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      Detection of SARS-CoV-2 infection in the general population by three prevailing rapid antigen tests: cross-sectional diagnostic accuracy study


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          Rapid antigen diagnostic tests (Ag-RDTs) are the most widely used point-of-care tests for detecting SARS-CoV-2 infection. Since the accuracy may have altered by changes in SARS-CoV-2 epidemiology, indications for testing, sampling and testing procedures, and roll-out of COVID-19 vaccination, we evaluated the performance of three prevailing SARS-CoV-2 Ag-RDTs.


          In this cross-sectional study, we consecutively enrolled individuals aged >16 years presenting for SARS-CoV-2 testing at three Dutch public health service COVID-19 test sites. In the first phase, participants underwent either BD-Veritor System (Becton Dickinson), PanBio (Abbott), or SD-Biosensor (Roche Diagnostics) testing with routine sampling procedures. In a subsequent phase, participants underwent SD-Biosensor testing with a less invasive sampling method (combined oropharyngeal-nasal [OP-N] swab). Diagnostic accuracies were assessed against molecular testing.


          Six thousand nine hundred fifty-five of 7005 participants (99%) with results from both an Ag-RDT and a molecular reference test were analysed. SARS-CoV-2 prevalence and overall sensitivities were 13% (188/1441) and 69% (129/188, 95% CI 62–75) for BD-Veritor, 8% (173/2056) and 69% (119/173, 61–76) for PanBio, and 12% (215/1769) and 74% (160/215, 68–80) for SD-Biosensor with routine sampling and 10% (164/1689) and 75% (123/164, 68–81) for SD-Biosensor with OP-N sampling. In those symptomatic or asymptomatic at sampling, sensitivities were 72–83% and 54–56%, respectively. Above a viral load cut-off (≥5.2 log 10 SARS-CoV-2 E-gene copies/mL), sensitivities were 86% (125/146, 79–91) for BD-Veritor, 89% (108/121, 82–94) for PanBio, and 88% (160/182, 82–92) for SD-Biosensor with routine sampling and 84% (118/141, 77–89) with OP-N sampling. Specificities were >99% for all tests in most analyses. Sixty-one per cent of false-negative Ag-RDT participants returned for testing within 14 days (median: 3 days, interquartile range 3) of whom 90% tested positive.


          Overall sensitivities of three SARS-CoV-2 Ag-RDTs were 69–75%, increasing to ≥86% above a viral load cut-off. The decreased sensitivity among asymptomatic participants and high positivity rate during follow-up in false-negative Ag-RDT participants emphasise the need for education of the public about the importance of re-testing after an initial negative Ag-RDT should symptoms develop. For SD-Biosensor, the diagnostic accuracy with OP-N and deep nasopharyngeal sampling was similar; adopting the more convenient sampling method might reduce the threshold for professional testing.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12916-022-02300-9.

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          Nextstrain: real-time tracking of pathogen evolution

          Abstract Summary Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike. Availability and implementation All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.
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            Data, disease and diplomacy: GISAID's innovative contribution to global health

            Abstract The international sharing of virus data is critical for protecting populations against lethal infectious disease outbreaks. Scientists must rapidly share information to assess the nature of the threat and develop new medical countermeasures. Governments need the data to trace the extent of the outbreak, initiate public health responses, and coordinate access to medicines and vaccines. Recent outbreaks suggest, however, that the sharing of such data cannot be taken for granted – making the timely international exchange of virus data a vital global challenge. This article undertakes the first analysis of the Global Initiative on Sharing All Influenza Data as an innovative policy effort to promote the international sharing of genetic and associated influenza virus data. Based on more than 20 semi‐structured interviews conducted with key informants in the international community, coupled with analysis of a wide range of primary and secondary sources, the article finds that the Global Initiative on Sharing All Influenza Data contributes to global health in at least five ways: (1) collating the most complete repository of high‐quality influenza data in the world; (2) facilitating the rapid sharing of potentially pandemic virus information during recent outbreaks; (3) supporting the World Health Organization's biannual seasonal flu vaccine strain selection process; (4) developing informal mechanisms for conflict resolution around the sharing of virus data; and (5) building greater trust with several countries key to global pandemic preparedness.
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              STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies

              Incomplete reporting has been identified as a major source of avoidable waste in biomedical research. Essential information is often not provided in study reports, impeding the identification, critical appraisal, and replication of studies. To improve the quality of reporting of diagnostic accuracy studies, the Standards for Reporting Diagnostic Accuracy (STARD) statement was developed. Here we present STARD 2015, an updated list of 30 essential items that should be included in every report of a diagnostic accuracy study. This update incorporates recent evidence about sources of bias and variability in diagnostic accuracy and is intended to facilitate the use of STARD. As such, STARD 2015 may help to improve completeness and transparency in reporting of diagnostic accuracy studies.

                Author and article information

                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                24 February 2022
                24 February 2022
                : 20
                : 97
                [1 ]GRID grid.7692.a, ISNI 0000000090126352, Julius Center for Health Sciences and Primary Care, , University Medical Center Utrecht, Utrecht University, ; Utrecht, The Netherlands
                [2 ]GRID grid.31147.30, ISNI 0000 0001 2208 0118, Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), ; Bilthoven, The Netherlands
                [3 ]GRID grid.7692.a, ISNI 0000000090126352, Cochrane Netherlands, , University Medical Center Utrecht, Utrecht University, ; Utrecht, The Netherlands
                [4 ]GRID grid.413711.1, ISNI 0000 0004 4687 1426, Microvida Laboratory for Medical Microbiology, , Amphia Hospital, ; Breda, The Netherlands
                [5 ]Microvida Laboratory for Medical Microbiology, Bravis Hospital, Roosendaal, The Netherlands
                [6 ]Public Health Service West-Brabant, Breda, The Netherlands
                [7 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Viroscience, Erasmus MC, ; Rotterdam, The Netherlands
                [8 ]Public Health Service Rotterdam-Rijnmond, Rotterdam, The Netherlands
                [9 ]GRID grid.452600.5, ISNI 0000 0001 0547 5927, Laboratory of Medical Microbiology and Infectious Diseases, , Isala Hospital, ; Zwolle, The Netherlands
                [10 ]Public Health Service IJsselland, Zwolle, The Netherlands
                Author information
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                : 10 January 2022
                : 14 February 2022
                Funded by: FundRef http://dx.doi.org/10.13039/501100002999, Ministerie van Volksgezondheid, Welzijn en Sport;
                Research Article
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                © The Author(s) 2022

                sars-cov-2,covid-19,rapid antigen tests,diagnostic accuracy
                sars-cov-2, covid-19, rapid antigen tests, diagnostic accuracy


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