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Journal articleAndersson MI, Arancibia-Carcamo CV, Auckland K, et al., 2020,
SARS-CoV-2 RNA detected in blood products from patients with COVID-19 is not associated with infectious virus.
, Wellcome Open Res, Vol: 5, ISSN: 2398-502XBackground: Laboratory diagnosis of SARS-CoV-2 infection (the cause of COVID-19) uses PCR to detect viral RNA (vRNA) in respiratory samples. SARS-CoV-2 RNA has also been detected in other sample types, but there is limited understanding of the clinical or laboratory significance of its detection in blood. Methods: We undertook a systematic literature review to assimilate the evidence for the frequency of vRNA in blood, and to identify associated clinical characteristics. We performed RT-PCR in serum samples from a UK clinical cohort of acute and convalescent COVID-19 cases (n=212), together with convalescent plasma samples collected by NHS Blood and Transplant (NHSBT) (n=462 additional samples). To determine whether PCR-positive blood samples could pose an infection risk, we attempted virus isolation from a subset of RNA-positive samples. Results: We identified 28 relevant studies, reporting SARS-CoV-2 RNA in 0-76% of blood samples; pooled estimate 10% (95%CI 5-18%). Among serum samples from our clinical cohort, 27/212 (12.7%) had SARS-CoV-2 RNA detected by RT-PCR. RNA detection occurred in samples up to day 20 post symptom onset, and was associated with more severe disease (multivariable odds ratio 7.5). Across all samples collected ≥28 days post symptom onset, 0/494 (0%, 95%CI 0-0.7%) had vRNA detected. Among our PCR-positive samples, cycle threshold (ct) values were high (range 33.5-44.8), suggesting low vRNA copy numbers. PCR-positive sera inoculated into cell culture did not produce any cytopathic effect or yield an increase in detectable SARS-CoV-2 RNA. There was a relationship between RT-PCR negativity and the presence of total SARS-CoV-2 antibody (p=0.02). Conclusions: vRNA was detectable at low viral loads in a minority of serum samples collected in acute infection, but was not associated with infectious SARS-CoV-2 (within the limitations of the assays used). This work helps to inform biosafety precautions for handling blood products from patients with c
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Journal articleRiley S, Atchison C, Ashby D, et al., 2020,
REal-time Assessment of Community Transmission (REACT) of SARS-CoV-2 virus: Study protocol.
, Wellcome Open Res, Vol: 5, ISSN: 2398-502XBackground: England, UK has one of the highest rates of confirmed COVID-19 mortality globally. Until recently, testing for the SARS-CoV-2 virus focused mainly on healthcare and care home settings. As such, there is far less understanding of community transmission. Protocol: The REal-time Assessment of Community Transmission (REACT) programme is a major programme of home testing for COVID-19 to track progress of the infection in the community. REACT-1 involves cross-sectional surveys of viral detection (virological swab for RT-PCR) tests in repeated samples of 100,000 to 150,000 randomly selected individuals across England. This examines how widely the virus has spread and how many people are currently infected. The age range is 5 years and above. Individuals are sampled from the England NHS patient list. REACT-2 is a series of five sub-studies towards establishing the seroprevalence of antibodies to SARS-CoV-2 in England as an indicator of historical infection. The main study (study 5) uses the same design and sampling approach as REACT-1 using a self-administered lateral flow immunoassay (LFIA) test for IgG antibodies in repeated samples of 100,000 to 200,000 adults aged 18 years and above. To inform study 5, studies 1-4 evaluate performance characteristics of SARS-CoV-2 LFIAs (study 1) and different aspects of feasibility, usability and application of LFIAs for home-based testing in different populations (studies 2-4). Ethics and dissemination: The study has ethical approval. Results are reported using STROBE guidelines and disseminated through reports to public health bodies, presentations at scientific meetings and open access publications. Conclusions: This study provides robust estimates of the prevalence of both virus (RT-PCR, REACT-1) and seroprevalence (antibody, REACT-2) in the general population in England. We also explore acceptability and usability of LFIAs for self-administered testing for SARS-CoV-2 antibody in a home-based setting, not done before at
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Journal articleJeffrey B, Walters CE, Ainslie KEC, et al., 2020,
Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK.
, Wellcome Open Res, Vol: 5, ISSN: 2398-502XBackground: Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. Methods: Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths. Results: We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister's announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility. Conclusions: These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.
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Journal articleAldridge RW, Lewer D, Beale S, et al., 2020,
Seasonality and immunity to laboratory-confirmed seasonal coronaviruses (HCoV-NL63, HCoV-OC43, and HCoV-229E): results from the Flu Watch cohort study.
, Wellcome Open Res, Vol: 5, ISSN: 2398-502XBackground: There is currently a pandemic caused by the novel coronavirus SARS-CoV-2. The intensity and duration of this first and second waves in the UK may be dependent on whether SARS-CoV-2 transmits more effectively in the winter than the summer and the UK Government response is partially built upon the assumption that those infected will develop immunity to reinfection in the short term. In this paper we examine evidence for seasonality and immunity to laboratory-confirmed seasonal coronavirus (HCoV) from a prospective cohort study in England. Methods: In this analysis of the Flu Watch cohort, we examine seasonal trends for PCR-confirmed coronavirus infections (HCoV-NL63, HCoV-OC43, and HCoV-229E) in all participants during winter seasons (2006-2007, 2007-2008, 2008-2009) and during the first wave of the 2009 H1N1 influenza pandemic (May-Sep 2009). We also included data from the pandemic and 'post-pandemic' winter seasons (2009-2010 and 2010-2011) to identify individuals with two confirmed HCoV infections and examine evidence for immunity against homologous reinfection. Results: We tested 1,104 swabs taken during respiratory illness and detected HCoV in 199 during the first four seasons. The rate of confirmed HCoV infection across all seasons was 390 (95% CI 338-448) per 100,000 person-weeks; highest in the Nov-Mar 2008/9 season at 674 (95%CI 537-835) per 100,000 person-weeks. The highest rate was in February at 759 (95% CI 580-975) per 100,000 person-weeks. Data collected during May-Sep 2009 showed there was small amounts of ongoing transmission, with four cases detected during this period. Eight participants had two confirmed infections, of which none had the same strain twice. Conclusion: Our results provide evidence that HCoV infection in England is most intense in winter, but that there is a small amount of ongoing transmission during summer periods. We found some evidence of immunity against homologous reinfection.
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Journal articleGupta RK, Lipman M, Jackson C, et al., 2019,
Quantitative interferon gamma release assay and tuberculin skin test Results to predict incident tuberculosis: a prospective cohort study.
, American Journal of Respiratory and Critical Care Medicine, Vol: 208, Pages: 984-991, ISSN: 1073-449XRATIONALE: Development of diagnostic tools with improved predictive value for tuberculosis (TB) is a global research priority. OBJECTIVES: We evaluated whether implementing higher diagnostic thresholds than currently recommended for QuantiFERON Gold-in-Tube (QFT-GIT), T-SPOT.TB and the tuberculin skin test (TST) might improve prediction of incident TB. METHODS: Follow-up of a UK cohort of 9,610 adult TB contacts and recent migrants was extended by re-linkage to national TB surveillance records (median follow-up 4.7 years). Incidence rates and rate ratios, sensitivities, specificities and predictive values for incident TB were calculated according to ordinal strata for quantitative results of QFT-GIT, T-SPOT.TB and TST (with adjustment for prior BCG). MEASUREMENTS AND MAIN RESULTS: For all tests, incidence rates and rate ratios increased with the magnitude of the test result (p<0.0001). Over three years' follow-up, there was a modest increase in positive predictive value (PPV) with the higher thresholds (3.0% for QFT-GIT ≥0.35 IU/mL vs. 3.6% for ≥4.00 IU/mL; 3.4% for T-SPOT.TB ≥5 spots vs. 5.0% for ≥50 spots; and 3.1% for BCG-adjusted TST ≥5mm vs. 4.3% for ≥15mm). As thresholds increased, sensitivity to detect incident TB waned for all tests (61.0% for QFT-GIT ≥0.35 IU/mL vs. 23.2% for ≥4.00 IU/mL; 65.4% for T-SPOT.TB ≥5 spots vs. 27.2% for ≥50 spots; 69.7% for BCG-adjusted TST ≥5mm vs. 28.1% for ≥15mm). CONCLUSIONS: Implementation of higher thresholds for QFT-GIT, T-SPOT.TB and TST modestly increases PPV for incident TB, but markedly reduces sensitivity. Novel biomarkers or validated multivariable risk algorithms are required to improve prediction of incident TB. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Conference paperPark M, Dave D, Russell G, et al., 2019,
FDG-PET/CT APPEARANCES IN MDR-TB PATIENTS WITH RESIDUAL CT ABNORMALITIES
, Winter Meeting of the British-Thoracic-Society, Publisher: BMJ PUBLISHING GROUP, Pages: A69-A70, ISSN: 0040-6376 -
Conference paperChaplin E, Rervitt O, Ward S, et al., 2019,
S5 Changing the shape of rehabilitation: breathlessness rehabilitation
, British Thoracic Society Winter Meeting 2019, QEII Centre, Broad Sanctuary, Westminster, London SW1P 3EE, 4 to 6 December 2019, Programme and Abstracts, Publisher: BMJ Publishing Group Ltd and British Thoracic Society -
Journal articleBerrocal-Almanza LC, Harris R, Lalor MK, et al., 2019,
Effectiveness of pre-entry active tuberculosis and post-entry latent tuberculosis screening in new entrants to the UK: a retrospective, population-based cohort study.
, Lancet Infectious Diseases, Vol: 19, Pages: 1191-1201, ISSN: 1473-3099BACKGROUND: Evaluating interventions that might lead to a reduction in tuberculosis in high-income countries with a low incidence of the disease is key to accelerate progress towards its elimination. In such countries, migrants are known to contribute a large proportion of tuberculosis cases to the burden. We assessed the effectiveness of screening for active tuberculosis before entry to the UK and for latent tuberculosis infection (LTBI) post-entry for reduction of tuberculosis in new-entrant migrants to the UK. Additionally, we investigated the effect of access to primary care on tuberculosis incidence in this population. METHODS: We did a retrospective, population-based cohort study of migrants from 66 countries who were negative for active tuberculosis at pre-entry screening between Jan 1, 2011, and Dec 31, 2014, and eligible for LTBI screening. We used record linkage to track their first contact with primary care, uptake of LTBI screening, and development of active tuberculosis in England, Wales, and Northern Ireland. To assess the effectiveness of the pre-entry screening programme, we identified a control group of migrants who were not screened for active tuberculosis using the specific code for new entrants to the UK registering in primary care within the National Health Service patient registration data system. Our primary outcome was development of active tuberculosis notified to the National Enhanced Tuberculosis Surveillance System. FINDINGS: Our cohort comprised 224 234 migrants who were screened for active tuberculosis before entry to the UK and a control group of 118 738 migrants who were not. 103 990 (50%) migrants who were screened for active tuberculosis registered in primary care; all individuals in the control group were registered in primary care. 1828 tuberculosis cases were identified during the cohort time, of which 31 were prevalent. There were 26 incident active tuberculosis cases in migrants with no evidence of primary care registration, an
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Conference paperGupta R, Lipman M, Jackson C, et al., 2019,
Do higher quantitative interferon gamma release assay or tuberculin skin test results help to predict incident tuberculosis? Data from the UK PREDICT study
, International Congress of the European-Respiratory-Society (ERS), Publisher: EUROPEAN RESPIRATORY SOC JOURNALS LTD, ISSN: 0903-1936- Author Web Link
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Journal articleYang Y, Walker TM, Walker AS, et al., 2019,
DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis
, Bioinformatics, Vol: 35, Pages: 3240-3249, ISSN: 1367-4803MotivationResistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages.ResultsWe used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space.Availability and implementationThe details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php.
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