini Sarilumab use in severe SARS-CoV-2 pneumonia Importance: Interleukin-6 signal blockade has shown preliminary beneficial effects in treating aberrant host inflammatory response against SARS-CoV-2 leading to severe respiratory distress. Objective: to describe the effect of off-label intravenous use of Sarilumab in patients with severe SARS-CoV-2-related pneumonia. Design: Observational clinical cohort study. Setting: Fondazione Policlinico Universitario A. Gemelli IRCCS as Italian Covid reference center. Participants: Patients with laboratory-confirmed SARS-CoV-2 infection and respiratory distress with PaO2/FiO2 ratio<300 treated with Sarilumab between March 23rd - April 4th, 2020. Date of final follow-up was April 18, 2020. Main outcomes and measures: We describe the clinical outcomes of 53 patients with SARS-CoV-2 severe pneumonia treated with intravenous Sarilumab in terms of pulmonary function improvement or Intensive Care Unit (ICU) admission rate in medical wards setting and of live discharge rate in ICU treated patients as well as in terms of safety. Each patient received Sarilumab 400 mg administered intravenously on day 1, with eventual additional infusion based on clinical judgement, and was followed for at least 14 days, unless previously discharged or dead. No gluco-corticosteroids were used at baseline. Results: Of the 53 SARS-CoV-2pos patients receiving Sarilumab, 39 (73.6%) were treated in medical wards (66.7% with a single infusion) while 14 (26.4%) in ICU (92.6% with a second infusion). The median PaO2/FiO2 of patients in the Medical Ward was 146(IQR:120-212) while the median PaO2/FiO2 of patients in ICU was 112(IQR:100-141.5), respectively. Within the medical wards, 7(17.9%) required ICU admission, 4 of whom were re-admitted to the ward within 5-8 days. At 19 days median follow-up, 89.7% of medical inpatients significantly improved (46.1% after 24 hours, 61.5% after 3 days), 70.6% were discharged from the hospital and 85.7% no longer needed oxygen therapy. Within patients receiving Sarilumab in ICU, 64.2% were discharged from ICU to the ward and 35.8% were still alive at the last follow-up. Overall mortality rate was 5.7% after Sarilumab administration: 1(2.5%) patient died in the Medical Ward whilst 2(14.2%) patients died in ICU, respectively. Conclusions and relevance: IL6-R inhibition appears to be a potential treatment strategy for severe SARS-CoV-2 pneumonia and intravenous Sarilumab seems a promising treatment approach showing, in the short term, an important clinical benefit and good safety. 2020-05-18
Zahra Raisi-Estabragh; Celeste McCracken; Maddalena Ardissino; Mae S Bethell; Jackie Cooper; Cyrus Cooper; Nicholas C Harvey; Steffen E Petersen NON-WHITE ETHNICITY, MALE SEX, AND HIGHER BODY MASS INDEX, BUT NOT MEDICATIONS ACTING ON THE RENIN-ANGIOTENSIN SYSTEM ARE ASSOCIATED WITH CORONAVIRUS DISEASE 2019 (COVID-19) HOSPITALISATION: REVIEW OF THE FIRST 669 CASES FROM THE UK BIOBANK Background: Cardiometabolic morbidity and medications, specifically Angiotensin Converting Enzyme inhibitors (ACEi) and Angiotensin Receptor Blockers (ARBs), have been linked with adverse outcomes from coronavirus disease 2019 (COVID-19). This study aims to investigate factors associated with COVID-19 positivity for the first 669 UK Biobank participants; compared with individuals who tested negative, and with the untested, presumed negative, rest of the population. Methods: We studied 1,474 participants from the UK Biobank who had been tested for COVID-19. Given UK testing policy, this implies a hospital setting, suggesting at least moderate to severe symptoms. We considered the following exposures: age, sex, ethnicity, body mass index (BMI), diabetes, hypertension, hypercholesterolaemia, ACEi/ARB use, prior myocardial infarction (MI), and smoking. We undertook comparisons between: 1) COVID-19 positive and COVID-19 tested negative participants; and 2) COVID-19 tested positive and the remaining participants (tested negative plus untested, n=501,837). Logistic regression models were used to investigate univariate and mutually adjusted associations. Results: Among participants tested for COVID-19, non-white ethnicity, male sex, and greater BMI were independently associated with COVID-19 positive result. Non-white ethnicity, male sex, greater BMI, diabetes, hypertension, prior MI, and smoking were independently associated with COVID-19 positivity compared to the remining cohort (test negatives plus untested). However, similar associations were observed when comparing those who tested negative for COVID-19 with the untested cohort; suggesting that these factors associate with general hospitalisation rather than specifically with COVID-19. Conclusions: Among participants tested for COVID-19 with presumed moderate to severe symptoms in a hospital setting, non-white ethnicity, male sex, and higher BMI are associated with a positive result. Other cardiometabolic morbidities confer increased risk of hospitalisation, without specificity for COVID-19. Notably, ACE/ARB use did not associate with COVID-19 status. 2020-05-15
Anton De Spiegeleer; Antoon Bronselaer; James T Teo; Geert Byttebier; Guy De Tre; Luc Belmans; Richard Dobson; Evelien Wynendaele; Christophe Van De Wiele; Filip Vandaele; Diemer Van Dijck; Daniel Bean; David Fedson; Bart De Spiegeleer The effects of ARBs, ACEIs and statins on clinical outcomes of COVID-19 infection among nursing home residents Background. COVID-19 infection has limited preventive or therapeutic drug options at this stage. Some of common existing drugs like angiotensin-converting enzyme inhibitors (ACEi), angiotensin II receptor blockers (ARB) and the HMG-CoA reductase inhibitors (statins) have been hypothesised to impact on disease severity. However, up till now, no studies investigating this association were conducted in the most vulnerable and affected population groups, i.e. older people residing in nursing homes. The purpose of this study has been to explore the association of ACEi/ARB and/or statins with clinical manifestations in COVID-19 infected older people residing in nursing homes. Methods and Findings. We undertook a retrospective multi-centre cohort study in two Belgian nursing homes that experienced similar COVID-19 outbreaks. COVID-19 diagnoses were based on clinical suspicion and/or viral presence using PCR of nasopharyngeal samples. A total of 154 COVID-19 positive subjects was identified. The outcomes were defined as 1) serious COVID-19 defined as a long-stay hospital admission (length of stay [&ge;] 7 days) or death (at hospital or nursing home) within 14 days of disease onset, and 2) asymptomatic, i.e. no disease symptoms in the whole study-period while still being PCR diagnosed. Disease symptoms were defined as any COVID-19-related clinical symptom (e.g. coughing, dyspnoea, sore throat) or sign (low oxygen saturation and fever) for [&ge;] 2 days out of 3 consecutive days. Logistic regression models with Firth corrections were applied on these 154 subjects to analyse the association between ACEi/ARB and/or statin use with the outcomes. Age, sex, functional status, diabetes and hypertension were used as covariates. Sensitivity analyses were conducted to evaluate the robustness of our statistical significant findings. We found a statistically significant association between statin intake and the absence of symptoms during COVID-19 infection (unadjusted OR 2.91; CI 1.27-6.71; p=0.011), which remained statistically significant after adjusting for age, sex, functional status, diabetes mellitus and hypertension. The strength of this association was considerable and clinically important. Although the effects of statin intake on serious clinical outcome (long-stay hospitalisation or death) were in the same beneficial direction, these were not statistically significant (OR 0.75; CI 0.25-1.85; p=0.556). There was also no statistically significant association between ACEi/ARB and asymptomatic status (OR 1.52; CI 0.62-3.50; p=0.339) or serious clinical outcome (OR 0.79; CI 0.26-1.95; p=0.629). Conclusions. Our data indicate that statin intake in old, frail people could be associated with a considerable beneficial effect on COVID-19 related clinical symptoms. The role of statins and any interaction with renin-angiotensin system drugs need to be further explored in larger observational studies as well as randomised clinical trials. 2020-05-15
Lucy Rivett; Sushmita Sridhar; Dominic Sparkes; Matthew Routledge; Nicholas K. Jones; Sally Forrest; Jamie Young; Joana Pereira-Dias; William L Hamilton; Mark Ferris; Estee Torok; Luke Meredith; The CITIID-NIHR COVI Bioresource Collaboration; Martin Curran; Stewart Fuller; Afzal Chaudhry; Ashley Shaw; Richard J. Samsworth; John R. Bradley; Gordon Dougan; Kenneth G. C. Smith; Paul J. Lehner; Nicholas J. Matheson; Giles Wright; Ian Goodfellow; Stephen Baker; Michael P. Weekes Screening of healthcare workers for SARS-CoV-2 highlights the role of asymptomatic carriage in COVID-19 transmission Significant differences exist in the availability of healthcare worker (HCW) SARS-CoV-2 testing between countries, and existing programmes focus on screening symptomatic rather than asymptomatic staff. Over a 3-week period (April 2020), 1,032 asymptomatic HCWs were screened for SARS-CoV-2 in a large UK teaching hospital. Symptomatic staff and symptomatic household contacts were additionally tested. Real-time RT-PCR was used to detect viral RNA from a throat+nose self-swab. 3% of HCWs in the asymptomatic screening group tested positive for SARS-CoV-2. 17/30 (57%) were truly asymptomatic/pauci-symptomatic. 12/30 (40%) had experienced symptoms compatible with coronavirus disease 2019 (COVID-19) >7 days prior to testing, most self-isolating, returning well. Clusters of HCW infection were discovered on two independent wards. Viral genome sequencing showed that the majority of HCWs had the dominant lineage B{middle dot}1. Our data demonstrates the utility of comprehensive screening of HCWs with minimal or no symptoms. This approach will be critical for protecting patients and hospital staff. 2020-05-15
Stephen N. Crooke; Inna G. Ovsyannikova; Richard B. Kennedy; Gregory A. Poland Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome A novel coronavirus (SARS-CoV-2) emerged from China in late 2019 and rapidly spread across the globe, infecting millions of people and generating societal disruption on a level not seen since the 1918 influenza pandemic. A safe and effective vaccine is desperately needed to prevent the continued spread of SARS-CoV-2; yet, rational vaccine design efforts are currently hampered by the lack of knowledge regarding viral epitopes targeted during an immune response, and the need for more in-depth knowledge on betacoronavirus immunology. To that end, we developed a computational workflow using a series of open-source algorithms and webtools to analyze the proteome of SARS-CoV-2 and identify putative T cell and B cell epitopes. Using increasingly stringent selection criteria to select peptides with significant HLA promiscuity and predicted antigenicity, we identified 41 potential T cell epitopes (5 HLA class I, 36 HLA class II) and 6 potential B cell epitopes, respectively. Docking analysis and binding predictions demonstrated enrichment for peptide binding to HLA-B (class I) and HLA-DRB1 (class II) molecules. Overlays of predicted B cell epitopes with the structure of the viral spike (S) glycoprotein revealed that 4 of 6 epitopes were located in the receptor-binding domain of the S protein. To our knowledge, this is the first study to comprehensively analyze all 10 (structural, non-structural and accessory) proteins from SARS-CoV-2 using predictive algorithms to identify potential targets for vaccine development. Significance StatementThe novel coronavirus SARS-CoV-2 recently emerged from China, rapidly spreading and ushering in a global pandemic. Despite intensive research efforts, our knowledge of SARS-CoV-2 immunology and the proteins targeted by the immune response remains relatively limited, making it difficult to rationally design candidate vaccines. We employed a suite of bioinformatic tools, computational algorithms, and structural modeling to comprehensively analyze the entire SARS-CoV-2 proteome for potential T cell and B cell epitopes. Utilizing a set of stringent selection criteria to filter peptide epitopes, we identified 41 T cell epitopes (5 HLA class I, 36 HLA class II) and 6 B cell epitopes that could serve as promising targets for peptide-based vaccine development against this emerging global pathogen. 2020-05-14
Luke W Meredith; William L Hamilton; Ben Warne; Charlotte J Houldcroft; Myra Hosmillo; Aminu Jahun; Martin D Curran; Surendra Parmar; Laura Caller; Sarah L Caddy; Fahad A Khokhar; Anna Yakovleva; Grant R Hall; Theresa Feltwell; Sally N Forret; Sushmita Sridhar; Michael p Weekes; Stephen Baker; Nicholas Brown; Elinor Moore; Theodore Gouliouris; Ashley Popay; Iain Roddick; Mark Reacher; Sharon Peacock; Gordon Dougan; M. Estee Torok; Ian Goodfellow Rapid implementation of real-time SARS-CoV-2 sequencing to investigate healthcare-associated COVID-19 infections Background The burden and impact of healthcare-associated COVID-19 infections is unknown. We aimed to examine the utility of rapid sequencing of SARS-CoV-2 combined with detailed epidemiological analysis to investigate healthcare-associated COVID-19 infections and to inform infection control measures. Methods We set up rapid viral sequencing of SARS-CoV-2 from PCR-positive diagnostic samples using nanopore sequencing, enabling sample-to-sequence in less than 24 hours. We established a rapid review and reporting system with integration of genomic and epidemiological data to investigate suspected cases of healthcare-associated COVID-19. Results Between 13 March and 24 April 2020 we collected clinical data and samples from 5191 COVID-19 patients in the East of England. We sequenced 1000 samples, producing 747 complete viral genomes. We conducted combined epidemiological and genomic analysis of 299 patients at our hospital and identified 26 genomic clusters involving 114 patients. 66 cases (57.9%) had a strong epidemiological link and 15 cases (13.2%) had a plausible epidemiological link. These results were fed back clinical, infection control and hospital management teams, resulting in infection control interventions and informing patient safety reporting. Conclusions We established real-time genomic surveillance of SARS-CoV-2 in a UK hospital and demonstrated the benefit of combined genomic and epidemiological analysis for the investigation of healthcare-associated COVID-19 infections. This approach enabled us to detect cryptic transmission events and identify opportunities to target infection control interventions to reduce further healthcare-associated infections. 2020-05-14
Daniel Ward; Matthew Higgins; Jody Phelan; Martin L. Hibberd; Susana Campino; Taane G Clark An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets BackgroundThe COVID-19 pandemic is causing a major global health and socio-economic burden, instigating the mobilisation of resources into the development of control tools, such as diagnostics and vaccines. The poor performance of some diagnostic serological tools has emphasised the need for up to date immune-informatic analyses to inform the selection of viable targets for further study. This requires the integration and analysis of genetic and immunological data for SARS-CoV-2 and its homology with other human coronavirus species to understand cross-reactivity. MethodsWe have developed an online tool for SARS-CoV-2 research, which combines an extensive epitope mapping and prediction meta-analysis, with an updated variant database (55,944 non-synonymous mutations) based on 16,087 whole genome sequences, and an analysis of human coronavirus homology. To demonstrate its utility, we present an integrated analysis of the SARS-CoV-2 spike and nucleocapsid proteins, which are candidate vaccine and serological diagnostic targets. ResultsOur analysis reveals that the nucleocapsid protein in its native form appears to be a sub-optimal target for use in serological diagnostic platforms. Whilst, a further analysis suggests that orf3a proteins may be a suitable alternative target for diagnostic assays. ConclusionsThe tool can be accessed online (http://genomics.lshtm.ac.uk/immuno) and will serve as a useful tool for biological discovery in the fight against SARS-CoV-2. Further, it may be adapted to inform on biological targets in future outbreaks, including new human coronaviruses that spill over from animal hosts. 2020-05-13
David Rubin; Jing Huang; Brian T Fisher; Antonio Gasparrini; Vicky Tam; Lihai Song; Xi Wang; Jason Kaufman; Kate Fitzpatrick; Arushi Jain; Heather Griffis; Koby Crammer; Gregory Tasian The Association of Social Distancing, Population Density, and Temperature with the SARS-CoV-2 Instantaneous Reproduction Number in Counties Across the United States Importance: The Covid-19 pandemic has been marked by considerable heterogeneity in outbreaks across the United States. Local factors that may be associated with variation in SARS-CoV-2 transmission have not been well studied. Objective: To examine the association of county-level factors with variation in the SARS-CoV-2 reproduction number over time. Design: Observational study Setting: 211 counties in 46 states and the District of Columbia between February 25, 2020 and April 23, 2020. Participants: Residents within the counties (55% of the US population) Exposures: Social distancing as measured by percent change in visits to non-essential businesses, population density, lagged daily wet bulb temperatures. Main Outcomes and Measures: The instantaneous reproduction number (Rt) which is the estimated number of cases generated by one case at a given time during the pandemic. Results: Median case incidence was 1185 cases and fatality rate was 43.7 deaths per 100,000 people for the top decile of 21 counties, nearly ten times the incidence and fatality rate in the lowest density quartile. Average Rt in the first two weeks was 5.7 (SD 2.5) in the top decile, compared to 3.1 (SD 1.2) in the lowest quartile. In multivariable analysis, a 50% decrease in visits to non-essential businesses was associated with a 57% decrease in Rt (95% confidence interval, 56% to 58%). Cumulative temperature effects over 4 to 10 days prior to case incidence were nonlinear; relative Rt decreased as temperatures warmed above 32F to 53F, which was the point of minimum Rt, then increased between 53F and 66F, at which point Rt began to decrease. At 55F, and with a 70% reduction in visits to non-essential business, 96% of counties were estimated to fall below a threshold Rt of 1.0, including 86% of counties among the top density decile and 98% of counties in the lowest density quartile. Conclusions and Relevance: Social distancing, lower population density, and temperate weather change were associated with a decreased SARS-Co-V-2 Rt in counties across the United States. These relationships can inform selective public policy planning in communities during the SARS-CoV-2 pandemic. 2020-05-12
Helen I McDonald; Elise Tessier; Joanne M White; Matthew Woodruff; Charlotte Knowles; Chris Bates; John Parry; Jemma L Walker; J Anthony Scott; Liam Smeeth; Joanne Yarwood; Mary Ramsay; Michael Edelstein Early impact of the COVID-19 pandemic and social distancing measures on routine childhood vaccinations in England, January to April 2020 Electronic health records were used to assess the early impact of COVID-19 on routine childhood vaccination in England to 26 April 2020. MMR vaccination counts fell from February 2020, and in the three weeks after introduction of social distancing measures were 19.8% lower (95% CI -20.7 to -18.9%) than the same period in 2019, before improving in mid-April. A gradual decline in hexavalent vaccination counts throughout 2020 was not accentuated on introduction of social distancing. 2020-05-11
Benjamin Parcell; Kathryn Brechin; Sarah Allstaff; Meg Park; Wendy Third; Susan Bean; Chris Hind; Rajiv Farmer; James D Chalmers Drive-through testing for SARS-CoV-2 in symptomatic health and social care workers and household members: an observational cohort study in Tayside, Scotland It has been recognised that health and social care workers (HSCW) experience higher rates of infection with SARS-CoV-2. Widespread testing of HSCWs and their symptomatic household contacts (SHCs) has not been fully implemented in the United Kingdom. We describe the results of a testing programme for HSCWs and SHCs in a single UK region (Tayside, Scotland). The testing service was established 17 th March 2020 as the first in the country, and samples were collected at a drive-through testing hub based at a local community hospital. HSCWs with mild symptoms who were self-isolating and the SHCs of HSCWs who would therefore be absent from work attended for testing. From 17 th March 2020 to 11 th April, 1887 HSCWs and SHCs underwent testing. Clinical information was available for 1727 HSCWs and SHCs. 4/155 (2.6%) child contacts, 73/374 (19.5%) adult contacts and 325/1173 (27.7%) HSCWs tested positive for SARS-CoV-2. 15 of 188 undetermined cases were positive (8.0%). We estimate that testing prevented up to 3634 lost work days from HSCW testing, 2795 from adult SHC testing and 1402 lost work days from child SHC testing. The establishment of this testing programme has assisted the infection prevention and control team in their investigation of transmission and supported adequate staffing in health and social care sectors. 2020-05-11
Jon C Emery; Timothy W Russel; Yang Liu; Joel Hellewell; Carl AB Pearson; - CMMID 2019-nCoV working group; Gwen M Knight; Rosalind M Eggo; Adam J Kucharski; Sebastian Funk; Stefan Flasche; Rein M G J Houben The contribution of asymptomatic SARS-CoV-2 infections to transmission - a model-based analysis of the Diamond Princess outbreak Background: Some key gaps in the understanding of SARS-CoV-2 infection remain. One of them is the contribution to transmission from individuals experiencing asymptomatic infections. We aimed to characterise the proportion and infectiousness of asymptomatic infections using data from the outbreak on the Diamond Princess cruise ship. Methods: We used a transmission model of COVID-19 with asymptomatic and presymptomatic states calibrated to outbreak data from the Diamond Princess, to quantify the contribution of asymptomatic infections to transmission. Data available included the date of symptom onset for symptomatic disease for passengers and crew, the number of symptom agnostic tests done each day, and date of positive test for asymptomatic and presymptomatic individuals. Findings: On the Diamond Princess 74% (70-78%) of infections proceeded asymptomatically, i.e. a 1:3.8 case-to-infection ratio. Despite the intense testing 53%, (51-56%) of infections remained undetected, most of them asymptomatic. Asymptomatic individuals were the source for 69% (20-85%) of all infections. While the data did not allow identification of the infectiousness of asymptomatic infections, assuming no or low infectiousness resulted in posterior estimates for the net reproduction number of an individual progressing through presymptomatic and symptomatic stages in excess of 15. Interpretation: Asymptomatic SARS-CoV-2 infections may contribute substantially to transmission. This is essential to consider for countries when assessing the potential effectiveness of ongoing control measures to contain COVID-19. 2020-05-11
Matt J Keeling; Edward Hill; Erin Gorsich; Bridget Penman; Glen Guyver-Fletcher; Alex Holmes; Trystan Leng; Hector McKimm; Massimiliano Tamborrino; Louise Dyson; Michael Tildesley Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategies Background: Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a "stay at home" order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. Methods: We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020, on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. Findings: We find that significant relaxation of social distancing measures on 7th May can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Conclusions: Our work supports the decision to apply stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. We provide strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units. 2020-05-11
Jia Huang; Le Zheng; Zhen Li; Shiying Hao; Fangfan Ye; Jun Chen; Xiaoming Yao; Jiayu Liao; Song Wang; Manfei Zeng; Liping Qiu; Fanlan Cen; Yajing Huang; Tengfei Zhu; Zehui Xu; Manhua Ye; Yang Yang; Guowei Wang; Jinxiu Li; Lifei Wang; Jiuxin Qu; Jing Yuan; Wei Zheng; Zheng Zhang; Chunyang Li; John C Whitin; Lu Tian; Henry Chubb; KuoYuan Hwa; Hayley A Gans; Scott R Ceresnak; Wei Zhang; Ying Lu; Yvonne A Maldonado; Harvey J Cohen; Doff B McElhinney; Karl G Sylvester; Qing He; Zhaoqin Wang; Yingxia Liu; Lei Liu; Xuefeng B Ling Recurrence of SARS-CoV-2 PCR positivity in COVID-19 patients: a single center experience and potential implications IMPORTANCE How to appropriately care for patients who become PCR-negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still not known. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system if a subset were to be PCR-positive again with reactivated SARS-CoV-2. OBJECTIVE To characterize a single center COVID-19 cohort with and without recurrence of PCR positivity, and develop an algorithm to identify patients at high risk of retest positivity after discharge to inform health care policy and case management decision-making. DESIGN, SETTING, AND PARTICIPANTS A cohort of 414 patients with confirmed SARS-CoV-2 infection, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020. EXPOSURES Polymerase chain reaction (PCR) and IgM-IgG antibody confirmed SARS-CoV-2 infection. MAIN OUTCOMES AND MEASURES Univariable and multivariable statistical analysis of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data to develop an algorithm to predict patients at risk of recurrence of PCR positivity. RESULTS 16.7% (95CI: 13.0%-20.3%) patients retest PCR positive 1 to 3 times after discharge, despite being in strict quarantine. The driving factors in the recurrence prediction model included: age, BMI; lowest levels of the blood laboratory tests during hospitalization for cholinesterase, fibrinogen, albumin, prealbumin, calcium, eGFR, creatinine; highest levels of the blood laboratory tests during hospitalization for total bilirubin, lactate dehydrogenase, alkaline phosphatase; the first test results during hospitalization for partial pressure of oxygen, white blood cell and lymphocyte counts, blood procalcitonin; and the first test episodic Ct value and the lowest Ct value of the nasopharyngeal swab RT PCR results. Area under the ROC curve is 0.786. CONCLUSIONS AND RELEVANCE This case series provides clinical characteristics of COVID-19 patients with recurrent PCR positivity, despite strict quarantine, at a 16.7% rate. Use of a recurrence prediction algorithm may identify patients at high risk of PCR retest positivity of SARS-CoV-2 and help modify COVID-19 case management and health policy approaches. 2020-05-10
Elizabeth Sapey; Suzy Gallier; Chris Mainey; Peter Nightingale; David McNulty; Hannah Crothers; Felicity Evison; Katharine Reeves; Domenico Pagano; Alastair K Denniston; Krishnarajah Nirantharakumar; Peter Diggle; Simon Ball Ethnicity and risk of death in patients hospitalised for COVID-19 infection: an observational cohort study in an urban catchment area Objectives. To determine if specific ethnic groups are at higher risk of mortality from COVID19 infection. Design. Retrospective cohort study Setting. University Hospitals Birmingham NHS Foundation Trust (UHB) in Birmingham, UK Participants. Patients with confirmed SARS CoV 2 infection requiring admission to UHB between 10th March 2020 and 17th April 2020 Exposure. Ethnicity Main outcome measures. Standardised Admission Ratio (SAR) and Standardised Mortality Ratio (SMR) for each ethnicity was calculated using observed sex specific age distributions of COVID19 admissions/deaths and 2011 census data for Birmingham/Solihull. Hazard Ratio (aHR) for mortality was estimated for each ethnic group with white population as reference group, using Cox proportional hazards model adjusting for age, sex, social deprivation and co-morbidities, and propensity score matching. Results. 2217 patients admitted to UHB with a proven diagnosis of COVID19 were included. 58.2% were male, 69.5% White and the majority (80.2%) had co morbidities. 18.5% were of South Asian ethnicity, and these patients were more likely to be younger (median age 61 years vs.77 years), have no co morbidities (27.8% vs. 16.6%) but a higher prevalence of diabetes mellitus (48.0% vs 28.2%) than White patients. SAR and SMR suggested more admissions and deaths in South Asian patients than would be predicted. South Asian patients were also more likely to present with severe disease despite no delay in presentation since symptom onset. South Asian ethnicity was associated with an increased risk of death; both by Cox regression (Hazard Ratio 1.66 (95%CI 1.32 to 2.10)) after adjusting for age, sex, deprivation and comorbidities and by propensity score matching, (Hazard ratio 1.68 (1.33 to 2.13), using the same factors but categorising ethnicity into South Asian or not. Conclusions. Current evidence suggests those of South Asian ethnicity may be at risk of worse COVID19 outcomes, further studies need to establish the underlying mechanistic pathways. 2020-05-09
Bram A.D. van Bunnik; Alex L.K. Morgan; Paul Bessell; Giles Calder-Gerver; Feifei Zhang; Samuel Haynes; Jordan Ashworth; Shengyuan Zhao; Nicola Rose Cave; Meghan R. Perry; Hannah C. Lepper; Lu Lu; Paul Kellam; Aziz Sheikh; Graham F. Medley; Mark E.J. Woolhouse Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown In this study we demonstrate that the adoption of a segmenting and shielding (S&S) strategy could increase scope to partially exit COVID-19 lockdown while limiting the risk of an overwhelming second wave of infection. The S&S strategy has an antecedent in the "cocooning" of infants by immunisation of close family members (Forsyth et al., 2015), and forms a pillar of infection, prevention and control (IPC) strategies (RCN, 2017). We are unaware of it being proposed as a major public health initiative previously. We illustrate the S&S strategy using a mathematical model that segments the vulnerable population and their closest contacts, the "shielders". We explore the effects on the epidemic curve of a gradual ramping up of protection for the vulnerable population and a gradual ramping down of restrictions on the non-vulnerable population over a period of 12 weeks after lockdown. The most important determinants of outcome are: i) post-lockdown transmission rates within the general population segment and between the general and vulnerable segments; ii) the fraction of the population in the vulnerable and shielder segments; iii) adherence with need to be protected; and iv) the extent to which population immunity builds up in all segments. We explored the effects of extending the duration of lockdown and faster or slower transition to post-lockdown conditions and, most importantly, the trade-off between increased protection of the vulnerable segment and fewer restrictions on the general population. We illustrate how the potential for the relaxation of restrictions interacts with specific policy objectives. We show that the range of options for relaxation in the general population can be increased by maintaining restrictions on the shielder segment and by intensive routine screening of shielders. We find that the outcome of any future policy is strongly influenced by the contact matrix between segments and the relationships between physical distancing measures and transmission rates. These relationships are difficult to quantify so close monitoring of the epidemic would be essential during and after the exit from lockdown. More generally, S&S has potential applications for any infectious disease for which there are defined proportions of the population who cannot be treated or who are at risk of severe outcomes. 2020-05-08
Dan Lewer; Isobel Braithwaite; Miriam Bullock; Max T Eyre; Robert W Aldridge COVID-19 and homelessness in England: a modelling study of theCOVID-19 pandemic among people experiencing homelessness, and theimpact of a residential intervention to isolate vulnerable people andcare for people with symptoms Background: There is an ongoing pandemic of the viral respiratory disease COVID-19. People experiencing homelessness are vulnerable to infection and severe disease. Health and housing authorities in England have developed a residential intervention that aims to isolate those vulnerable to severe disease (COVID-PROTECT) and care for people with symptoms (COVID CARE). Methods: We used a discrete-time Markov chain model to forecast COVID-19 infections among people experiencing homelessness, given strong containment measures in the general population and some transmission among 35,817 people living in 1,065 hostels, and 11,748 people sleeping rough (the 'do nothing' scenario). We then estimated demand for beds if those eligible are offered COVID-PROTECT and COVID-CARE. We estimated the reduction in the number of COVID-19 cases, deaths, and hospital admissions that could be achieved by these interventions. We also conducted sensitivity and scenario analyses to identify programme success factors. Results: In a 'do nothing' scenario, we estimate that 34% of the homeless population could get COVID-19 between March and August 2020, with 364 deaths, 4,074 hospital admissions and 572 critical care admissions. In our 'base intervention' scenario, demand for COVID-PROTECT peaks at 9,934 beds, and demand for COVID-CARE peaks at 1,366 beds. The intervention could reduce transmission by removing symptomatic individuals from the community, and preventing vulnerable individuals from being infected. This could lead to a reduction of 164 deaths, 2,624 hospital admissions, and 248 critical care admissions over this period. Sensitivity analyses showed that the number of deaths is sensitive to transmission of COVID-19 in COVID-PROTECT. If COVID-PROTECT capacity is limited, scenario analyses show the benefit of prioritising people who are vulnerable to severe disease. Conclusion: Supportive accommodation can mitigate the impact of the COVID-19 pandemic on the homeless population of England, and reduce the burden on acute hospitals. 2020-05-08
Nicholas G Davies; Sedona Sweeney; Sergio Torres-Rueda; Fiammetta Bozzani; Nichola Kitson; Edwine Barasa; Simon Procter; Matthew Quaife; - LSHTM Centre for Mathematical Modelling of Infectious Diseases COVID-19 Working Group; Rosalind M Eggo; Anna Vassall; Mark Jit The impact of Coronavirus disease 2019 (COVID-19) on health systems and household resources in Africa and South Asia Background. Coronavirus disease 2019 (COVID-19) epidemics strain health systems and households. Health systems in Africa and South Asia may be particularly at risk due to potential high prevalence of risk factors for severe disease, large household sizes and limited healthcare capacity. Methods. We investigated the impact of an unmitigated COVID-19 epidemic on health system resources and costs, and household costs, in Karachi, Delhi, Nairobi, Addis Ababa and Johannesburg. We adapted a dynamic model of SARS-CoV-2 transmission and disease to capture country-specific demography and contact patterns. The epidemiological model was then integrated into an economic framework that captured city-specific health systems and household resource use. Findings. The cities severely lack intensive care beds, healthcare workers and financial resources to meet demand during an unmitigated COVID-19 epidemic. A highly mitigated COVID-19 epidemic, under optimistic assumptions, may avoid overwhelming hospital bed capacity in some cities, but not critical care capacity. Interpretation. Viable mitigation strategies encompassing a mix of responses need to be established to expand healthcare capacity, reduce peak demand for healthcare resources, minimise progression to critical care and shield those at greatest risk of severe disease. 2020-05-08
Yan Wu; Feiran Wang; Chenguang Shen; Weiyu Peng; Delin Li; Cheng Zhao; Zhaohui Li; Shihua Li; Yuhai Bi; Yang Yang; Yuhuan Gong; Haixia Xiao; Zheng Fan; Shuguang Tan; Guizhen Wu; Wenjie Tan; Xuancheng Lu; Changfa Fan; Qihui Wang; Yingxia Liu; Jianxun Qi; George Fu Gao; Feng Gao; Lei Liu A non-competing pair of human neutralizing antibodies block COVID-19 virus binding to its receptor ACE2 Neutralizing antibodies could be antivirals against COVID-19 pandemics. Here, we report the isolation of four human-origin monoclonal antibodies from a convalescent patient in China. All of these isolated antibodies display neutralization abilities in vitro. Two of them (B38 and H4) block the binding between RBD and vial cellular receptor ACE2. Further competition assay indicates that B38 and H4 recognize different epitopes on the RBD, which is ideal for a virus-targeting mAb-pair to avoid immune escape in the future clinical applications. Moreover, therapeutic study on the mouse model validated that these two antibodies can reduce virus titers in the infected mouse lungs. Structure of RBD-B38 complex revealed that most residues on the epitope are overlapped with the RBD-ACE2 binding interface, which explained the blocking efficacy and neutralizing capacity. Our results highlight the promise of antibody-based therapeutics and provide the structural basis of rational vaccine design. 2020-05-07
- The OpenSAFELY Collaborative; Elizabeth Williamson; Alex J Walker; Krishnan J Bhaskaran; Seb Bacon; Chris Bates; Caroline E Morton; Helen J Curtis; Amir Mehrkar; David Evans; Peter Inglesby; Jonathan Cockburn; Helen I Mcdonald; Brian MacKenna; Laurie Tomlinson; Ian J Douglas; Christopher T Rentsch; Rohini Mathur; Angel Wong; Richard Grieve; David Harrison; Harriet Forbes; Anna Schultze; Richard T Croker; John Parry; Frank Hester; Sam Harper; Rafael Perera; Stephen Evans; Liam Smeeth; Ben Goldacre OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients. Background Establishing who is at risk from a novel rapidly arising cause of death, and why, requires a new approach to epidemiological research with very large datasets and timely data. Working on behalf of NHS England we therefore set out to deliver a secure and pseudonymised analytics platform inside the data centre of a major primary care electronic health records vendor establishing coverage across detailed primary care records for a substantial proportion of all patients in England. The following results are preliminary. Data sources Primary care electronic health records managed by the electronic health record vendor TPP, pseudonymously linked to patient-level data from the COVID-19 Patient Notification System (CPNS) for death of hospital inpatients with confirmed COVID-19, using the new OpenSAFELY platform. Population 17,425,445 adults. Time period 1st Feb 2020 to 25th April 2020. Primary outcome Death in hospital among people with confirmed COVID-19. Methods Cohort study analysed by Cox-regression to generate hazard ratios: age and sex adjusted, and multiply adjusted for co-variates selected prospectively on the basis of clinical interest and prior findings. Results There were 5683 deaths attributed to COVID-19. In summary after full adjustment, death from COVID-19 was strongly associated with: being male (hazard ratio 1.99, 95%CI 1.88-2.10); older age and deprivation (both with a strong gradient); uncontrolled diabetes (HR 2.36 95% CI 2.18-2.56); severe asthma (HR 1.25 CI 1.08-1.44); and various other prior medical conditions. Compared to people with ethnicity recorded as white, black people were at higher risk of death, with only partial attenuation in hazard ratios from the fully adjusted model (age-sex adjusted HR 2.17 95% CI 1.84-2.57; fully adjusted HR 1.71 95% CI 1.44-2.02); with similar findings for Asian people (age-sex adjusted HR 1.95 95% CI 1.73-2.18; fully adjusted HR 1.62 95% CI 1.43-1.82). Conclusions We have quantified a range of clinical risk factors for death from COVID-19, some of which were not previously well characterised, in the largest cohort study conducted by any country to date. People from Asian and black groups are at markedly increased risk of in-hospital death from COVID-19, and contrary to some prior speculation this is only partially attributable to pre-existing clinical risk factors or deprivation; further research into the drivers of this association is therefore urgently required. Deprivation is also a major risk factor with, again, little of the excess risk explained by co-morbidity or other risk factors. The findings for clinical risk factors are concordant with policies in the UK for protecting those at highest risk. Our OpenSAFELY platform is rapidly adding further NHS patients' records; we will update and extend these results regularly. Keywords COVID-19, risk factors, ethnicity, deprivation, death, informatics. 2020-05-07
Juan Juan; Maria M Gil; Zhihui Rong; Yuanzhen Zhang; Huixia Yang; Liona Chiu Yee Poon Effects of Coronavirus Disease 2019 (COVID-19) on Maternal, Perinatal and Neonatal Outcomes: a Systematic Review of 266 Pregnancies Objective: To perform a systematic review of available published literature on pregnancies affected by COVID-19 to evaluate the effects of COVID-19 on maternal, perinatal and neonatal outcomes. Methods: We performed a systematic review to evaluate the effects of COVID-19 on pregnancy, perinatal and neonatal outcomes. We conducted a comprehensive literature search using PubMed, EMBASE, Cochrane library, China National Knowledge Infrastructure Database and Wan Fang Data until April 20, 2020 (studies were identified through PubMed alert after April 20, 2020). For the research strategy, combinations of the following keywords and MeSH terms were used: SARS-CoV-2, COVID-19, coronavirus disease 2019, pregnancy, gestation, maternal, mothers, vertical transmission, maternal-fetal transmission, intrauterine transmission, neonates, infant, delivery. Eligibility criteria included laboratory-confirmed and/or clinically diagnosed COVID-19, patient was pregnant on admission, availability of clinical characteristics, including maternal, perinatal or neonatal outcomes. Exclusion criteria were unpublished reports, unspecified date and location of the study or suspicion of duplicate reporting, and unreported maternal or perinatal outcomes. No language restrictions were applied. Results: We identified several case-reports and case-series but only 19 studies, including a total of 266 pregnant women with COVID-19, met eligibility criteria and were finally included in the review. In the combined data from seven case-series, the maternal age ranged from 20 to 41 years and the gestational age on admission ranged from 5 to 41 weeks. The most common symptoms at presentation were fever, cough, dyspnea/shortness of breath and fatigue. The rate of severe pneumonia was relatively low, with the majority of the cases requiring intensive care unit admission. Almost all cases from the case-series had positive computer tomography chest findings. There were six and 22 cases that had nucleic-acid testing in vaginal mucus and breast milk samples, respectively, which were negative for SARS-CoV-2. Only a few cases had spontaneous miscarriage or abortion. 177 cases had delivered, of which the majority by Cesarean section. The gestational age at delivery ranged from 28 to 41 weeks. Apgar scores at 1 and 5 minutes ranged from 7 to 10 and 8 to 10, respectively. A few neonates had birthweight less than 2500 grams and over one-third of cases were transferred to neonatal intensive care unit. There was one case each of neonatal asphyxia and neonatal death. There were 113 neonates that had nucleic-acid testing in throat swab, which was negative for SARS-CoV-2. From the case-reports, two maternal deaths among pregnant women with COVID-19 were reported. Conclusions: The clinical characteristics of pregnant women with COVID-19 are similar to those of nonpregnant adults with COVID-19. Currently, there is no evidence that pregnant women with COVID-19 are more prone to develop severe pneumonia, in comparison to nonpregnant patients. The subject of vertical transmission of SARS-CoV-2 remains controversial and more data is needed to investigate this possibility. Most importantly, in order to collect meaningful pregnancy and perinatal outcome data, we urge researchers and investigators to reference previously published cases in their publications and to record such reporting when the data of a case is being entered into a registry or several registries. 2020-05-06
James T Teo; Daniel Bean; Rebecca Bendayan; Richard Dobson; Ajay Shah Impact of ethnicity on outcome of severe COVID-19 infection. Data from an ethnically diverse UK tertiary centre During the current COVID-19 pandemic, anecdotal reports suggest that BAME background patients may be disproportionately affected compared to White but few objective data are available. We took advantage of near real-time hospital data access and analysis pipelines to look at the impact of ethnicity in 437 consecutive patients admitted during March to Kings College Hospital NHS Trust in London. Our key findings are firstly that BAME patients are significantly younger and have different co-morbidity profiles than White individuals. Secondly, there is no significant effect of ethnicity itself on severe outcomes (death or ITU admission) within 14-days of symptom onset, with adjustment for age, sex, comorbidities. 2020-05-06
Jonine Figueroa; Paul Brennan; Evropi Theodoratou; Michael Poon; Karin Purshouse; Farhat Din; Kai Jin; Ines Mesa-Eguiagaray; Malcolm G Dunlop; Peter S Hall; David Cameron; Sarah Wild; Cathie LM Sudlow Trends in excess cancer and cardiovascular deaths in Scotland during the COVID-19 pandemic 30 December 2019 to 20 April 2020 Understanding the trends in causes of death for different diseases during the current COVID-19 pandemic is important to determine whether there are excess deaths beyond what is normally expected. Using the most recent report from National Records Scotland (NRS) on 29 April 2020, we examined the percentage difference in crude numbers of deaths in 2020 compared to the average for 2015-2019 by week of death within calendar year. To determine if trends were similar, suggesting underreporting/underdiagnosed COVID-19 related deaths, we also looked at the trends in % differences for cardiovascular disease deaths. From the first 17 weeks' of data, we found a peak in excess deaths between weeks 14 of 2020, about four weeks after the first case in Scotland was detected on 1 March 2020-- but by week 17 these excesses had diminished around the time lockdown in the UK began. Similar observations were seen for cardiovascular disease-related deaths. These observations suggest that the short-term increase in excess cancer and cardiovascular deaths might be associated with undetected/unconfirmed deaths related to COVID-19. Both of these conditions make patients more susceptible to infection and lack of widespread access to testing for COVID-19 are likely to have resulted in under-estimation of COVID-19 mortality. These data further suggest that the cumulative toll of COVID-19 on mortality is likely undercounted. More detailed analysis is needed to determine if these excesses were directly or indirectly related to COVID-19. Disease specific mortality will need constant monitoring for the foreseeable future as changes occur in increasing capacity and access to testing, reporting criteria, changes to health services and different measures are implemented to control the spread of the COVID-19. Multidisciplinary, multi-institutional, national and international collaborations for complementary and population specific data analysis is required to respond and mitigate adverse effects of the COVID-19 pandemic and to inform planning for future pandemics. 2020-05-06
Kali A Barrett; Yoshiko Nakamachi; Terra Ierasts; Yasin Khan; Stephen Mac; David Naimark; Nathan Stall; Raphael Ximenes; Andrew Morris; Beate Sander A model to estimate demand for personal protective equipment for Ontario acute care hospitals during the COVID-19 pandemic In addition to instituting public health measures for COVID-19, managing healthcare resources is important for outcomes. The experiences in Italy and New York have shown that personal protective equipment (PPE) shortages can cause increased morbidity and mortality. We demonstrate a method to predict PPE demand across a health care system. 2020-05-05
Long H. Nguyen; David Alden Drew; Amit D. Joshi; Chuan-Guo Guo; Wenjie Ma; Raaj S. Mehta; Daniel R. Sikavi; Chun-Han Lo; Sohee Kwon; Mingyang Song; Lorelei A. Mucci; Meir Stampfer; Walter C. Willett; A. Heather Eliassen; Jaime Hart; Jorge E. Chavarro; Janet Rich-Edwards; Richard Davies; Joan Capdevila; Karla A. Lee; Mary Ni Lochlainn; Thomas Varsavsky; Mark Graham; Carol H. Sudre; M. Jorge Cardoso; Jonathan Wolf; Sebastien Ourselin; Claire Steves; Timothy Spector; Andrew T. Chan Risk of symptomatic Covid-19 among frontline healthcare workers Background: Data 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. Methods: We 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. Findings: Among 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 11.6 (95% CI: 10.9 to 12.3) for reporting a positive test. The corresponding aHR was 3.40 (95% CI: 3.37 to 3.43) 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 1.46 (95% CI: 1.21 to 1.76) for those reporting PPE reuse and 1.31 (95% CI: 1.10 to 1.56) 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 4.83 (95% CI: 3.99 to 5.85) if they had adequate PPE, 5.06 (95% CI: 3.90 to 6.57) for reused PPE, and 5.91 (95% CI: 4.53 to 7.71) for inadequate PPE. Interpretation: Frontline 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. 2020-05-05
Amit Sud; Michael Jones; John Broggio; Stephen Scott; Chey Loveday; Bethany Torr; Alice Garrett; David L. Nicol; Shaman Jhanji; Stephen A. Boyce; Matthew Williams; Georgios Lyratzopoulos; Claire Barry; Elio Riboli; Emma Kipps; Ethna McFerran; Mark Lawler; David C. Muller; Muti Abulafi; Richard Houlston; Clare Ann Turnbull Quantifying and mitigating the impact of the COVID-19 pandemic on outcomes in colorectal cancer Background: The COVID-19 pandemic has caused disruption across cancer pathways for diagnosis and treatment. In England, 32% of colorectal cancer (CRC) is diagnosed via urgent symptomatic referral from primary care, the "2-week-wait" (2WW) pathway. Access to routine endoscopy is likely to be a critical bottleneck causing delays in CRC management due to chronic limitation in capacity, acute competition for physician time, and safety concerns. Methods: We used age-specific, stage-specific 10 year CRC survival for England 2007-2017 and 2WW CRC cases volumes. We used per-day hazard ratios of CRC survival generated from observational studies of CRC diagnosis-to-treatment interval to model the effect of different durations of per-patient delay. We utilised data from a large London observational study of faecal immunochemical testing (FIT) in symptomatic patients to model FIT-triage to mitigate delay to colonoscopy. Findings: Modest delays result in significant reduction in survival from CRC with a 4-month delay resulting across age groups in [&ge;]20% reduction in survival in Stage 3 disease and in total over a year, 1,419 attributable deaths across the 11,266 CRC patients diagnosed via the 2WW pathway. FIT triage of >10 ug Hb/g would salvage 1,292/1,419 of the attributable deaths and reduce colonoscopy requirements by >80%. Diagnostic colonoscopy offers net survival in all age groups, providing nosocomial COVID-19 infection rates are kept low (<2.5%). Interpretation To avoid significant numbers of avoidable deaths from CRC, normal diagnostic and surgical throughput must be maintained. An accrued backlog of cases will present to primary care following release of lockdown, supranormal endoscopy capacity will be required to manage this without undue delays. FIT-triage of symptomatic cases provides a rational approach by which to avoid patient delay and mitigate pressure on capacity in endoscopy. This would also reduce exposure to nosocomial COVID-19 infection, relevant in particular to older patient groups. Funding: Breast Cancer Now, Cancer Research UK, Bobby Moore Fund for Cancer Research, National Institute for Health Research (NIHR). 2020-05-05
Kevin van Zandvoort; Christopher I Jarvis; Carl Pearson; Nicholas G Davies; CMMID COVID-19 working group; Timothy W Russell; Adam J Kucharski; Mark J Jit; Stefan Flasche; Rosalind M Eggo; Francesco Checchi Response strategies for COVID-19 epidemics in African settings: a mathematical modelling study Background The health impact of COVID-19 may differ in African settings as compared to countries in Europe or China due to demographic, epidemiological, environmental and socio-economic factors. We evaluated strategies to reduce SARS-CoV-2 burden in African countries, so as to support decisions that balance minimising mortality, protecting health services and safeguarding livelihoods. Methods We used a Susceptible-Exposed-Infectious-Recovered mathematical model, stratified by age, to predict the evolution of COVID-19 epidemics in three countries representing a range of age distributions in Africa (from oldest to youngest average age: Mauritius, Nigeria and Niger), under various effectiveness assumptions for combinations of different non-pharmaceutical interventions: self-isolation of symptomatic people, physical distancing, and shielding (physical isolation) of the high-risk population. We adapted model parameters to better represent uncertainty about what might be expected in African populations, in particular by shifting the distribution of severity risk towards younger ages and increasing the case-fatality ratio. Results We predicted median clinical attack rates over the first 12 months of 17% (Niger) to 39% (Mauritius), peaking at 2-4 months, if epidemics were unmitigated. Self-isolation while symptomatic had a maximum impact of about 30% on reducing severe cases, while the impact of physical distancing varied widely depending on percent contact reduction and R 0 . The effect of shielding high-risk people, e.g. by rehousing them in physical isolation, was sensitive mainly to residual contact with low-risk people, and to a lesser extent to contact among shielded individuals. Response strategies incorporating self-isolation of symptomatic individuals, moderate physical distancing and high uptake of shielding reduced predicted peak bed demand by 46% to 54% and mortality by 60% to 75%. Lockdowns delayed epidemics by about 3 months. Estimates were sensitive to differences in age-specific social mixing patterns, as published in the literature. Discussion In African settings, as elsewhere, current evidence suggests large COVID-19 epidemics are expected. However, African countries have fewer means to suppress transmission and manage cases. We found that self-isolation of symptomatic persons and general physical distancing are unlikely to avert very large epidemics, unless distancing takes the form of stringent lockdown measures. However, both interventions help to mitigate the epidemic. Shielding of high-risk individuals can reduce health service demand and, even more markedly, mortality if it features high uptake and low contact of shielded and unshielded people, with no increase in contact among shielded people. Strategies combining self-isolation, moderate physical distancing and shielding will probably achieve substantial reductions in mortality in African countries. Temporary lockdowns, where socioeconomically acceptable, can help gain crucial time for planning and expanding health service capacity. 2020-05-03
Huayu Zhang; Ting Shi; Xiaodong Wu; Xin Zhang; Kun Wang; Daniel Bean; Richard Dobson; James T Teo; Jiaxing Sun; Pei Zhao; Chenghong Li; Kevin Dhaliwal; Honghan Wu; Qiang Li; Bruce Guthrie Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK Background Accurate risk prediction of clinical outcome would usefully inform clinical decisions and intervention targeting in COVID-19. The aim of this study was to derive and validate risk prediction models for poor outcome and death in adult inpatients with COVID-19. Methods Model derivation using data from Wuhan, China used logistic regression with death and poor outcome (death or severe disease) as outcomes. Predictors were demographic, comorbidity, symptom and laboratory test variables. The best performing models were externally validated in data from London, UK. Findings 4.3% of the derivation cohort (n=775) died and 9.7% had a poor outcome, compared to 34.1% and 42.9% of the validation cohort (n=226). In derivation, prediction models based on age, sex, neutrophil count, lymphocyte count, platelet count, C-reactive protein and creatinine had excellent discrimination (death c-index=0.91, poor outcome c-index=0.88), with good-to-excellent calibration. Using two cut-offs to define low, high and very-high risk groups, derivation patients were stratified in groups with observed death rates of 0.34%, 15.0% and 28.3% and poor outcome rates 0.63%, 8.9% and 58.5%. External validation discrimination was good (c-index death=0.74, poor outcome=0.72) as was calibration. However, observed rates of death were 16.5%, 42.9% and 58.4% and poor outcome 26.3%, 28.4% and 64.8% in predicted low, high and very-high risk groups. Interpretation Our prediction model using demography and routinely-available laboratory tests performed very well in internal validation in the lower-risk derivation population, but less well in the much higher-risk external validation population. Further external validation is needed. Collaboration to create larger derivation datasets, and to rapidly externally validate all proposed prediction models in a range of populations is needed, before routine implementation of any risk prediction tool in clinical care. 2020-05-03
Christoph B. Messner; Vadim Demichev; Daniel Wendisch; Laura Michalick; Matthew White; Anja Freiwald; Kathrin Textoris-Taube; Spyros I. Vernardis; Anna-Sophia Egger; Marco Kreidl; Daniela Ludwig; Christiane Kilian; Federica Agostini; Aleksej Zelezniak; Charlotte Thibeault; Moritz Pfeiffer; Stefan Hippenstiel; Andreas Hocke; Christof von Kalle; Archie Campbell; Caroline Hayward; David J. Porteous; Riccardo E. Marioni; Claudia Langenberg; Kathryn S. Lilley; Wolfgang M. Kuebler; Michael Muelleder; Christian Drosten; Martin Witzenrath; Florian Kurth; Leif Erik Sander; Markus Ralser Clinical classifiers of COVID-19 infection from novel ultra-high-throughput proteomics The COVID-19 pandemic is an unprecedented global challenge. Highly variable in its presentation, spread and clinical outcome, novel point-of-care diagnostic classifiers are urgently required. Here, we describe a set of COVID-19 clinical classifiers discovered using a newly designed low-cost high-throughput mass spectrometry-based platform. Introducing a new sample preparation pipeline coupled with short-gradient high-flow liquid chromatography and mass spectrometry, our methodology facilitates clinical implementation and increases sample throughput and quantification precision. Providing a rapid assessment of serum or plasma samples at scale, we report 27 biomarkers that distinguish mild and severe forms of COVID-19, of which some may have potential as therapeutic targets. These proteins highlight the role of complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling upstream and downstream of Interleukin 6. Application of novel methodologies hence transforms proteomics from a research tool into a rapid-response, clinically actionable technology adaptable to infectious outbreaks. 2020-05-03
Frederick K Ho; Carlos A Celis-Morales; Stuart R Gray; Srinivasa Vittal Katikireddi; Claire L Niedzwiedz; Claire Hastie; Donald M. Lyall; Lyn D. Ferguson; Colin Berry; Daniel F. Mackay; Jason M.R. Gill; Jill P. Pell; Naveed Sattar; Paul I Welsh Modifiable and non-modifiable risk factors for COVID-19: results from UK Biobank Background Information on risk factors for COVID-19 is sub-optimal. We investigated demographic, lifestyle, socioeconomic, and clinical risk factors, and compared them to risk factors for pneumonia and influenza in UK Biobank. Methods UK Biobank recruited 37-70 year olds in 2006-2010 from the general population. The outcome of confirmed COVID-19 infection (positive SARS-CoV-2 test) was linked to baseline UK Biobank data. Incident influenza and pneumonia were obtained from primary care data. Poisson regression was used to study the association of exposure variables with outcomes. Findings Among 428,225 participants, 340 had confirmed COVID-19. After multivariable adjustment, modifiable risk factors were higher body mass index (RR 1.24 per SD increase), smoking (RR 1.38), slow walking pace as a proxy for physical fitness (RR 1.66) and use of blood pressure medications as a proxy for hypertension (RR 1.40). Non-modifiable risk factors included older age (RR 1.10 per 5 years), male sex (RR 1.64), black ethnicity (RR 1.86), socioeconomic deprivation (RR 1.26 per SD increase in Townsend Index), longstanding illness (RR 1.38) and high cystatin C (RR 1.24 per 1 SD increase). The risk factors overlapped with pneumonia somewhat; less so for influenza. The associations with modifiable risk factors were generally stronger for COVID-19, than pneumonia or influenza. Interpretation These findings suggest that modification of lifestyle may help to reduce the risk of COVID-19 and could be a useful adjunct to other interventions, such as social distancing and shielding of high risk. Funding British Heart Foundation, Medical Research Council, Chief Scientist Office. 2020-05-02
Mary Ni Lochlainn; Karla A Lee; Carole H Sudre; Thomas Varsavsky; M. Jorge Cardoso; Cristina Menni; Ruth C. E. Bowyer; Long H. Nguyen; David Alden Drew; Sajaysurya Ganesh; Julien Lavigne du Cadet; Alessia Visconti; Maxim B Freydin; Marc Modat; Mark S Graham; Joan Capdevila Pujol; Benjamin Murray; Julia S El-Sayed Moustafa; Xinyuan Zhang; Richard Davies; Mario Falchi; Timothy D Spector; Andrew T Chan; Sebastien Ourselin; Claire J Steves Key predictors of attending hospital with COVID19: An association study from the COVID Symptom Tracker App in 2,618,948 individuals 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 2020-04-29
Jody Phelan; Wouter Deelder; Daniel Ward; Susana Campino; Martin L Hibberd; Taane G Clark Controlling the SARS-CoV-2 outbreak, insights from large scale whole genome sequences generated across the world BackgroundSARS-CoV-2 most likely evolved from a bat beta-coronavirus and started infecting humans in December 2019. Since then it has rapidly infected people around the world, with more than 4.5 million confirmed cases by the middle of May 2020. Early genome sequencing of the virus has enabled the development of molecular diagnostics and the commencement of therapy and vaccine development. The analysis of the early sequences showed relatively few evolutionary selection pressures. However, with the rapid worldwide expansion into diverse human populations, significant genetic variations are becoming increasingly likely. The current limitations on social movement between countries also offers the opportunity for these viral variants to become distinct strains with potential implications for diagnostics, therapies and vaccines. MethodsWe used the current sequencing archives (NCBI and GISAID) to investigate 15,487 whole genomes, looking for evidence of strain diversification and selective pressure. ResultsWe used 6,294 SNPs to build a phylogenetic tree of SARS-CoV-2 diversity and noted strong evidence for the existence of two major clades and six sub-clades, unevenly distributed across the world. We also noted that convergent evolution has potentially occurred across several locations in the genome, showing selection pressures, including on the spike glycoprotein where we noted a potentially critical mutation that could affect its binding to the ACE2 receptor. We also report on mutations that could prevent current molecular diagnostics from detecting some of the sub-clades. ConclusionsThe worldwide whole genome sequencing effort is revealing the challenge of developing SARS-CoV-2 containment tools suitable for everyone and the need for data to be continually evaluated to ensure accuracy in outbreak estimations. 2020-04-29
Ewan Carr; Rebecca Bendayan; Daniel Bean; Kevin O'Gallagher; Andrew Pickles; Daniel Stahl; Rosita Zakeri; Thomas Searle; Anthony Shek; Zeljko Kraljevic; James T Teo; Ajay Shah; Richard Dobson Supplementing the National Early Warning Score (NEWS2) for anticipating early deterioration among patients with COVID-19 infection Importance: An early minimally symptomatic phase is often followed by deterioration in patients with COVID-19 infection. This study shows that the addition of age and a minimal set of common blood tests taken in patients on admission to hospital significantly improves the National Early Warning Score (NEWS2) for risk-stratification of severe COVID disease. Objective: To supplement the NEWS2 score with a small number of easily obtained additional demographic, physiological and blood variables indicative of severity of COVID-19 infection. Design: Retrospective observational cohort with internal and temporal held-out external validation. Setting: Acute secondary care. Participants: 708 patients admitted to an acute multi-site UK NHS hospital with confirmed COVID-19 disease from 1st March to 5th April 2020. Intervention: Not applicable. Main outcome and measures: The primary outcome was patient status at 14 days after symptom onset categorised as severe disease (WHO-COVID-19 Outcomes Scales 6-8: i.e. transferred to intensive care unit or death). 218 of the 708 patients reached the primary end point. A range of physiological and blood biomarkers as well age, gender, ethnicity and comorbidities (hypertension, diabetes, heart, respiratory and kidney diseases) were assessed for their association with the primary outcome. Results: NEWS2 total score on admission was a weak predictor for severity of COVID-19 infection at 14 days (internally validated AUC = 0.628). The addition of age and common blood tests (CRP, neutrophil count, estimated GFR and albumin) provided substantial improvements to a risk stratification model but performance was still only moderate (AUC = 0.75). Common comorbidities hypertension, diabetes, heart, respiratory and kidney diseases have minor additional predictive value. Conclusions and relevance: Adding age and a minimal set of common blood parameters to NEWS2 improves the risk stratification of patients likely to develop severe COVID-19 outcomes. The addition of a few common parameters is likely to be much easier to implement in a short time-scale than a novel risk-scoring system. 2020-04-29
Claire L Niedzwiedz; Bhautesh D Jani; Evangelia Demou; Frederick K Ho; Carlos Celis-Morales; Barbara I Nicholl; Frances Mair; Paul Welsh; Naveed Sattar; Jill Pell; Srinivasa Vittal Katikireddi Ethnic and socioeconomic differences in SARS-CoV2 infection in the UK Biobank cohort study Background Understanding of the role of ethnicity and socioeconomic position in the risk of developing SARS-CoV-2 infection is limited. We investigated this in the UK Biobank study. Methods The UK Biobank study recruited 40-70 year olds in 2006-2010 from the general population, collecting information about self-defined ethnicity and socioeconomic variables (including area-level socioeconomic deprivation and educational attainment). SARS-CoV-2 test results from Public Health England were linked to baseline UK Biobank data. Poisson regression with robust standard errors was used to assess risk ratios (RRs) between the exposures and dichotomous variables for: being tested, having a positive test and testing positive in hospital. We also investigated whether ethnicity and socioeconomic position were associated with having a positive test amongst those tested. We adjusted for covariates including age, sex, social variables (including healthcare work and household size), behavioural risk factors and baseline health. Results Among 428,225 participants in England, 1,474 had been tested and 669 tested positive between 16 March and 13 April 2020. Black, south Asian and white Irish people were more likely to have confirmed infection (RR 4.01 (95%CI 2.92-5.12); RR 2.11 (95%CI 1.43-3.10); and RR 1.60 (95% CI 1.08-2.38) respectively) and were more likely to be hospital cases compared to the White British. While they were more likely to be tested, they were also more likely to test positive. Adjustment for baseline health and behavioural risk factors led to little change, with only modest attenuation when accounting for socioeconomic variables. Socioeconomic deprivation and having no qualifications were consistently associated with a higher risk of confirmed infection (RR 2.26 (95%CI 1.76-2.90); and RR 1.91 (95%CI 1.53-2.38) respectively). Conclusions Some minority ethnic groups have a higher risk of confirmed SARS-CoV-2 infection in the UK Biobank study which was not accounted for by differences in socioeconomic conditions, measured baseline health or behavioural risk factors. An urgent response to addressing these elevated risks is required. 2020-04-27
Ruth Bowyer; Thomas Varsavsky; Carole H Sudre; Benjamin Murray; Maxim Freidin; Darioush Yarand; Sajaysurya Ganesh; Joan Capdevila; Ellen J Thompson; Elco Bakker; M Jorge Cardoso; Richard Davies; Jonathan Wolf; Tim D Spector; Sebastien Ourselin; Claire J Steves; Cristina Menni 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 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. 2020-04-27
Frances MK Williams; Maxim Freydin; Massimo Mangino; Simon Couvreur; Alessia Visconti; Ruth CE Bowyer; Caroline I Le Roy; Mario Falchi; Carole Sudre; Richard Davies; Christopher Hammond; Cristina Menni; Claire Steves; Tim Spector Self-reported symptoms of covid-19 including symptoms most predictive of SARS-CoV-2 infection, are heritable 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)%. 2020-04-24
David A Leon; Christopher I Jarvis; Anne M Johnson; Liam Smeeth; Vladimir M Shkolnikov What can trends in hospital deaths from COVID-19 tell us about the progress and peak of the pandemic? An analysis of death counts from England announced up to 20 April 2020 Background. Reporting of daily hospital COVID-19 deaths in the UK are promoted by the government and scientific advisers alike as a key metric for assessing the progress in the control of the epidemic. These data, however, have certain limitations, among which one of the most significant concerns the fact that the daily totals span deaths that have occurred between 1 and 10 days or more in the past. Data and methods. We obtained daily data published published by NHS England up to and including April 25 in the form of Excel spreadsheets in which deaths counts are presented by date of death according to age and region. Simple descriptive analyses were conducted and presented in graphical and tabular form which were aimed at illustrating the biases inherent in focussing on daily counts regardless of when the deaths occurred. We then looked at how a less biased picture could be obtained by looking at trends in death counts stratifying by individual period of delay in days between occurrence of death and when the death was included in the daily announcement. Findings. The number of hospital COVID-19 deaths announced daily overestimates the maximum number of deaths actually occurring so far in the epidemic in the UK, and also obscures the pattern of decline in deaths. Taking account of reporting delays suggests that for England as a whole a peak in hospital COVID-19 deaths may have been reached on April 8 with a subsequent gradual decline suggested. The same peak is also seen among those aged 60-79 and 80+, although there is slightly shallower decline in the oldest age group (80+ years). Among those aged 40-59 years a later peak on April 11 is evident. London shows a peak on April 8 and a clearer and steeper pattern of subsequent decline compared to England as a whole. Interpretation. Analyses of mortality trends must take account of delay, and in communication with the public more emphasis should be placed on looking at trends based on deaths that occurred 5 or more days prior to the announcement day. The slightly weaker decline seen at age 80+ may reflect increased hospitalisation of people from care homes, whereas the later peak under the age of 60 years may reflect the higher proportions at these younger ages being admitted to critical care resulting in an extension of life of several days. 2020-04-24
Yue-qiang Fu; Yue-lin Sun; Si-wei Lu; Yang Yang; Yi Wang; Feng Xu Impact of blood analysis and immune function on the prognosis of patients with COVID-19 Introduction: This retrospective study investigated the implications of changes in blood parameters and cellular immune function in patients with 2019-coronavirus infected disease (COVID-19). Methods: Records were reviewed of 85 patients with COVID-19 between February 4 and 16, 2020. The primary outcome was in-hospital mortality at 28 days. Results: Fourteen patients died. The baseline leukocyte count, neutrophil count and hemoglobin was significantly higher in non-survivors compared with survivors, while the reverse was true of lymphocyte count, platelet, PaO2/FiO2, CD3+ count and CD4+ count. The percentage of neutrophil count > 6.3*109/L in death group was significantly higher than that in survival group, and multivariate logistic regression showed neutrophil count was independently associated with mortality. However, there were not significant difference in IgG, IgM, IgA, C3, C4 and the percentage of IgE > 100 IU/ml between the death group and survival group. Areas under the receiver operating characteristic curves of the following at baseline could significantly predict mortality: leukocyte, neutrophil, lymphocyte, CD3+ and CD4+ counts. Conclusions: For patients with COVID-19, lymphocyte, CD3+ and CD4+ counts that marked decrease suggest a poor outcome. A high neutrophil count is independently associated with mortality. At admission, leukocyte, neutrophil, lymphocyte, CD3+ and CD4+ counts should receive added attention. 2020-04-22
Andrew Clark; Mark Jit; Charlotte Warren-Gash; Bruce Guthrie; Harry HX Wang; Stewart W Mercer; Colin Sanderson; Martin McKee; Christopher Troeger; Kanyin I Ong; Francesco Checchi; Pablo Perel; Sarah Joseph; Hamish P Gibbs; Amitava Banerjee; LSHTM CMMID COVID-19 working group; Rosalind M Eggo How many are at increased risk of severe COVID-19 disease? Rapid global, regional and national estimates for 2020 Background The risk of severe COVID-19 disease is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 illness, and how this varies between countries may inform the design of possible strategies to shield those at highest risk. Methods We estimated the number of individuals at increased risk of severe COVID-19 disease by age (5-year age groups), sex and country (n=188) based on prevalence data from the Global Burden of Disease (GBD) study for 2017 and United Nations population estimates for 2020. We also calculated the number of individuals without an underlying condition that could be considered at-risk because of their age, using thresholds from 50-70 years. The list of underlying conditions relevant to COVID-19 disease was determined by mapping conditions listed in GBD to the guidelines published by WHO and public health agencies in the UK and US. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. Results We estimate that 1.7 (1.0 - 2.4) billion individuals (22% [15-28%] of the global population) are at increased risk of severe COVID-19 disease. The share of the population at increased risk ranges from 16% in Africa to 31% in Europe. Chronic kidney disease (CKD), cardiovascular disease (CVD), diabetes and chronic respiratory disease (CRD) were the most prevalent conditions in males and females aged 50+ years. African countries with a high prevalence of HIV/AIDS and Island countries with a high prevalence of diabetes, also had a high share of the population at increased risk. The prevalence of multimorbidity (>1 underlying conditions) was three times higher in Europe than in Africa (10% vs 3%). Conclusion Based on current guidelines and prevalence data from GBD, we estimate that one in five individuals worldwide has a condition that is on the list of those at increased risk of severe COVID-19 disease. However, for many of these individuals the underlying condition will be undiagnosed or not severe enough to be captured in health systems, and in some cases the increase in risk may be quite modest. There is an urgent need for robust analyses of the risks associated with different underlying conditions so that countries can identify the highest risk groups and develop targeted shielding policies to mitigate the effects of the COVID-19 pandemic. 2020-04-22
Billy J Quilty; Charlie Diamond; Yang Liu; Hamish Gibbs; Timothy W Russell; Christopher I Jarvis; Kiesha Prem; Carl A B Pearson; Samuel J Clifford; Stefan Flasche; CMMID COVID-19 working group; Petra Klepac; Rosalind M Eggo; Mark Jit The effect of inter-city travel restrictions on geographical spread of COVID-19: Evidence from Wuhan, China Background: To contain the spread of COVID-19, a cordon sanitaire was put in place in Wuhan prior to the Lunar New Year, on 23 January 2020, restricting travel to other parts of China. We assess the efficacy of the cordon sanitaire to delay the introduction and onset of local transmission of COVID-19 in other major cities in mainland China. Methods: We estimated the number of infected travellers from Wuhan to other major cities in mainland China from November 2019 to March 2020 using previously estimated COVID-19 prevalence in Wuhan and publicly available mobility data. We focused on Beijing, Chongqing, Hangzhou, and Shenzhen as four representative major cities to identify the potential independent contribution of the cordon sanitaire and holiday travel. To do this, we simulated outbreaks generated by infected arrivals in these destination cities using stochastic branching processes. We also modelled the effect of the cordon sanitaire in combination with reduced transmissibility scenarios representing the effect of local non-pharmaceutical interventions. Findings: In the four cities, given the potentially high prevalence of COVID-19 in Wuhan between Dec 2019 and early Jan 2020, local transmission may have been seeded as early as 2 - 8 January 2020. By the time the cordon sanitaire was imposed, simulated case counts were likely in the hundreds. The cordon sanitaire alone did not substantially affect the epidemic progression in these cities, although it may have had some effect in smaller cities. Interpretation: Our results indicate that the cordon sanitaire may not have prevented COVID-19 spread in major Chinese cities; local non-pharmaceutical interventions were likely more important for this. 2020-04-21
Amitava Banerjee; Michail Katsoulis; Alvina G Lai; Laura Pasea; Thomas A Treibel; Charlotte Manisty; Spiros Denaxas; Giovanni Quarta; Harry Hemingway; Joao Cavalcante; Mahdad Nousardeghi; James C Moon Clinical academic research in the time of Corona: a simulation study in England and a call for action Background: Coronavirus (COVID-19) poses health system challenges in every country. As with any public health emergency, a major component of the global response is timely, effective science. However, particular factors specific to COVID-19 must be overcome to ensure that research efforts are optimised. We aimed to model the impact of COVID-19 on the clinical academic response in the UK, and to provide recommendations for COVID-related research. Methods: We constructed a simple stochastic model to determine clinical academic capacity in the UK in four policy approaches to COVID-19 with differing population infection rates: Italy model (6%), mitigation (10%), relaxed mitigation (40%) and do-nothing (80%) scenarios. The ability to conduct research in the COVID-19 climate is affected by the following key factors: (i) infection growth rate and population infection rate (from UK COVID-19 statistics and WHO); (ii) strain on the healthcare system (from published model); and (iii) availability of clinical academic staff with appropriate skillsets affected by frontline clinical activity and sickness (from UK statistics). Findings: In Italy model, mitigation, relaxed mitigation and do-nothing scenarios, from 5 March 2020 the duration (days) and peak infection rates (%) are 95(2.4%), 115(2.5%), 240(5.3%) and 240(16.7%) respectively. Near complete attrition of academia (87% reduction, less than 400 clinical academics) occurs 35 days after pandemic start for 11, 34, 62, 76 days respectively, with no clinical academics at all for 37 days in the do-nothing scenario. Restoration of normal academic workforce (80% of normal capacity) takes 11,12, 30 and 26 weeks respectively. Interpretation: Pandemic COVID-19 crushes the science needed at system level. National policies mitigate, but the academic community needs to adapt. We highlight six key strategies: radical prioritisation (eg 3-4 research ideas per institution), deep resourcing, non-standard leadership (repurposing of key non-frontline teams), rationalisation (profoundly simple approaches), careful site selection (eg protected sites with large academic backup) and complete suspension of academic competition with collaborative approaches. 2020-04-17
Xueyan Mei; Hao-Chih Lee; Kaiyue Diao; Mingqian Huang; Bin Lin; Chenyu Liu; Zongyu Xie; Yixuan Ma; Philip M. Robson; Michael Chung; Adam Bernheim; Venkatesh Mani; Claudia Calcagno; Kunwei Li; Shaolin Li; Hong Shan; Jian Lv; Tongtong Zhao; Junli Xia; Qihua Long; Sharon Steinberger; Adam Jacobi; Timothy Deyer; Marta Luksza; Fang Liu; Brent P. Little; Zahi A. Fayad; Yang Yang Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19) For diagnosis of COVID-19, a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to two days to complete, serial testing may be required to rule out the possibility of false negative results, and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of COVID-19 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiologic findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and laboratory testing to rapidly diagnose COVID-19 positive patients. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an AUC of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of RT-PCR positive COVID-19 patients who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients. 2020-04-17
Qin-Long Jing; Ming-Jin Liu; Jun Yuan; Zhou-Bin Zhang; An-Ran Zhang; Natalie E Dean; Lei Luo; Meng-Meng Ma; Ira Longini; Eben Kenah; Ying Lu; Yu Ma; Neda Jalali; Li-Qun Fang; Zhi-Cong Yang; Yang Yang Household Secondary Attack Rate of COVID-19 and Associated Determinants Background: As of April 2, 2020, the global reported number of COVID-19 cases has crossed over 1 million with more than 55,000 deaths. The household transmissibility of SARS-CoV-2, the causative pathogen, remains elusive. Methods: Based on a comprehensive contact-tracing dataset from Guangzhou, we estimated both the population-level effective reproductive number and individual-level secondary attack rate (SAR) in the household setting. We assessed age effects on transmissibility and the infectivity of COVID-19 cases during their incubation period. Results: A total of 195 unrelated clusters with 212 primary cases, 137 nonprimary (secondary or tertiary) cases and 1938 uninfected close contacts were traced. We estimated the household SAR to be 13.8% (95% CI: 11.1-17.0%) if household contacts are defined as all close relatives and 19.3% (95% CI: 15.5-23.9%) if household contacts only include those at the same residential address as the cases, assuming a mean incubation period of 4 days and a maximum infectious period of 13 days. The odds of infection among children (<20 years old) was only 0.26 (95% CI: 0.13-0.54) times of that among the elderly ([&ge;]60 years old). There was no gender difference in the risk of infection. COVID-19 cases were at least as infectious during their incubation period as during their illness. On average, a COVID-19 case infected 0.48 (95% CI: 0.39-0.58) close contacts. Had isolation not been implemented, this number increases to 0.62 (95% CI: 0.51-0.75). The effective reproductive number in Guangzhou dropped from above 1 to below 0.5 in about 1 week. Conclusion: SARS-CoV-2 is more transmissible in households than SARS-CoV and MERS-CoV, and the elderly [&ge;]60 years old are the most vulnerable to household transmission. Case finding and isolation alone may be inadequate to contain the pandemic and need to be used in conjunction with heightened restriction of human movement as implemented in Guangzhou. 2020-04-15
Samuel P C Brand; Rabia Aziza; Ivy K Kombe; Charles N Agoti; Joe Hilton; Kat S Rock; Andrea Parisi; D James Nokes; Matt Keeling; Edwine Barasa Forecasting the scale of the COVID-19 epidemic in Kenya Background The first COVID-19 case in Kenya was confirmed on March 13th, 2020. Here, we provide forecasts for the potential incidence rate, and magnitude, of a COVID-19 epidemic in Kenya based on the observed growth rate and age distribution of confirmed COVID-19 cases observed in China, whilst accounting for the demographic and geographic dissimilarities between China and Kenya. Methods We developed a modelling framework to simulate SARS-CoV-2 transmission in Kenya, KenyaCoV. KenyaCoV was used to simulate SARS-CoV-2 transmission both within, and between, different Kenyan regions and age groups. KenyaCoV was parameterized using a combination of human mobility data between the defined regions, the recent 2019 Kenyan census, and estimates of age group social interaction rates specific to Kenya. Key epidemiological characteristics such as the basic reproductive number and the age-specific rate of developing COVID-19 symptoms after infection with SARS-CoV-2, were adapted for the Kenyan setting from a combination of published estimates and analysis of the age distribution of cases observed in the Chinese outbreak. Results We find that if person-to-person transmission becomes established within Kenya, identifying the role of subclinical, and therefore largely undetected, infected individuals is critical to predicting and containing a very significant epidemic. Depending on the transmission scenario our reproductive number estimates for Kenya range from 1.78 (95% CI 1.44 - 2.14) to 3.46 (95% CI 2.81-4.17). In scenarios where asymptomatic infected individuals are transmitting significantly, we expect a rapidly growing epidemic which cannot be contained only by case isolation. In these scenarios, there is potential for a very high percentage of the population becoming infected (median estimates: >80% over six months), and a significant epidemic of symptomatic COVID-19 cases. Exceptional social distancing measures can slow transmission, flattening the epidemic curve, but the risk of epidemic rebound after lifting restrictions is predicted to be high. 2020-04-14
Dipender Gill; Marios Arvanitis; Paul Carter; Ana I Hernandez Cordero; Brian Jo; Ville Karhunen; Susanna C Larsson; Xuan Li; Sam M Lockhart; Amy M Mason; Evanthia Pashos; Ashis Saha; Vanessa Tan; Verena Zuber; Yohan Bosse; Sarah Fahle; Ke Hao; Tao Jiang; Philippe Joubert; Alan C Lunt; Willem hendrik Ouwehand; David J Roberts; Wim Timens; Maarten van den Berge; Nicholas A Watkins; Alexis Battle; Adam S Butterworth; John Danesh; Barbara E Engelhard; James E Peters; Don Sin; Stephen Burgess ACE inhibition and cardiometabolic risk factors, lung ACE2 and TMPRSS2 gene expression, and plasma ACE2 levels: a Mendelian randomization study Objectives: To use human genetic variants that proxy angiotensin-converting enzyme (ACE) inhibitor drug effects and cardiovascular risk factors to provide insight into how these exposures affect lung ACE2 and TMPRSS2 gene expression and circulating ACE2 levels. Design: Two-sample Mendelian randomization (MR) analysis. Setting: Summary-level genetic association data. Participants: Participants were predominantly of European ancestry. Variants that proxy ACE inhibitor drug effects and cardiometabolic risk factors (body mass index, chronic obstructive pulmonary disease, lifetime smoking index, low-density lipoprotein cholesterol, systolic blood pressure and type 2 diabetes mellitus) were selected from publicly available genome-wide association study data (sample sizes ranging from 188,577 to 898,130 participants). Genetic association estimates for lung expression of ACE2 and TMPRSS2 were obtained from the Gene-Tissue Expression (GTEx) project (515 participants) and the Lung eQTL Consortium (1,038 participants). Genetic association estimates for circulating plasma ACE2 levels were obtained from the INTERVAL study (4,947 participants). Main outcomes and measures: Lung ACE2 and TMPRSS2 expression and plasma ACE2 levels. Results: There were no association of genetically proxied ACE inhibition with any of the outcomes considered here. There was evidence of a positive association of genetic liability to type 2 diabetes mellitus with lung ACE2 gene expression in GTEx (p = 4x10-4) and with circulating plasma ACE2 levels in INTERVAL (p = 0.03), but not with lung ACE2 expression in the Lung eQTL Consortium study (p = 0.68). There were no associations between genetically predicted levels of the other cardiometabolic traits with the outcomes. Conclusions: This study does not provide evidence to support that ACE inhibitor antihypertensive drugs affect lung ACE2 and TMPRSS2 expression or plasma ACE2 levels. In the current COVID-19 pandemic, our findings do not support a change in ACE inhibitor medication use without clinical justification. 2020-04-14
Daniel Bean; Zeljko Kraljevic; Thomas Searle; Rebecca Bendayan; Andrew Pickles; Amos Folarin; Lukasz Roguski; Kawsar Noor; Anthony Shek; Rosita Zakeri; Ajay Shah; James Teo; Richard JB Dobson Treatment with ACE-inhibitors is associated with less severe disease with SARS-Covid-19 infection in a multi-site UK acute Hospital Trust Aims: The SARS-Cov2 virus binds to the ACE2 receptor for cell entry. It has been suggested that ACE- inhibitors (ACEi) and Angiotensin-2 Blockers (ARB), which are commonly used in patients with hypertension or diabetes and may raise ACE2 levels, could increase the risk of severe COVID19 infection. Methods and Results: We evaluated this hypothesis in a consecutive cohort of 1200 acute inpatients with COVID19 at two hospitals with a multi-ethnic catchment population in London (UK). The mean age was 68+-17 years (57% male) and 74% of patients had at least 1 comorbidity. 415 patients (34.6%) reached the primary endpoint of death or transfer to a critical care unit for organ support within 21-days of symptom onset. 399 patients (33.3 %) were taking ACEi or ARB. Patients on ACEi/ARB were significantly older and had more comorbidities. The odds ratio (OR) for the primary endpoint in patients on ACEi and ARB, after adjustment for age, sex and co-morbidities, was 0.63 (CI 0.47-0.84, p<0.01). Conclusions: There was no evidence for increased severity of COVID19 disease in hospitalised patients on chronic treatment with ACEi or ARB. A trend towards a beneficial effect of ACEi/ARB requires further evaluation in larger meta-analyses and randomised clinical trials. 2020-04-11
Lilin Ye; Xiangyu Chen; Ren Li; Zhiwei Pan; Chunfeng Qian; Yang Yang; Renrong You; Jing Zhao; Leiqong Gao; Zhirong Li; Qizhao Huang; Lifan Xu; Jianfang Tang; Qin Tian; Wei Yao; Li Hu; Xiaofeng Yan; Xinyuan Zhou; Pinghuang Liu; Yuzhang Wu; Kai Deng; Zheng Zhang; Yaokai Chen; Zhaohui Qian Human monoclonal antibodies block the binding of SARS-CoV-2 spike protein to angiotensin converting enzyme 2 receptor The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of novel corona virus disease (COVID-19). To date, no prophylactic vaccines or approved therapeutic agents are available for preventing and treating this highly transmittable disease. Here we report two monoclonal antibodies (mAbs) cloned from memory B cells of patients recently recovered from COVID-19, and both mAbs specifically bind to the spike (S) protein of SARS-CoV-2, block the binding of receptor binding domain (RBD) of SARS-CoV-2 to human angiotensin converting enzyme 2 (hACE2), and effectively neutralize S protein-pseudotyped virus infection. These human mAbs hold the promise for the prevention and treatment of the ongoing pandemic of COVID-19. 2020-04-11
Jane Cheatley; Sabine Vuik; Marion Devaux; Stefano Scarpetta; Mark Pearson; Francesca Colombo; Michele Cecchini The effectiveness of non-pharmaceutical interventions in containing epidemics: a rapid review of the literature and quantitative assessment The number of confirmed COVID-19 cases has rapidly increased since discovery of the disease in December 2019. In the absence of medical countermeasures to stop the spread of the disease (i.e. vaccines), countries have responded by implementing a suite of non-pharmaceutical interventions (NPIS) to contain and mitigate COVID-19. Individual NPIs range in intensity (e.g. from lockdown to public health campaigns on personal hygiene), as does their impact on reducing disease transmission. This study uses a rapid review approach and investigates evidence from previous epidemic outbreaks to provide a quantitative assessment of the effectiveness of key NPIs used by countries to combat the COVID-19 pandemic. Results from the study are designed to help countries enhance their policy response as well as inform transition strategies by identifying which policies should be relaxed and which should not. 2020-04-10
Kathryn Tuttle; Ross Minter; Katherine Waugh; Paula Araya; Michael Ludwig; Colin Sempeck; Keith Smith; Zdenek Andrysik; Matthew Burchill; Beth Tamburini; David Orlicky; Kelly D Sullivan; Joaquin Espinosa JAK1 inhibition blocks lethal sterile immune responses:implications for COVID-19 therapy Cytokine storms are drivers of pathology and mortality in myriad viral infections affecting the human population. In SARS-CoV-2-infected patients, the strength of the cytokine storm has been associated with increased risk of acute respiratory distress syndrome, myocardial damage, and death. However, the therapeutic value of attenuating the cytokine storm in COVID-19 remains to be defined. Here, we report results obtained using a novel mouse model of lethal sterile anti-viral immune responses. Using a mouse model of Down syndrome (DS) with a segmental duplication of a genomic region encoding four of the six interferon receptor genes (Ifnrs), we demonstrate that these animals overexpress Ifnrs and are hypersensitive to IFN stimulation. When challenged with viral mimetics that activate Toll-like receptor signaling and IFN anti-viral responses, these animals overproduce key cytokines, show exacerbated liver pathology, rapidly lose weight, and die. Importantly, the lethal immune hypersensitivity, accompanying cytokine storm, and liver hyperinflammation are blocked by treatment with a JAK1-specific inhibitor. Therefore, these results point to JAK1 inhibition as a potential strategy for attenuating the cytokine storm and consequent organ failure during overdrive immune responses. Additionally, these results indicate that people with DS, who carry an extra copy of the IFNR gene cluster encoded on chromosome 21, should be considered at high risk during the COVID-19 pandemic. One Sentence SummaryInhibition of the JAK1 kinase prevents pathology and mortality caused by a rampant innate immune response in mice. 2020-04-09
Cristina Menni; Ana Valdes; Maxim B Freydin; Sajaysurya Ganesh; Julia El-Sayed Moustafa; Alessia Visconti; Pirro Hysi; Ruth C E Bowyer; Massimo Mangino; Mario Falchi; Jonathan Wolf; Claire Steves; Tim Spector Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection Importance: A strategy for preventing further spread of the ongoing COVID-19 epidemic is to detect infections and isolate infected individuals without the need of extensive bio-specimen testing. Objectives: Here we investigate the prevalence of loss of smell and taste among COVID-19 diagnosed individuals and we identify the combination of symptoms, besides loss of smell and taste, most likely to correspond to a positive COVID-19 diagnosis in non-severe cases. Design: Community survey. Setting and Participants: Subscribers of RADAR COVID-19, an app that was launched for use among the UK general population asking about COVID-19 symptoms. Main Exposure: Loss of smell and taste. Main Outcome Measures: COVID-19. Results: Between 24 and 29 March 2020, 1,573,103 individuals reported their symptoms via the app; 26% reported suffering from one or more symptoms of COVID-19. Of those, n=1702 reported having had a RT-PCR COVID-19 test and gave full report on symptoms including loss of smell and taste; 579 were positive and 1123 negative. In this subset, we find that loss of smell and taste were present in 59% of COVID-19 positive individuals compared to 18% of those negative to the test, yielding an odds ratio (OR) of COVID-19 diagnosis of OR[95%CI]=6.59[5.25; 8.27], P= 1.90x10-59 . We also find that a combination of loss of smell and taste, fever, persistent cough, fatigue, diarrhoea, abdominal pain and loss of appetite is predictive of COVID-19 positive test with sensitivity 0.54[0.44; 0.63], specificity 0.86[0.80; 0.90], ROC-AUC 0.77[0.72; 0.82] in the test set, and cross-validation ROC-AUC 0.75[0.72; 0.77]. When applied to the 410,598 individuals reporting symptoms but not formally tested, our model predicted that 13.06%[12.97%;13.15] of these might have been already infected by the virus. Conclusions and Relevance: Our study suggests that loss of taste and smell is a strong predictor of having been infected by the COVID-19 virus. Also, the combination of symptoms that could be used to identify and isolate individuals includes anosmia, fever, persistent cough, diarrhoea, fatigue, abdominal pain and loss of appetite. This is particularly relevant to healthcare and other key workers in constant contact with the public who have not yet been tested for COVID-19. 2020-04-07
Eran Segal; Feng Zhang; Xihong Lin; Gary King; Ophir Shalem; Smadar Shilo; William E. Allen; Yonatan H. Grad; Casey S. Greene; Faisal Alquaddoomi; Simon Anders; Ran Balicer; Tal Bauman; Ximena Bonilla; Gisel Booman; Andrew T. Chan; Ori Ori Cohen; Silvano Coletti; Natalie Davidson; Yuval Dor; David A. Drew; Olivier Elemento; Georgina Evans; Phil Ewels; Joshua Gale; Amir Gavrieli; Benjamin Geiger; Iman Hajirasouliha; Roman Jerala; Andre Kahles; Olli Kallioniemi; Ayya Keshet; Gregory Landua; Tomer Meir; Aline Muller; Long H. Nguyen; Matej Oresic; Svetlana Ovchinnikova; Hedi Peterson; Jay Rajagopal; Gunnar Ratsch; Hagai Rossman; Johan Rung; Andrea Sboner; Alexandros Sigaras; Tim Spector; Ron Steinherz; Irene Stevens; Jaak Vilo; Paul Wilmes; CCC (Coronavirus Census Collective) Building an International Consortium for Tracking Coronavirus Health Status Information is the most potent protective weapon we have to combat a pandemic, at both the individual and global level. For individuals, information can help us make personal decisions and provide a sense of security. For the global community, information can inform policy decisions and offer critical insights into the epidemic of COVID-19 disease. Fully leveraging the power of information, however, requires large amounts of data and access to it. To achieve this, we are making steps to form an international consortium, Coronavirus Census Collective (CCC, coronaviruscensuscollective.org), that will serve as a hub for integrating information from multiple data sources that can be utilized to understand, monitor, predict, and combat global pandemics. These sources may include self-reported health status through surveys (including mobile apps), results of diagnostic laboratory tests, and other static and real-time geospatial data. This collective effort to track and share information will be invaluable in predicting hotspots of disease outbreak, identifying which factors control the rate of spreading, informing immediate policy decisions, evaluating the effectiveness of measures taken by health organizations on pandemic control, and providing critical insight on the etiology of COVID-19. It will also help individuals stay informed on this rapidly evolving situation and contribute to other global efforts to slow the spread of disease. In the past few weeks, several initiatives across the globe have surfaced to use daily self-reported symptoms as a means to track disease spread, predict outbreak locations, guide population measures and help in the allocation of healthcare resources. The aim of this paper is to put out a call to standardize these efforts and spark a collaborative effort to maximize the global gain while protecting participant privacy. 2020-04-06
David A. Drew; Long H. Nguyen; Claire J. Steves; Jonathan Wolf; Tim D. Spector; Andrew T. Chan; COPE Consortium Rapid implementation of mobile technology for real-time epidemiology of COVID-19 The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (COVID-19) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) consortium to bring together scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application that we launched in the UK on March 24, 2020 and the US on March 29, 2020 garnering more than 2.25 million users to date. This mobile application offers data on risk factors, herald symptoms, clinical outcomes, and geographical hot spots. This initiative offers critical proof-of-concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis which is critical for a data-driven response to this public health challenge. 2020-04-06
Nicholas G Davies; Adam J Kucharski; Rosalind M Eggo; Amy Gimma; - CMMID COVID-19 Working Group; W. John Edmunds The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study Background Non-pharmaceutical interventions have been implemented to reduce transmission of SARS-CoV-2 in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been critical to support evidence-based policymaking during the early stages of the epidemic. Methods We used a stochastic age-structured transmission model to explore a range of intervention scenarios, including the introduction of school closures, social distancing, shielding of elderly groups, self-isolation of symptomatic cases, and extreme "lockdown"-type restrictions. We simulated different durations of interventions and triggers for introduction, as well as combinations of interventions. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (intensive care unit, ICU) treatment, and deaths. Findings We found that mitigation measures aimed at reducing transmission would likely have decreased the reproduction number, but not sufficiently to prevent ICU demand from exceeding NHS availability. To keep ICU bed demand below capacity in the model, more extreme restrictions were necessary. In a scenario where "lockdown"-type interventions were put in place to reduce transmission, these interventions would need to be in place for a large proportion of the coming year in order to prevent healthcare demand exceeding availability. Interpretation The characteristics of SARS-CoV-2 mean that extreme measures are likely required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs. 2020-04-06
Marc F Osterdahl; Karla A Lee; Mary Ni Lochlainn; Stuart Wilson; Sam Douthwaite; Rachel Horsfall; Alyce Sheedy; Simon D Goldenberg; Christoper J Stanley; Tim D Spector; Claire Steves Detecting SARS-CoV-2 at point of care: Preliminary data comparing Loop-mediated isothermal amplification (LAMP) to PCR Background: The need for a fast and reliable test for COVID-19 is paramount in managing the current pandemic. A cost effective and efficient diagnostic tool as near to the point of care (PoC) as possible would be a game changer in current testing. We tested reverse transcription loop mediated isothermal amplification (RT-LAMP), a method which can produce results in under 30 minutes, alongside standard methods in a real-life clinical setting. Methods: This service improvement project piloted a research RT-LAMP method on nasal and pharyngeal swabs on 21 residents in an NHS Category 1 care home, with two index COVID-19 cases, and compared it to multiplex tandem reverse transcription polymerase chain reaction (RT-PCR). We calculated the sensitivity, specificity, positive and negative predictive values of a single RT-LAMP swab compared to RT-PCR, as per STARD guidelines. We also recorded vital signs of patients to correlate clinical and laboratory information. Findings: The novel method accurately detected 8/10 PCR positive cases and identified a further 3 positive cases. Eight further cases were negative using both methods. Using repeated RT-PCR as a 'gold standard', the sensitivity and specificity of the novel test were 80% and 73% respectively. Positive predictive value (PPV) was 73% and negative predictive value (NPV) was 83%. We also observed hypothermia to be a significant early clinical sign in a number of COVID-19 patients in this setting. Interpretation: RT-LAMP testing for SARS-CoV-2 was found to be promising, fast, easy to use and to work equivalently to RT-PCR methods. Definitive studies to evaluate this method in larger cohorts are underway. RT-LAMP has the potential to transform COVID-19 detection, bringing rapid and accurate testing to the point of care. This method could be deployed in mobile testing units in the community, care homes and hospitals to detect disease early and prevent spread. 2020-04-04
Andrew Francis; Yi Guo; Paul Hurley; Oliver Obst; Laurence A. F. Park; Mark Tanaka; Russell Thomson; Rosalind Wang Projected ICU and Mortuary load due to COVID-19 in Sydney The spread of COVID-19 is expected to put a large strain on many hospital resources, including ICU bed space, and mortuary capacity. In this report we study the possible demands on ICU and mortuary capacity in Sydney, Australia, using an adapted SEIR epidemiological model. 2020-04-03
Tom Jefferson; Mark Jones; Lubna A Al Ansari; Ghada Bawazeer; Elaine Beller; Justin Clark; John Conly; Chris Del Mar; Elisabeth Dooley; Eliana Ferroni; Paul Glasziou; Tammy Hoffman; Sarah Thorning; Mieke Van Driel Physical interventions to interrupt or reduce the spread of respiratory viruses. Part 1 - Face masks, eye protection and person distancing: systematic review and meta-analysis Abstract OBJECTIVE: To examine the effectiveness of eye protection, face masks, or person distancing on interrupting or reducing the spread of respiratory viruses. DESIGN: Update of a Cochrane review that included a meta-analysis of observational studies during the SARS outbreak of 2003. DATA SOURCES: Eligible trials from the previous review; search of Cochrane Central Register of Controlled Trials, PubMed, Embase and CINAHL from October 2010 up to 1 April 2020; and forward and backward citation analysis. DATA SELECTION: Randomised and cluster-randomised trials of people of any age, testing the use of eye protection, face masks, or person distancing against standard practice, or a similar physical barrier. Outcomes included any acute respiratory illness and its related consequences. DATA EXTRACTION AND ANALYSIS: Six authors independently assessed risk of bias using the Cochrane tool and extracted data. We used a generalised inverse variance method for pooling using a random-effects model and reported results with risk ratios and 95% Confidence Intervals (CI). RESULTS: We included 15 randomised trials investigating the effect of masks (14 trials) in healthcare workers and the general population and of quarantine (1 trial). We found no trials testing eye protection. Compared to no masks there was no reduction of influenza-like illness (ILI) cases (Risk Ratio 0.93, 95%CI 0.83 to 1.05) or influenza (Risk Ratio 0.84, 95%CI 0.61-1.17) for masks in the general population, nor in healthcare workers (Risk Ratio 0.37, 95%CI 0.05 to 2.50). There was no difference between surgical masks and N95 respirators: for ILI (Risk Ratio 0.83, 95%CI 0.63 to 1.08), for influenza (Risk Ratio 1.02, 95%CI 0.73 to 1.43). Harms were poorly reported and limited to discomfort with lower compliance. The only trial testing quarantining workers with household ILI contacts found a reduction in ILI cases, but increased risk of quarantined workers contracting influenza. All trials were conducted during seasonal ILI activity. CONCLUSIONS: Most included trials had poor design, reporting and sparse events. There was insufficient evidence to provide a recommendation on the use of facial barriers without other measures. We found insufficient evidence for a difference between surgical masks and N95 respirators and limited evidence to support effectiveness of quarantine. Based on observational evidence from the previous SARS epidemic included in the previous version of our Cochrane review we recommend the use of masks combined with other measures. 2020-03-30
Nicholas G Davies; Petra Klepac; Yang Liu; Kiesha Prem; Mark Jit; CMMID COVID-19 working group; Rosalind M Eggo Age-dependent effects in the transmission and control of COVID-19 epidemics The COVID-19 pandemic has shown a markedly low proportion of cases among children. Age disparities in observed cases could be explained by children having lower susceptibility to infection, lower propensity to show clinical symptoms, or both. We evaluate these possibilities by fitting an age-structured mathematical model to epidemic data from six countries. We estimate that clinical symptoms occur in 25% (95% CrI: 19-32%) of infections in 10-19-year-olds, rising to 76% (68-82%) in over-70s, and that susceptibility to infection in under-20s is approximately half that of older adults. Accordingly, we find that interventions aimed at children may have a relatively small impact on total cases, particularly if the transmissibility of subclinical infections is low. The age-specific clinical fraction and susceptibility we have estimated has implications for the expected global burden of COVID-19 because of demographic differences across settings: in younger populations, the expected clinical attack rate would be lower, although it is likely that comorbidities in low-income countries will affect disease severity. Without effective control measures, regions with older populations may see disproportionally more clinical cases, particularly in the later stages of the pandemic. 2020-03-27
HANMING FANG; LONG WANG; YANG YANG Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China We quantify the causal impact of human mobility restrictions, particularly the lockdown of the city of Wuhan on January 23, 2020, on the containment and delay of the spread of the Novel Coronavirus (2019-nCoV). We employ a set of difference-in-differences (DID) estimations to disentangle the lockdown effect on human mobility reductions from other confounding effects including panic effect, virus effect, and the Spring Festival effect. We find that the lockdown of Wuhan reduced inflow into Wuhan by 76.64%, outflows from Wuhan by 56.35%, and within-Wuhan movements by 54.15%. We also estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities, the epicenter of the 2019-nCoV outbreak, on the destination cities' new infection cases. We find, using simulations with these estimates, that the lockdown of the city of Wuhan on January 23, 2020 contributed significantly to reducing the total infection cases outside of Wuhan, even with the social distancing measures later imposed by other cities. We find that the COVID-19 cases would be 64.81% higher in the 347 Chinese cities outside Hubei province, and 52.64% higher in the 16 non-Wuhan cities inside Hubei, in the counterfactual world in which the city of Wuhan were not locked down from January 23, 2020. We also find that there were substantial undocumented infection cases in the early days of the 2019-nCoV outbreak in Wuhan and other cities of Hubei province, but over time, the gap between the officially reported cases and our estimated "actual" cases narrows significantly. We also find evidence that enhanced social distancing policies in the 63 Chinese cities outside Hubei province are effective in reducing the impact of population inflows from the epicenter cities in Hubei province on the spread of 2019-nCoV virus in the destination cities elsewhere. 2020-03-26
Amitava Banerjee; Laura Pasea; Steve Harris; Arturo Gonzalez-Izquierdo; Ana Torralbo; Laura Shallcross; Mahdad Noursadeghi; Deenan Pillay; Christina Pagel; Wai Keong Wong; Claudia Langenberg; Bryan Williams; Spiros Denaxas; Harry Hemingway Estimating excess 1- year mortality from COVID-19 according to underlying conditions and age in England: a rapid analysis using NHS health records in 3.8 million adults RAPID COMMUNICATION 22 March 2020 Estimating excess 1- year mortality from COVID-19 according to underlying conditions and age in England: a rapid analysis using NHS health records in 3.8 million adults Background: The medical, health service, societal and economic impact of the COVID-19 emergency has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom (to date at least) have underlying conditions. Models have not incorporated information on high risk conditions or their longer term background (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence rates and differing mortality impacts. Methods: Using population based linked primary and secondary care electronic health records in England (HDR UK - CALIBER), we report the prevalence of underlying conditions defined by UK Public Health England COVID-19 guidelines (16 March 2020) in 3,862,012 individuals aged [&ge;]30 years from 1997-2017. We used previously validated phenotypes, openly available (https://caliberresearch.org/portal), for each condition using ICD-10 diagnosis, Read, procedure and medication codes. We estimated the 1-year mortality in each condition, and developed simple models of excess COVID-19-related deaths assuming relative risk (RR) of the impact of the emergency (compared to background mortality) of 1.2, 1.5 and 2.0. Findings: 20.0% of the population are at risk according to current PHE guidelines, of which; 13.7% were age>70 years and 6.3% aged [&le;]70 years with [&ge;]1 underlying condition (cardiovascular disease (2.3%), diabetes (2.2%), steroid therapy (1.9%), severe obesity (0.9%), chronic kidney disease (0.6%) and chronic obstructive pulmonary disease, COPD (0.5%). Multimorbidity (co-occurrence of [&ge;]2 conditions in an individual) was common (10.1%). The 1-year mortality in the at-risk population was 4.46%, and age and underlying conditions combine to influence background risk, varying markedly across conditions (5.9% in age>70 years, 8.6% for COPD and 13.1% in those with [&ge;]3 or more conditions). In a suppression scenario (at SARS CoV2 rates of 0.001% of the UK population), there would be minimal excess deaths (3 and 7 excess deaths at relative risk, RR, 1.5 and 2.0 respectively). At SARS CoV2 rates of 10% of the UK population (mitigation) the model estimates the numbers of excess deaths as: 13791, 34479 and 68957 (at RR 1.2, 1.5 and 2.0 respectively). At SARS CoV2 rates of 80% in the UK population (do-nothing), the model estimates the number of excess deaths as 110332, 275,830 and 551,659 (at RR 1.2, 1.5 and 2.0) respectively. Interpretation: We provide the public, researchers and policy makers a simple model to estimate the excess mortality over 1 year from COVID-19, based on underlying conditions at different ages. If the relative mortality impact of COVID-19 were to be about 20% (similar magnitude as the established winter vs summer mortality excess), then the excess deaths would be 0 when 1 in 100 000 (suppression), 13791 when 1 in 10 (mitigation) and 110332 when 8 in 10 are infected (do nothing) scenario. However, the relative impact of COVID-19 is unknown. If the emergency were to double the mortality risk, then we estimate 7, 68957 and 551,659 excess deaths in the same scenarios. These results may inform the need for more stringent suppression measures as well as efforts to target those at highest risk for a range of preventive interventions. 2020-03-24
Ellen Brooks-Pollock; Jonathan M Read; Thomas House; Graham Medley; Matt J Keeling; Leon Danon The Population Attributable Fraction (PAF) of cases due to gatherings and groups with relevance to COVID-19 mitigation strategies Background Many countries have banned groups and gatherings as part of their response to the pandemic caused by the coronavirus, SARS-CoV-2. Although there are outbreak reports involving mass gatherings, the contribution to overall transmission is unknown. Methods We used data from a survey of social contact behaviour that specifically asked about contact with groups to estimate the Population Attributable Fraction (PAF) due to groups as the relative change in the Basic Reproduction Number when groups are prevented. Findings We estimate that PAF due to groups of 50+ people is 2.2% (95%CI 1.1%, 3.6%); the PAF due to groups of 20+ people is 6.4% (5.0%, 8.0%); the PAF due to groups of 10+ is 11.3% (9.9%, 13.0%) Interpretation Large groups of individuals have a small epidemiological impact; small and medium sized groups between 10 and 50 people have a larger impact on an epidemic. 2020-03-23
Thibaut Jombart; Kevin van Zandvoort; Tim Russell; Christopher Jarvis; Amy Gimma; Sam Abbott; Samuel Clifford; Sebastian Funk; Hamish Gibbs; Yang Liu; Carl Pearson; Nikos Bosse; Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group; Rosalind M Eggo; Adam J Kucharski; John Edmunds Inferring the number of COVID-19 cases from recently reported deaths We estimate the number of COVID-19 cases from newly reported deaths in a population without previous reports. Our results suggest that by the time a single death occurs, hundreds to thousands of cases are likely to be present in that population. This suggests containment via contact tracing will be challenging at this point, and other response strategies should be considered. Our approach is implemented in a publicly available, user-friendly, online tool. 2020-03-13
Kiesha Prem; Yang Liu; Tim Russell; Adam J Kucharski; Rosalind M Eggo; Nicholas Davies; Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group; Mark Jit; Petra Klepac The effect of control strategies that reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China BACKGROUND: In December 2019, a novel strain of SARS-CoV-2 emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures and efforts in response to the outbreak. METHODS: We quantified the effects of control measures on population contact patterns in Wuhan, China, to assess their effects on the progression of the outbreak. We included the latest estimates of epidemic parameters from a transmission model fitted to data on local and internationally exported cases from Wuhan in the age-structured epidemic framework. Further, we looked at the age-distribution of cases. Lastly, we simulated lifting of the control measures by allowing people to return to work in a phased-in way, and looked at the effects of returning to work at different stages of the underlying outbreak. FINDINGS: Changes in mixing patterns may have contributed to reducing the number of infections in mid-2020 by 92% (interquartile range: 66-97%). There are benefits to sustaining these measures until April in terms of reducing the height of the peak, overall epidemic size in mid-2020 and probability that a second peak may occur after return to work. However, the modelled effects of social distancing measures vary by the duration of infectiousness and the role school children play in the epidemic. INTERPRETATION: Restrictions on activities in Wuhan, if maintained until April, would likely contribute to the reduction and delay the epidemic size and peak, respectively. However, there are some limitations to the analysis, including large uncertainties around estimates of R0 and the duration of infectiousness. 2020-03-12
Timothy W Russell; Joel Hellewell; Christopher I Jarvis; Kevin van-Zandvoort; Sam Abbott; Ruwan Ratnayake; CMMID nCov working group; Stefan Flasche; Rosalind M Eggo; Adam J Kucharski Estimating the infection and case fatality ratio for COVID-19 using age-adjusted data from the outbreak on the Diamond Princess cruise ship Adjusting for delay from confirmation-to-death, we estimated case and infection fatality ratios (CFR, IFR) for COVID-19 on the Diamond Princess ship as 1.2% (0.38-2.7%) and 2.3% (0.75%-5.3%). Comparing deaths onboard with expected deaths based on naive CFR estimates using China data, we estimate IFR and CFR in China to be 0.5% (95% CI: 0.2-1.2%) and 1.1% (95% CI: 0.3-2.4%) respectively. 2020-03-08
Tao Suo; Xinjin Liu; Jiangpeng Feng; Ming Guo; Wenjia Hu; Dong Guo; Hafiz Ullah; Yang Yang; Qiuhan Zhang; Xin Wang; Muhanmmad Sajid; Zhixiang Huang; Liping Deng; Tielong Chen; Fang Liu; Ke Xu; Yuan Liu; Qi Zhang; Yingle Liu; Yong Xiong; Guozhong Chen; Ke Lan; Yu Chen ddPCR: a more sensitive and accurate tool for SARS-CoV-2 detection in low viral load specimens Real time fluorescent quantitative PCR (RT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, due to the low viral load in patient throats and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, early treat, cut off transmission, and assess discharge criteria. To improve this situation, an optimized droplet digital PCR (ddPCR) was used for detection of SARS-CoV-2, which showed that the limit of detection of ddPCR is significantly lower than that of RT-PCR. We further explored the feasibility of ddPCR to detect SARS-CoV-2 nucleic acid from 77 clinical throat swab samples, including 63 suspected outpatients with fever and 14 supposed convalescents who were about to discharge after treatment, and compared with RT-PCR in terms of the diagnostic accuracy. In this double-blind study, we tested, surveyed subsequently and statistically analyzed 77 clinical samples. According to our study, 26 samples from COVID-19 patients with RT-PCR negative were detected as positive by ddPCR. No FPRs of RT-PCR and ddPCR were observed. The sensitivity, specificity, PPV, NPV, NLR and accuracy were improved from 40% (95% CI: 27-55%), 100% (95% CI: 54-100%), 100%, 16% (95% CI: 13-19%), 0.6 (95% CI: 0.48-0.75) and 47% (95% CI: 33-60%) for RT-PCR to 94% (95% CI: 83-99%), 100% (95% CI: 48-100%), 100%, 63% (95% CI: 36-83%), 0.06 (95% CI: 0.02-0.18) and 95% (95% CI: 84-99%) for ddPCR, respectively. Moreover, 14 (42.9 %) convalescents still carry detectable SARS-CoV-2 after discharge. Overall, ddPCR shows superiority for clinical diagnosis of SARS-CoV-2 to reduce the false negative reports, which could be a powerful complement to the current standard RT-PCR. It also suggests that the current clinical practice that the convalescent after discharge continues to be quarantined for at least 2 weeks is completely necessary which can prevent potential viral transmission. 2020-03-06
Yang Yang; Chenguang Shen; Jinxiu Li; Jing Yuan; Minghui Yang; Fuxiang Wang; Guobao Li; Yanjie Li; Li Xing; Ling Peng; Jinli Wei; Mengli Cao; Haixia Zheng; Weibo Wu; Rongrong Zou; Delin Li; Zhixiang Xu; Haiyan Wang; Mingxia Zhang; Zheng Zhang; Lei Liu; Yingxia Liu Exuberant elevation of IP-10, MCP-3 and IL-1ra during SARS-CoV-2 infection is associated with disease severity and fatal outcome The outbreak of Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, December 2019, and continuously poses a serious threat to public health. Our previous study has shown that cytokine storm occurred during SARS-CoV-2 infection, while the detailed role of cytokines in the disease severity and progression remained unclear due to the limited case number. In this study, we examined 48 cytokines in the plasma samples from 53 COVID-19 cases, among whom 34 were severe cases, and the others moderate. Results showed that 14 cytokines were significantly elevated upon admission in COVID-19 cases. Moreover, IP-10, MCP-3, and IL-1ra were significantly higher in severe cases, and highly associated with the PaO2/FaO2 and Murray score. Furthermore, the three cytokines were independent predictors for the progression of COVID-19, and the combination of IP-10, MCP-3 and IL-1ra showed the biggest area under the curve (AUC) of the receiver-operating characteristics (ROC) calculations. Serial detection of IP-10, MCP-3 and IL-1ra in 14 severe cases showed that the continuous high levels of these cytokines were associated with disease deterioration and fatal outcome. In conclusion, we report three cytokines that closely associated with disease severity and outcome of COVID-19. These findings add to our understanding of the immunopathologic mechanisms of SARS-CoV-2 infection, which suggested novel therapeutic targets and strategy. 2020-03-06
Joe Hilton; Matt J Keeling Estimation of country-level basic reproductive ratios for novel Coronavirus (COVID-19) using synthetic contact matrices The outbreak of novel coronavirus (COVID-19) has the potential for global spread, infecting large numbers in all countries. In this case, estimating the country-specific basic reproductive ratio is a vital first step in public-health planning. The basic reproductive ratio (R0) is determined by both the nature of pathogen and the network of contacts through which the disease can spread - with this network determined by socio-demographics including age-structure and household composition. Here we focus on the age-structured transmission within the population, using data from China to inform age-dependent susceptibility and synthetic age-mixing matrices to inform the contact network. This allows us to determine the country-specific basic reproductive ratio as a multiplicative scaling of the value from China. We predict that R0 will be highest across Eastern Europe and Japan, and lowest across Africa, Central America and South-Western Asia. This pattern is largely driven by the ratio of children to older adults in each country and the observed propensity of clinical cases in the elderly. 2020-02-27
Matt J Keeling; T. Deirdre Hollingsworth; Jonathan M Read The Efficacy of Contact Tracing for the Containment of the 2019 Novel Coronavirus (COVID-19). Contact tracing is a central public health response to infectious disease outbreaks, especially in the early stages of an outbreak when specific treatments are limited. Importation of novel Coronavirus (COVID-19) from China and elsewhere into the United Kingdom highlights the need to understand the impact of contact tracing as a control measure. Using detailed survey information on social encounters coupled to predictive models, we investigate the likely efficacy of the current UK definition of a close contact (within 2 meters for 15 minutes or more) and the distribution of secondary cases that may go untraced. Taking recent estimates for COVID-19 transmission, we show that less than 1 in 5 cases will generate any subsequent untraced cases, although this comes at a high logistical burden with an average of 36.1 individuals (95th percentiles 0-182) traced per case. Changes to the definition of a close contact can reduce this burden, but with increased risk of untraced cases; we estimate that any definition where close contact requires more than 4 hours of contact is likely to lead to uncontrolled spread. 2020-02-17
Leon Danon; Ellen Brooks-Pollock; Mick Bailey; Matt J Keeling A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing Background: An outbreak of a novel coronavirus, named CoVID-19, was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable cases of human-to-human transmission in England. Methods: We adapted an existing national-scale metapopulation model to capture the spread of CoVID-19 in England and Wales. We used 2011 census data to capture population sizes and population movement, together with parameter estimates from the current outbreak in China. Results: We predict that a CoVID-19 outbreak will peak 126 to 147 days (~4 months) after the start of person-to-person transmission in England and Wales in the absence of controls, assuming biological parameters remain unchanged. Therefore, if person-to-person transmission persists from February, we predict the epidemic peak would occur in June. The starting location has minimal impact on peak timing, and model stochasticity varies peak timing by 10 days. Incorporating realistic parameter uncertainty leads to estimates of peak time ranging from 78 days to 241 days after person-to-person transmission has been established. Seasonal changes in transmission rate substantially impact the timing and size of the epidemic peak, as well as the total attack rate. Discussion: We provide initial estimates of the potential course of CoVID-19 in England and Wales in the absence of control measures. These results can be refined with improved estimates of epidemiological parameters, and permit investigation of control measures and cost effectiveness analyses. Seasonal changes in transmission rate could shift the timing of the peak into winter months, which will have important implications for healthcare capacity planning. 2020-02-14
Samuel J Clifford; Carl A B Pearson; Petra Klepac; Kevin Van Zandvoort; Billy J Quilty; - CMMID COVID-19 working group; Rosalind M Eggo; Stefan Flasche Interventions targeting air travellers early in the pandemic may delay local outbreaks of SARS-CoV-2 Background: We evaluated if interventions aimed at air travellers can delay local SARS-CoV-2 community transmission in a previously unaffected country. Methods: We simulated infected air travellers arriving into countries with no sustained SARS-CoV-2 transmission or other introduction routes from affected regions. We assessed the effectiveness of syndromic screening at departure and/or arrival & traveller sensitisation to the COVID-2019-like symptoms with the aim to trigger rapid self-isolation and reporting on symptom onset to enable contact tracing. We assumed that syndromic screening would reduce the number of infected arrivals and that traveller sensitisation reduces the average number of secondary cases. We use stochastic simulations to account for uncertainty in both arrival and secondary infections rates, and present sensitivity analyses on arrival rates of infected travellers and the effectiveness of traveller sensitisation. We report the median expected delay achievable in each scenario and an inner 50% interval. Results: Under baseline assumptions, introducing exit and entry screening in combination with traveller sensitisation can delay a local SARS-CoV-2 outbreak by 8 days (50% interval: 3-14 days) when the rate of importation is 1 infected traveller per week at time of introduction. The additional benefit of entry screening is small if exit screening is effective: the combination of only exit screening and traveller sensitisation can delay an outbreak by 7 days (50% interval: 2-13 days). In the absence of screening, with less effective sensitisation, or a higher rate of importation, these delays shrink rapidly to less than 4 days. Conclusion: Syndromic screening and traveller sensitisation in combination may have marginally delayed SARS-CoV-2 outbreaks in unaffected countries. 2020-02-13
Yang Yang; Minghui Yang; Chenguang Shen; Fuxiang Wang; Jing Yuan; Jinxiu Li; Mingxia Zhang; Zhaoqin Wang; Li Xing; Jinli Wei; Ling Peng; Gary Wong; Haixia Zheng; Mingfeng Liao; Kai Feng; Jianming Li; Qianting Yang; Juanjuan Zhao; Zheng Zhang; Lei Liu; Yingxia Liu Laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections Background: The outbreak of novel coronavirus pneumonia (NCP) caused by 2019-nCoV spread rapidly, and elucidation the diagnostic accuracy of different respiratory specimens is crucial for the control and treatment of this diseases. Methods: Respiratory samples including nasal swabs, throat swabs, sputum and bronchoalveolar lavage fluid (BALF) were collected from Guangdong CDC confirmed NCP patients, and viral RNAs were detected using a CFDA approved detection kit. Results were analyzed in combination with sample collection date and clinical information. Finding: Except for BALF, the sputum possessed the highest positive rate (74.4%~88.9%), followed by nasal swabs (53.6%~73.3%) for both severe and mild cases during the first 14 days after illness onset (d.a.o). For samples collected [&ge;] 15 d.a.o, sputum and nasal swabs still possessed a high positive rate ranging from 42.9%~61.1%. The positive rate of throat swabs collected [&ge;] 8 d.a.o was low, especially in samples from mild cases. Viral RNAs could be detected in all the lower respiratory tract of severe cases, but not the mild cases. CT scan of cases 02, 07 and 13 showed typical viral pneumonia with ground glass opacity, while no viral RNAs were detected in first three or all the upper respiratory samples. Interpretation: Sputum is most accurate for laboratory diagnosis of NCP, followed by nasal swabs. Detection of viral RNAs in BLAF is necessary for diagnosis and monitoring of viruses in severe cases. CT scan could serve as an important make up for the diagnosis of NCP. Funding National Science and Technology Major Project, Sanming Project of Medicine and China Postdoctoral Science Foundation. 2020-02-12
Joel Hellewell; Sam Abbott; Amy Gimma; Nikos I Bosse; Christopher I Jarvis; Timothy W Russell; James D Munday; Adam J Kucharski; W John Edmunds; CMMID nCoV working group; Sebastian Funk; Rosalind M Eggo Feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts Background: To assess the viability of isolation and contact tracing to control onwards transmission from imported cases of 2019-nCoV. Methods: We developed a stochastic transmission model, parameterised to the 2019-nCoV outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a 2019 nCoV-like pathogen. We considered scenarios that varied in: the number of initial cases; the basic reproduction number R0; the delay from symptom onset to isolation; the probability contacts were traced; the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings: While simulated outbreaks starting with only 5 initial cases, R0 of 1.5 and little transmission before symptom onset could be controlled even with low contact tracing probability, the prospects of controlling an outbreak dramatically dropped with the number of initial cases, with higher R0, and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R0 of 1.5 were controllable with under 50% of contacts successfully traced. For R0 of 2.5 and 3.5, more than 70% and 90% of contacts respectively had to be traced to control the majority of outbreaks. The delay between symptom onset and isolation played the largest role in determining whether an outbreak was controllable for lower values of R0. For higher values of R0 and a large initial number of cases, contact tracing and isolation was only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation: We found that in most scenarios contact tracing and case isolation alone is unlikely to control a new outbreak of 2019-nCov within three months. The probability of control decreases with longer delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts. 2020-02-11
Yang Yang; Qingbin Lu; Mingjin Liu; Yixing Wang; Anran Zhang; Neda Jalali; Natalie Dean; Ira Longini; M. Elizabeth Halloran; Bo Xu; Xiaoai Zhang; Liping Wang; Wei Liu; Liqun Fang Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China Our manuscript was based on surveillance cases of COVID-19 identified before January 26, 2020. As of February 20, 2020, the total number of confirmed cases in mainland China has reached 18 times of the number in our manuscript. While the methods and the main conclusions in our original analyses remain solid, we decided to withdraw this preprint for the time being, and will replace it with a more up-to-date version shortly. Should you have any comments or suggestions, please feel free to contact the corresponding author. 2020-02-11
Adam J Kucharski; Timothy W Russell; Charlie Diamond; Yang Liu; CMMID nCoV working group; John Edmunds; Sebastian Funk; Rosalind M Eggo Early dynamics of transmission and control of 2019-nCoV: a mathematical modelling study Background: An outbreak of the novel coronavirus SARS-CoV-2 has led to 46,997 confirmed cases as of 13th February 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Methods: We combined a stochastic transmission model with data on cases of novel coronavirus disease (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January and February 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. Findings: We estimated that the median daily reproduction number, Rt , declined from 2.35 (95% CI: 1.15-4.77) one week before travel restrictions were introduced on 23rd January to 1.05 (95% CI: 0.413-2.39) one week after. Based on our estimates of Rt,we calculated that in locations with similar transmission potential as Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population. Interpretation: Our results show that COVID-19 transmission likely declined in Wuhan during late January 2020, coinciding with the introduction of control measures. As more cases arrive in international locations with similar transmission potential to Wuhan pre-control, it is likely many chains of transmission will fail to establish initially, but may still cause new outbreaks eventually. 2020-02-02
Billy Quilty; Sam Clifford; Stefan Flasche; Rosalind M Eggo Effectiveness of airport screening at detecting travellers infected with 2019-nCoV As the number of novel coronavirus cases grows both inside and outside of China, public health authorities require evidence on the effectiveness of control measures such as thermal screening of arrivals at airports. We evaluated the effectiveness of exit and entry screening for 2019-nCoV infection. In our baseline scenario, we estimated that 46.5% (95%CI: 35.9 to 57.7) of infected travellers would not be detected, depending on the incubation period, sensitivity of exit and entry screening, and the proportion of cases which are asymptomatic. Airport screening is unlikely to detect a sufficient proportion of 2019-nCoV infected travellers to avoid entry of infected travellers. We developed an online tool so that results can be updated as new information becomes available. 2020-02-02
Vespignani A, Tian H, Dye C, Lloyd-Smith JO, Eggo RM, Shrestha M, Scarpino SV, Gutierrez B, Kraemer MUG, Wu J, Leung K, Leung GM. Modelling COVID-19. Nature Reviews Physics
Banerjee A, Pasea L, Harris S, Gonzalez-Izquierdo A, Torralbo A, Shallcross L, Noursadeghi M, Pillay D, Sebire N, Holmes C, Pagel C, Wong WK, Langenberg C, Williams B, Denaxas S, Hemingway H. Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study. Lancet (London, England) 2020
Prem K, Liu Y, Russell TW, Kucharski AJ, Eggo RM, Davies N, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Jit M, Klepac P. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. The Lancet. Public health 2020
Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, Munday JD, Kucharski AJ, Edmunds WJ, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Funk S, Eggo RM. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet. Global health 2020
Drew DA, Nguyen LH, Steves CJ, Menni C, Freydin M, Varsavsky T, Sudre CH, Cardoso MJ, Ourselin S, Wolf J, Spector TD, Chan AT, COPE Consortium. Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science (New York, N.Y.) 2020
Quilty BJ, Clifford S, Flasche S, Eggo RM, CMMID nCoV working group. Effectiveness of airport screening at detecting travellers infected with novel coronavirus (2019-nCoV). Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020
Hastie CE, Mackay DF, Ho F, Celis-Morales CA, Katikireddi SV, Niedzwiedz CL, Jani BD, Welsh P, Mair FS, Gray SR, O'Donnell CA, Gill JM, Sattar N, Pell JP. Vitamin D concentrations and COVID-19 infection in UK Biobank. Diabetes & Metabolic Syndrome
Jit M, Jombart T, Nightingale ES, Endo A, Abbott S, Lshtm Centre For Mathematical Modelling Of Infectious Diseases Covid-Working Group, Edmunds WJ. Estimating number of cases and spread of coronavirus disease (COVID-19) using critical care admissions, United Kingdom, February to March 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020
Russell TW, Hellewell J, Jarvis CI, van Zandvoort K, Abbott S, Ratnayake R, Cmmid Covid-Working Group, Flasche S, Eggo RM, Edmunds WJ, Kucharski AJ. Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020
Been JV, Sheikh A. COVID-19 must catalyse key global natural experiments. Journal of global health 2020
Jarvis CI, Van Zandvoort K, Gimma A, Prem K, CMMID COVID-19 working group, Klepac P, Rubin GJ, Edmunds WJ. Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC medicine 2020
Polonsky JA, Baidjoe A, Kamvar ZN, Cori A, Durski K, Edmunds WJ, Eggo RM, Funk S, Kaiser L, Keating P, de Waroux OLP, Marks M, Moraga P, Morgan O, Nouvellet P, Ratnayake R, Roberts CH, Whitworth J, Jombart T. Outbreak analytics: a developing data science for informing the response to emerging pathogens. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 2019
Mizen A, Lyons J, Milojevic A, Doherty R, Wilkinson P, Carruthers D, Akbari A, Lake I, Davies GA, Al Sallakh M, Fry R, Dearden L, Rodgers S. Impact of air pollution on educational attainment for respiratory health treated students: A cross sectional data linkage study. Health & place
GBD 2017 Lower Respiratory Infections Collaborators. Quantifying risks and interventions that have affected the burden of lower respiratory infections among children younger than 5 years: an analysis for the Global Burden of Disease Study 2017. The Lancet. Infectious diseases 2020
Lewandowski K, Xu Y, Pullan ST, Lumley SF, Foster D, Sanderson N, Vaughan A, Morgan M, Bright N, Kavanagh J, Vipond R, Carroll M, Marriott AC, Gooch KE, Andersson M, Jeffery K, Peto TEA, Crook DW, Walker AS, Matthews PC. Metagenomic Nanopore Sequencing of Influenza Virus Direct from Clinical Respiratory Samples. Journal of clinical microbiology 2019
Global Burden of Disease Health Financing Collaborator Network. Health sector spending and spending on HIV/AIDS, tuberculosis, and malaria, and development assistance for health: progress towards Sustainable Development Goal 3. Lancet (London, England) 2020
Total COVID-19 Research Questions: 84
Question Author Topics
Can we identify vulnerable groups from users of the UK's largest mental health service provider? Robert Stewart
Honghan Wu
RQ09
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 6
Funnel Stage: Prioritise
COVID-19
SAGE Priority Area: Other conditions
vulnerable patients
mental health
Prioritisation Rounds: 2
How can we maximise the speed and power of host genomic studies internationally to inform drug development? Martin Tobin RQ36
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Prioritisation Rounds: 2
Funnel Stage: Analysis, User Management
COVID-19
SAGE Report
How long will immunity last following infection, and what level of protection is available against different mutant strains? What is the most effective approach for identifying people as recovered/immune? Chris Wigley RQ50
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 9
COVID-19
SAGE Report
Prioritisation Rounds: 2
Funnel Stage: Analysis, User Management
To what extent does the strain in the UK match international profiles, and so will genomic research in the UK be universally applicable? Chris Wigley RQ53
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Which antibodies treatments reduce lung damage in cytokine release storms? Chris Wigley RQ55
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
What are the COVID-19 impacts on shielded individuals? Ronan Lyons RQ71
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we investigate the impact of COVID-19 on Acute Kidney Injury (AKI)? Ronan Lyons RQ84
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
We should include social determinants, including social norms and discourse in different communities in any epidemiological questions raised Linsey Hovard RQ17
SAGE Priority Area: Indirect Impact - socio-economic
Prioritisation Rounds: 1
Funnel Stage: Sort
COVID-19
SAGE Report
What are 30 day and longer term outcomes of patients with and without infection admitted to hospital? There are important biases in considering only inhospital deaths; the health consequences of infection after discharge are poorly understood. Harry Hemingway (typed in by Clara Fennessy) RQ33
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7, 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
How has the delivery of primary care to vulnerable patient groups changed during the NHS response to the COVID-19 pandemic? John Macleod RQ62
SAGE Priority Area: Indirect Impact - healthcare pressures
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Identify Resource
COVID-19
SAGE Report
Could we study the long-term impact of COVID-19 on critical care planning and future wave preparation? Ronan Lyons RQ75
SAGE Priority Area: Other
Prioritisation Score(s): 7
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
What are the psychological, social and economic consequences of policies to limit the spread and flatten the peak of COVID 19? David Porteous and Cathie Sudlow RQ22
SAGE Priority Question: Behavioural Science (SQ45)
SAGE Priority Area: Indirect Impact - socio-economic
Prioritisation Score(s): 8
Prioritisation Rounds: 2
Funnel Stage: Analysis, User Management
COVID-19
SAGE Report
Does air quality affect susceptibility to COVID-19? How is the COVID-19 pandemic affecting air quality? Anna Hansell (entered by Martin Tobin) RQ37
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 5.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we compare the outcomes data for patients who are receiving / not immunosuppressants and validate whether this population group are more vulnerable? Liz Sapey
Alastair Denniston
Tanya Pank Hurst (entered by Alice Turnbull)
RQ39
SAGE Priority Area: Other conditions
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Identify Resource
COVID-19
SAGE Report
Are there any concomitant treatments/ongoing prescribed medication which are making the outcomes of coronavirus infection worse for patients? Liz Sapey
Alastair Denniston
Tanya Pank Hurst (entered by Alice Turnbull)
RQ40
SAGE Priority Area: Other conditions
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
What are the consequences of COVID-19 on pregnancy outcomes for both mothers & babies? Rhos Walker (HDR UK Central) on behalf of the HDR UK public health pregnancy national team (Krishnarajah Nirantharakumar
Colin McCowan
Sinead Brophy & Ronan Lyons) et al.
RQ46
SAGE Priority Area: Indirect Impact - socio-economic
Prioritisation Score(s): 7.5, 6.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
To what extent can key workers be protected by immunoglobulin extracted from sick patients? Can this be feasibly scaled? Chris Wigley RQ54
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Given Hong Kong/Singapore past experience with Mass Thermal Imaging at major hubs why not extend it into schools, airports and other industries? Steve Tyrell RQ66
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we analyse the mediating factors to better understand the impact of health inequalities on COVID-19 incidence? Ronan Lyons RQ72
SAGE Priority Area: Indirect Impact-socioeconomic, other conditions
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Can we set up a national COVID-19 surveillance platform? Aziz Sheikh RQ06
SAGE Priority Area: Other
Funnel Stage: Sort
COVID-19
surveillance
Prioritisation Rounds: 0
Is the use of C-pap/Bi-Pap a useful lower acuity intervention before intubation? Chris Wigley RQ56
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7.5, 6.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we link the incidence and prevalence data from Zoe Symptom Tracker app and the patient journies through the health systems with the potential to link to outcomes? Ronan Lyons RQ76
SAGE Priority Area: Other
Prioritisation Score(s): 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Are patients who received a shortened course of immunotherapy less likely to present with severe COVID-19/ have worse outcomes? Alice Turnbull RQ44
SAGE Priority Area: Other conditions
Prioritisation Score(s): 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Asymptomatic carriers: do they exist, and what proportion of the population fit into this category? Does this differ with age? Immunology and serology (SQ 1 & SQ6) Chris Wigley RQ51
SAGE Priority Question: Immunology and serology (SQ4, SQ6)
SAGE Priority Area: Indirect impact - socio-economic
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
How can we build from existing studies (e.g. Kings and internationally) that have looked at the effect of ACE inhibitors and Angiotensin Receptor Blockers on predisposition to COVID-19? Professor Sir Munir Pirmohamed RQ57
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Identify Resource
COVID-19
SAGE Report
What are the direct and indirect effects of SARS-Cov2/COVID-19 on incidence, presentation, diagnosis, management, and prognosis of cancer patients and which patients are most susceptible to these direct and indirect effects? Jonine Figueroa RQ59
SAGE Priority Question: Clinical Health Care Management (SQ23)
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8.5
Prioritisation Rounds: 2
Funnel Stage: Analysis, User Management
COVID-19
SAGE Report
What are the direct and indirect impacts of COVID-19 on care home residents? Question put forward by Caroline Cake RQ63
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Two initiatives in relation to a mortality heat map and analysis of COVID mortality relating to ethnicity and deprivation Leigh Johnson RQ68
SAGE Priority Area: Indirect Impact - healthcare pressures, Indirect Impact - socio-economic, Other conditions
Prioritisation Rounds: 1
COVID-19
SAGE Report
Funnel Stage: Prioritise
Prioritisation Score: 9
Could we analyse the biological interventions prescribed by class in COVID-19 positive patients? Ronan Lyons RQ82
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Does COVID-19 incidence, hospitalisation and mortility rates differ for patients taking Non-steroidal Anti-inflammatory Drugs (NSAID) and controls? Ronan Lyons RQ83
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
What is the influence of pre-existing cardiovascular disease on outcomes of COVID-19 infection? Linked question: What is the influence of pre-existing cancer impact on outcomes of COVID-19 infection? Cathie Sudlow RQ30
SAGE Priority Area: Other conditions
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Identify Resource
COVID-19
SAGE Report
Could we analyse the impact of COVID-19 by Occupational Group (to help address the limitation of ONS data), with a focus on key workers? Ronan Lyons RQ77
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
ISARIC-CCP: What are the clinical characteristics of COVID-19 positive patients; what are the determinants (genetic, other omic, prior medical history, other) of good and poor outcome; and how can knowledge of this help to target clinical and public health strategies? (RQ05) Cathie Sudlow & John Danesh RQ05
SAGE Priority Area: Direct Impact
Prioritisation Score(s): Auto-prioritised
COVID-19
SAGE Report
SAGE Priority Area: Other Conditions
Research Funnel Stage: 7 - Analysis, User Management
characteristics
determinants
genetic
omic
medical history
CCP-UK
What is the influence of COVID 19 epidemic in the UK and the NHS response to this on presentation, management and prognosis of non-COVID disease, in particular cardiovascular diseases such as MI and stroke? Cathie Sudlow RQ29
SAGE Priority Question: Clinical Health Care Management (SQ16 & SQ23)
SAGE Priority Area: Indirect Impact- healthcare pressures
Prioritisation Score(s): 8.5
Prioritisation Rounds: 4
Funnel Stage: Ethics, Funding, Access, Decision Making
COVID-19
SAGE Report
What is the time delay between first symptom development (as self-reported by patients), hospital presentation. Does this time delay influence rates of ICU referral and mortality? Alice Turnbull RQ42
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 6.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
COG-UK: Can study of the whole virus genome enable scientists to monitor changes at a national scale, reveal how the virus is spreading and whether different strains are emerging? (RQ01) Ewan Harrison
MIchael Chapman
John Danesh
RQ01
SAGE Priority Question: Virology (SQ11)
SAGE Priority Area: Direct Impact
Prioritisation Score(s): Auto-prioritised
Funnel Stage: Analysis, User Management
COVID-19
SAGE Report
virus genome
virus strain
Funnel Stage: Published Insights
Is it possible to predict the likelihood of ICU admission and provide real time data on outcomes per COVID-19 admission? Caroline Cake on behalf of Jose Sousa and Marko Balabanovic RQ11
SAGE Priority Question: Clinical Health Care Management (SQ16 & SQ23)
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8.5
Prioritisation Rounds: 2
Funnel Stage: Analysis, User Management
COVID-19
SAGE Report
SAGE Priority Area: Indirect Impact - healthcare pressures
SAGE Priority Area: Other conditions
SAGE Priority Area: Indirect Impact - socio-economic
EHR
ICU
age
demographics
pre-existing conditions
symptoms
clinical data
hospital data
Can we have support in setting up a national platform for appropriately reviewed publications on COVID? Liz Sapey RQ13
SAGE Priority Area: Indirect Impact - healthcare pressures
Prioritisation Rounds: 1
Funnel Stage: Sort
COVID-19
SAGE Report
How can we explain the differences in COVID-19 cases and deaths by key socio-demographic factors of age, sex, socioeconomic status, geographical location and ethnicity (BME groups)? Rhos Walker - inspired by prelimanary data from Kamlesh Khunti et. al. :https://twitter.com/docwas/status/1246421363763687424?s=20 (moved to below in sheet by Clara Fennessy 06/04/2020 17:12) and Prof. Melinda Mills (typed in by Clara Fennessy) RQ34
SAGE Priority Area: Direct impact
Prioritisation Score(s): 10
Prioritisation Rounds: 1
Funnel Stage: Published Insights
COVID-19
SAGE Report
Are we systematically recording the barriers we are facing to answer the above resarch questions? Alice Turnbull RQ45
SAGE Priority Area: Other
Prioritisation Rounds: 1
Funnel Stage: Sort
COVID-19
SAGE Report
CovidLife Survey David Porteous RQ64
SAGE Priority Area: Indirect Impact - socio-economic
Prioritisation Score(s): N/A (as sub question to RQ22)
Prioritisation Rounds: 1
Funnel Stage: Prioritise(d) as subset to RQ22
COVID-19
SAGE Report
Does oxytocin explain the morbidity and susceptibility patterns in COVID-19? Phuoc-Tan Diep RQ65
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Which people in the high risk/shielded patients list could directly benefit from vaccination? Hadijatou Sallah RQ69
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Does having oestrogen lead to a reduction in the severity and mortality of COVID-19? Dr Louise Newson RQ60
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 6.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Does receiving androgen-deprivation therapies for prostate cancer lower the risk of infection by SARS-CoV-2? Hayley Luxton RQ70
SAGE Priority Area: Other conditions
Prioritisation Score(s): 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we attempt to correlate frailty as measured by the electronic frailty index (eFI) as a possible predictor of COVID-19 hospitalisation, ICU/ITU and death? Ronan Lyons RQ80
SAGE Priority Area: Other
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
How can we measure the ongoing prevalnce of COVID-19 following idenitifcation of a "good enough" antibody diagnosic? Rhos Walker ( via Peter Diggle) - required in next 3-6 months RQ04
SAGE Priority Question: Epidemiology (SQ1)
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 9
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
antibodies
antibody diagnostic
vulnerable patients
representative sampling
NHSX would like to create a national database of chest X-rays images that enables the validation and supports development of automated diagnosis technologies in response to the COVID-19 pandemic Dominic Cushnan
Alberto Favaro (Faculty) Ottavia Bertolli (Faculty) Rosalind Berka (Faculty)
RQ25
SAGE Priority Area: Direct Impact
Prioritisation Rounds: 1
Funnel Stage: Sort
COVID-19
SAGE Report
Understanding vulnerable patients: How are underlying conditions defined, and what is the impact of infection on a range of outcomes, and what are the benefits of 'shielding' and other preventive interventions? Linked question: COVID-19 Risk prediction (Relevant to “How do we best understand and protect vulnerable populations?”) Harry Hemingway (typed in by Clara Fennessy) RQ32
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 9
Prioritisation Rounds: 1
COVID-19
SAGE Report
Funnel Stage: Analysis, User Management
Cancer and COVID-19; how do we manage cancer optimally through a public health crisis? Prof Mark Lawler (typed in by Clara Fennessy) RQ48
SAGE Priority Question: Clinical Health Care Management (SQ23)
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we track the patient flow metrics from incidence, adminssion through to ITU and outcome? Ronan Lyons RQ74
SAGE Priority Area: Other
Prioritisation Score(s): 5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Can the UK agree on a uniform way COVID-19 is recorded in EHRs? Neil Sebire; Spiros Denaxas etc RQ08
SAGE Priority Area: Other
Funnel Stage: Sort
COVID-19
Prioritisation Rounds: 0
EHR
data recording
What is the best route for understanding the number of people in vulnerable categories? Ben Gordon
Roz Eggo
Rich Fry
RQ10
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7
Funnel Stage: Prioritise
COVID-19
location
vulnerable patients
HES
CRPD
Prioritisation Rounds: 2
Can we simply provide an daily symptom journal app to provide geospatial tracking of pre-covid symptoms? Via Tim Spector: Chris Orton or David Ford; Aziz Sheikh RQ16
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7.5
Prioritisation Rounds: 2
Funnel Stage: Analysis, User Management
COVID-19
SAGE Report
What are the consequences of COVID-19 infection on respiratory health and disease as assessed in (i) general populations previously characterised for respiratoiry health (including spirometry) and (ii) among people discharged after severe COVID-19? Martin Tobin on behalf of BREATHE
Eur Respiratory Society (Chris Brightling)
SpiroMeta consortium (Martin
Tobin
Ian Hall) and pulmonary fibrosis consortia (Louise Wain
Gisli Jenkins)
RQ38
SAGE Priority Area: Other conditions
Prioritisation Score(s): 7.5, 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Are there any treatments which show evidence of improving outcomes for patients infected with coronavirus? Liz Sapey
Alastair Denniston
Tanya Pank Hurst (entered by Alice Turnbull)
RQ41
SAGE Priority Question: Clinical Health Care Management (SQ20)
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 9
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Can we use longitudinal peripheral blood measurements, such as leukocyte counts, liver fucntion readouts etc., to predect time of progression in disease severity using machine learning methods? John Connolly RQ49
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we analyse COVID mortality/hospitalisations/need for ICU amongst patients taking monoclonal antibodies versus those not taking them? Ronan Lyons RQ85
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Can we analyse doctors' notes in medical records of COVID-19 patients, using natural language processing (NLP), to gain insights onto the disease? Richard Dobson RQ15
SAGE Priority Area: Direct Impact
Prioritisation Rounds: 1
Funnel Stage: Sort
COVID-19
SAGE Report
Socioeconomic inequalities: Analysis by postcode IMD. What's the best way to provide targeted and tailored messages to diverse communities? Linsey Hovard RQ23
SAGE Priority Question: Behavioural Science (SQ39)
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Identify Resource
COVID-19
SAGE Report
What is the influence of COVID-19 on subsequent cardiovascular disease outcomes? Cathie Sudlow RQ28
SAGE Priority Area: Other conditions
Prioritisation Score(s): 7
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
To create and use a high resolution COVID-19 data resource based on data from digitally mature hospital trusts to enable near real time advanced data analytics, producing predictive tools to guide clinical decision making (DeCOVID) Chris Holmes and Liz Sapey (typed in by Melissa Lewis-Brown) RQ31
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Is the Clinical Frailty Index (recorded at hospital presentation) acting as a useful for likely ICU referral? Alice Turnbull RQ43
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 6.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we analyse NHS resilience analysis from linked disease status; hospital treatment; and critical care data to facilitate improved models? Ronan Lyons RQ73
SAGE Priority Area: Direct Impact, Indirect Impact - healthcare pressures
Prioritisation Score(s): 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Visual research of CoV familiy of viruses (CoVID-19, SARS, MERS) as seen through SEM/TEM/hematology analyzer? Charles Krul/Sam Weitzman via Phil Quinlan (TDCC request) RQ02
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 4.5
Funnel Stage: Prioritise
COVID-19
virus strain
SEM/TEM/hematology analyzer
Prioritisation Rounds: 2
Can a web-based intervention reduce anxiety and worry for people shielding indefinitely with long-term conditions? Dr Colette Hirsch RQ58
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 7
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
What is the influence of diabetes as an independent risk factor for infection and poorer outcomes of COVID-19? Alice Turnbull RQ67
SAGE Priority Area: Other conditions
Prioritisation Score(s): 7.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we analyse the readmission rates for COVID-19? Ronan Lyons RQ78
SAGE Priority Area: Direct Impact, Indirect Impact - healthcare pressures
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Can we create dummy datasets on the EHR COVID data for analyses and model set up? Ewan Birney RQ12
SAGE Priority Area: Other
Prioritisation Rounds: 1
Funnel Stage: Sort
COVID-19
SAGE Report
Can we predict the likelihood of an individual’s risk of complications/hospital admission/mortality upon contracting or suspected to have contracted COVID-19 given demographics, pre-existing conditions, symptoms and vital sign data collected from a smartphone app? Marko Balabanovic RQ26
SAGE Priority Area: Indirect Impact - socio-economic
Prioritisation Score(s): 7.5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we analyse the different clinical pathway approaches to COVID-19 positive patients? Ronan Lyons RQ79
SAGE Priority Area: Other
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
RECOVERY: Can Lopinavir-Ritonavir vs Interferon β vs lowdose corticosteroids be effective in treating COVID 19 test +ve hospitalised patients? (RQ18) Peter Horby; Martin Landray RQ18
SAGE Priority Question: Treatments & Preventative Measures (SQ26)
SAGE Priority Area: Direct Impact
Prioritisation Score(s): Auto-prioritised
Funnel Stage: Analysis, User Management
COVID-19
SAGE Report
What are the efects of COVID-19 on people affected by Multiple Sclerosis? David Ford RQ20
SAGE Priority Area: Other conditions
Prioritisation Score(s): 6
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Can we better engage the public in our COVID-19 research and innovation activities? Sinduja Manohar RQ21
SAGE Priority Area: Other
Prioritisation Rounds: 1
Funnel Stage: Sort
COVID-19
SAGE Report
What are the long-term mental health impacts of COVID-19 social isolation measures? Rhos Walker RQ24
SAGE Priority Question: Clinical/healthcare management (SQ23)
SAGE Priority Area: Indirect Impact - socio-economic
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
How can we ensure that we fully understand variations in response to COVID-19 infection at the molecular, environmental, social and economic levels, by effectively coordinating the UK's longtidudinal population stuides to gain a much richer understanding of disease progression and outcomes? Mary De Silva
Debbie Lawlor
Martin Tobin. John Danesh
Nic Timpson
& David Porteous; Wellcome Trust COVID-19 Longitudinal Population Study Steering Group
RQ35
SAGE Priority Question: Clinical and Health Care Management (SQ17)
SAGE Priority Area: Direct impact
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
What is the outcome (hospital admission / ITU admission / death) for patients with Inflammatory bowel disease / IBD on immunosuppressant or anti-TNF therapies who test +ve for Covid19? Miles Parkes RQ61
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 5
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
Could we analyse the incidence of COVID-19 on severe mental health vulnerable groups and subgroups - e.g. schizophrenia and treatment resistant schizophrenia Ronan Lyons RQ81
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
COVID-19
SAGE Report
What are the long-lasting indirect implications for clinical outcomes and healthcare workload (within primary and secondary care) as a result of the alteration of health-seeking behaviours from the COVID-19 lockdown? Initiative: Monitoring Attendance, INvestigation, Referral, and OUTcomEs in Primary Care: impact of and recovery from COVID-19 lockdown (MAINROUTE) MAINROUTE
lockdown
behaviours
recovery
SAGE Report
COVID-19
RQ94
Prioritisation Rounds: 1
Prioritisation Score(s): 9
SAGE Priority Area: Indirect Impact - healthcare pressures
SAGE Priority Area: Indirect Impact - socio-economic
Funnel Stage: Prioritise
Risk stratification for patients treated with drugs affecting the immune system: a) In patients receiving drugs affecting the immune system (e.g. patients with gastrointestinal/liver conditions and/or patients with dermatological conditions) and who have been infected by COVID-19, what factors are associated with an increased risk of poor outcomes?b) Compared to individuals infected by COVID-19 who are not receiving drugs affecting the immune system, do individuals infected by COVID-19 who are receiving drugs affecting the immune system have an increased risk of severe illness from COVID? (RQ102) Prioritisation Score(s): 8
SAGE Report
RQ102
SAGE Priority Area: Other Conditions
Prioritisation Rounds: 1
risk stratification
co-morbidities
immune system
severe COVID-19
How can data best support the recovery and restoration of cancer/health and care services: including ongoing treatment, diagnostic tests, appointments, screening services and palliative care (factoring in regional variation of impact)? SAGE Report
Prioritisation Score(s): 8.5
SAGE Priority Area: Indirect Impact - healthcare pressures
SAGE Priority Area: Other conditions
RQ89
Funnel Stage: Prioritise
COVID-19
recovery
cancer
healthcare services
health professionals
ongoing treatment
palliative care
regional variation
What is the diagnostic accuracy of multiple current and emerging Point-of-Care-Tests (POCTS) for active and past COVID-19 infection in the community setting? COVID-19
SAGE Report
Prioritisation Score(s): 9
SAGE Priority Area: Direct Impact
SAGE Priority Area: Indirect Impact - socio-economic
SAGE Priority Area: Indirect Impact - healthcare pressures
RQ95
Prioritisation Rounds: 1
serology
POCTs
RAPTOR-C19
How can we use health data to determine the proportion of the population not susceptible to COVID-19 (e.g. groups who have tested positive but haven’t shown any symptoms)? What is the potential scale of this and how can this information be used to inform decision making around the management of the disease? COVID-19
SAGE Report
SAGE Priority Area: Direct Impact
Prioritisation Score(s): 8
Funnel Stage: Prioritise
testing
population
susceptibility to COVID-19
RQ104
What is the impact of starting inhaled corticosteroids early in the course of COVID-19 illness (first 7 days of symptoms) in preventing the deterioration of illness, which then leads to ED presentation and hospitalisation? COVID-19
SAGE Report
SAGE Priority Area: Direct Impact
SAGE Priority Area: Indirect Impact - healthcare pressures
Prioritisation Score(s): 8
Prioritisation Rounds: 1
Funnel Stage: Prioritise
corticosteriods
hospitalisation
Total COVID-19 Tools: 10
Author Tool name Topics Features
Zoe Global Ltd COVID-19 Symptom Tracker App COVID-19 Symptom Tracker
Imperial College COVID-19 CovidSim Microsimulation Model COVID-19 Modelling
UCL COVID-19 Phenomics COVID-19 Phenomics
BSTI COVID-19 Imaging Database COVID-19
NHSX COVID-19 Contact Tracing App - iOS COVID-19 Contact Tracing
iOS
Public Health England Public Health England COVID-19 Tracker COVID-19
NHSX COVID-19 Contact Tracing App - Android COVID-19 Contact Tracing
Android
University of St. Andrews C19 Track COVID-19 Community Tracking
UCL OurRisk.CoV COVID-19 Risk Calculator
LSHTM CMMID Interactive Applications COVID-19 Modelling
Visualisation