Optimising COVID-19 Episode Identification Using Serology and PCR/Rapid Antigen Testing: Insights from the BRACE Trial

Optimising COVID-19 Episode Identification Using Serology and PCR/Rapid Antigen Testing: Insights from the BRACE Trial | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var 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ORCID Profile Julio Croda , View ORCID Profile Margareth Pretti Dalcolmo , View ORCID Profile Andrew Davidson , Glauce dos Santos , View ORCID Profile Kaya Gardiner , View ORCID Profile Amanda Gwee , View ORCID Profile Bruno Araújo Jardim , View ORCID Profile Marcus Vinícius Guimaraes Lacerda , View ORCID Profile Michaela Lucas , View ORCID Profile David J Lynn , View ORCID Profile Laurens Manning , View ORCID Profile Helen S Marshall , View ORCID Profile Kirsten P Perrett , View ORCID Profile Cristina Prat-Aymerich , View ORCID Profile Marco AM Puga , View ORCID Profile Peter C Richmond , View ORCID Profile Jesús Rodríguez-Baño , View ORCID Profile Ushma Wadia , View ORCID Profile Adilia Warris , View ORCID Profile Nicholas J Wood , View ORCID Profile Nigel Curtis , View ORCID Profile Nicole L Messina , the BRACE Trial Consortium Group doi: https://doi.org/10.1101/2025.03.12.25323795 Ellie McDonald a Infectious Diseases Group, Murdoch Children’s Research Institute , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ellie McDonald Laure F Pittet a Infectious Diseases Group, Murdoch Children’s Research Institute , Parkville, Victoria, Australia b Department of Paediatrics, The University of Melbourne , Parkville, Victoria, Australia c Immunology, Vaccinology, and Infectious Diseases Unit, Department of Paediatrics , Gynaecology and Obsterics, Faculty of Medicine, University of Geneva and University Hospitals of Geneva , Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laure F Pittet Marc Bonten d European Clinical Research Alliance on Infectious Diseases , Utrecht, the Netherlands e Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University , the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anthony Byrne f St Vincent’s Hospital , Heart Lung Stream, Darlinghurst, Sydney Australia and The University of New South Wales, Faculty of Medicine, St Vincent’s Clinical School , Sydney Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anthony Byrne John Campbell g Exeter Collaboration for Academic Primary Care, University of Exeter Medical School , Exeter, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John Campbell Julio Croda h Department of Epidemiology of Microbial Diseases, Yale School of Public Health , New Haven, CT, USA i Fiocruz Mato Grosso do Sul , Fundação Oswaldo Cruz, Campo Grande, Brazil j Universidade Federal de Mato Grosso do Sul , Campo Grande, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julio Croda Margareth Pretti Dalcolmo k Centro de Referência Professor Hélio Fraga, ENSP/FIOCRUZ (Fundação Oswaldo Cruz) , Rio de Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Margareth Pretti Dalcolmo Andrew Davidson l Melbourne Children’s Trial Centre, Murdoch Children’s Research Institute , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew Davidson Glauce dos Santos k Centro de Referência Professor Hélio Fraga, ENSP/FIOCRUZ (Fundação Oswaldo Cruz) , Rio de Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kaya Gardiner a Infectious Diseases Group, Murdoch Children’s Research Institute , Parkville, Victoria, Australia m Research Operations, The Royal Children’s Hospital Melbourne , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kaya Gardiner Amanda Gwee b Department of Paediatrics, The University of Melbourne , Parkville, Victoria, Australia n Infectious Diseases, Royal Children’s Hospital Melbourne , Parkville, Victoria, Australia o Antimicrobials Group, Murdoch Children’s Research Institute , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Amanda Gwee Bruno Araújo Jardim p Institute of Clinical Research Carlos Borborema, Doctor Heitor Vieira Dourado Tropical Medicine Foundation , Manaus, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bruno Araújo Jardim Marcus Vinícius Guimaraes Lacerda q Fundação de Medicina Tropical Doutor Heitor Vieira Dourado , Manaus, Brazil r Instituto Leônidas & Maria Deane, Oswaldo Cruz Foundation, Ministry of Health , Manaus, Brazil s University of Texas Medical Branch , Galveston, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marcus Vinícius Guimaraes Lacerda Michaela Lucas t Department of Immunology , Pathwest, Queen Elizabeth II Medical Centre , Nedlands, Western Australia, Australia u Department of Immunology, Perth Children’s Hospital , Nedlands, Western Australia, Australia v Department of Immunology, Sir Charles Gairdner Hospital , Nedlands, Western Australia, Australia w School of Medicine, University of Western Australia , Perth, Western Australia, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michaela Lucas David J Lynn x Flinders Health and Medical Research Institute, Flinders University , Bedford Park, South Australia , Australia y Precision Medicine Theme, South Australian Health and Medical Research Institute , Adelaide, South Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David J Lynn Laurens Manning w School of Medicine, University of Western Australia , Perth, Western Australia, Australia z Department of Infectious Diseases, Fiona Stanley Hospital , Murdoch, Western Australia, Australia aa Wesfarmers Centre for Vaccines and Infectious Diseases, The Kids Research Institute, Nedlands , Western Australia, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laurens Manning Helen S Marshall bb Adelaide Medical School and Robinson Research Institute, The University of Adelaide and the Women’s and Children’s Research Centre , Women’s and Children’s Health Network, Adelaide, SA, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Helen S Marshall Kirsten P Perrett b Department of Paediatrics, The University of Melbourne , Parkville, Victoria, Australia cc Department of Allergy and Immunology, Royal Children’s Hospital Melbourne , Parkville, Victoria, Australia dd Population Allergy Group, Murdoch Children’s Research Institute, Parkville , Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kirsten P Perrett Cristina Prat-Aymerich d European Clinical Research Alliance on Infectious Diseases , Utrecht, the Netherlands e Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University , the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cristina Prat-Aymerich Marco AM Puga i Fiocruz Mato Grosso do Sul , Fundação Oswaldo Cruz, Campo Grande, Brazil j Universidade Federal de Mato Grosso do Sul , Campo Grande, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marco AM Puga Peter C Richmond w School of Medicine, University of Western Australia , Perth, Western Australia, Australia aa Wesfarmers Centre for Vaccines and Infectious Diseases, The Kids Research Institute, Nedlands , Western Australia, Australia ee Department of Immunology and General Paediatrics, Perth Children’s Hospital, Nedlands , Western Australia, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Peter C Richmond Jesús Rodríguez-Baño ff Division of Infectious Diseases and Microbiology, Department of Medicine, Hospital Universitario Virgen Macarena, University of Seville , Biomedicines Institute of Seville-Consejo Superior de Investigaciones Científicas, Seville, Spain gg CIBER de Enfermedades Infecciosas, Instituto de Salud Carloss III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jesús Rodríguez-Baño Ushma Wadia w School of Medicine, University of Western Australia , Perth, Western Australia, Australia aa Wesfarmers Centre for Vaccines and Infectious Diseases, The Kids Research Institute, Nedlands , Western Australia, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ushma Wadia Adilia Warris hh Department of Infectious Diseases, Great Ormond Street Hospital , London, UK ii Medical Research Council Centre for Medical Mycology, Department of Biosciences, Faculty of Health and Life Sciences, University of Exeter , Exeter, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adilia Warris Nicholas J Wood jj Faculty of Medicine and Health, University of Sydney , Sydney, New South Wales, Australia kk National Centre for Immunisation Research and Surveillance of Vaccine Preventable Disease , Westmead, New South Wales, Australia ll Sydney Children’s Hospital Network , Westmead, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicholas J Wood Nigel Curtis a Infectious Diseases Group, Murdoch Children’s Research Institute , Parkville, Victoria, Australia b Department of Paediatrics, The University of Melbourne , Parkville, Victoria, Australia n Infectious Diseases, Royal Children’s Hospital Melbourne , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nigel Curtis Nicole L Messina a Infectious Diseases Group, Murdoch Children’s Research Institute , Parkville, Victoria, Australia b Department of Paediatrics, The University of Melbourne , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicole L Messina For correspondence: nicole.messina{at}unimelb.edu.au Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Accurately identifying COVID-19 episodes was crucial during the pandemic for evaluating interventions. Results from diagnostic tools like PCR, rapid antigen test (RAT) and serology are affected by factors such as timing of tests and vaccination status. The BRACE trial developed an algorithm integrating these diagnostic tools for illness episode classification. Methods In the BRACE trial, 3988 participants reported 5512 febrile/respiratory illness episodes and provided longitudinal blood samples over one year. SARS-CoV-2 diagnosis relied on a three-component algorithm: (1) a serology algorithm assessing anti-SARS-CoV-2 nucleocapsid antibody seroconversion, (2) a PCR/RAT algorithm, and (3) an episode interpretation algorithm combining serology and PCR/RAT results to categorise episodes as COVID-19, Not COVID-19 or Uncertain. The algorithms accounted for vaccination status and timing of testing relative to symptom onset to refine episode classifications. Results Of 5512 illness episodes, 890 (16%) were classified as COVID-19, 3852 (70%) as Not COVID-19, and 770 (14%) as Uncertain. Compared to relying solely on PCR/RAT results, integrating serology in the algorithm reduced the proportion of Uncertain classifications by more than half. Among the COVID-19 episodes, 89% were identified by positive PCR/RAT results, and the remaining 11% (with missing or negative PCR/RAT tests) were identified by serology. Discordance between PCR/RAT and serology occurred in 13% of episodes. Conclusion An algorithm integrating PCR/RAT and serology results in the context of test timing and vaccine status enabled the accurate identification of COVID-19 episodes and minimised the number of episodes that would otherwise have been classified as Uncertain. Background Accurately identifying COVID-19 episodes was vital for evaluating interventions in the pandemic. The performance of diagnostic tests for SARS-CoV-2, such as respiratory swab testing and serology, is influenced by the timing of testing relative to symptom onset, as well as prior COVID-19 vaccinations. In the BRACE trial, comprehensive symptom data were collected alongside SARS-CoV-2 testing.[ 1 – 3 ] Here, we describe the algorithm developed during the BRACE trial, intended to more precisely identify COVID-19 episodes using illness episode data and diagnostic test results. To account for test timing in relation to symptom onset and any effect of COVID-19 vaccination, a three-component interpretation algorithm was developed. This incorporated serological result interpretation, PCR/rapid antigen test (RAT) respiratory swab test interpretation, and finally episode classification to COVID-19, Not COVID-19 or Uncertain categories. In addition to evaluating the sensitivity and specificity of PCR/RAT testing compared to serology for SARS-CoV-2, and the frequency of discordant results, we assessed the impact of each component of the algorithm on episode classification. Methods Participants and study design The BRACE trial ( NCT04327206 ) was a phase 3 international randomised controlled trial to assess the impact of BCG vaccination on the prevalence of COVID-19 among healthcare workers.[ 1 – 3 ] Trial outcomes relied on accurate ascertainment of the onset of a participant’s first episode of COVID-19. The trial was designed early in the pandemic, with recruitment starting in March 2020. This study included the 3988 participants recruited to the BRACE trial from May 2020 to April 2021 from Australia, the Netherlands, Spain, the UK and Brazil. Selection of COVID-19 symptoms to be collected and diagnostic tests for SARS-CoV-2 were informed by best practice and government policies at the time. Illness episode ascertainment Recent illness symptoms were ascertained weekly via a custom-built smartphone app or direct communication (phone call or text). During illness episodes, symptoms were recorded daily, and participants were prompted to undergo SARS-CoV-2 respiratory swab testing using PCR (via government testing centres) or RAT (self-administered). Additionally, more comprehensive questionnaires were administered at the start of the trial and quarterly throughout follow-up. These collected information about COVID-19-specific vaccinations (date and vaccine type). Blood samples were collected at baseline and 3, 6, 9 and 12 months post-randomisation to measure anti-SARS-CoV-2 nucleocapsid (NCP) antibodies using the Roche Cobas Elecsys anti-SARS-CoV-2 assay [ 4 , 5 ] at the Victorian Infectious Diseases Reference Laboratory. For seroconversion, a cut-off index (COI) of ≥1.0 was used as per manufacturer’s instructions.[ 6 , 7 ] Serological interpretation algorithm The serology algorithm produced one of four possible outcomes: Seroconversion, No seroconversion, Indeterminate or No result (where a blood sample was unavailable). For each illness episode, the most recent blood sample collected at least seven days prior to symptom onset (pre-episode sample), and the first and second blood samples collected after symptom onset were identified. The NCP serology result from these samples were used in the serological interpretation algorithm. The algorithm considered the timing of blood sample in relation to the illness episode, any prior administration of the CoronaVac (Sinovac Biotech) COVID-19 vaccine (a whole inactivated virus vaccine which can induce anti-NCP antibodies) and change in anti-NCP antibody titre (in cases of positive-to-positive serology results). A detailed description of the serological algorithm is provided in Supplementary Figure 1. PCR/RAT interpretation algorithm The PCR/RAT interpretation algorithm yielded three possible outcomes: Positive, Negative, or No result. Both PCR and RAT positive results were considered confirmation of SARS-CoV-2 infection if the timing of the test fell within defined symptomatic illness parameters. Positive PCR results were considered valid if the sample was tested within three days prior to 10 days (RAT) or 21 days (PCR) after the start, or within seven days from the end, of an illness episode. A negative PCR was considered ‘Negative’, but a negative RAT was classified as ‘No result’ due to the lower sensitivity of RAT testing.[ 8 ] A detailed description of the PCR/RAT interpretation algorithm is provided in Supplementary Figure 2. Episode interpretation algorithm The episode interpretation algorithm produced three possible classifications for each illness episode: COVID-19, Not COVID-19 or Uncertain (which included episodes for which results were missing, or indeterminate). The results of the serological and PCR/RAT algorithms were combined to categorise illness episodes using the episode interpretation algorithm, detailed in Figure 1 . Additional conditions were then applied which evaluated other COVID-19 tests and episode results within the same timeframe. Download figure Open in new tab Figure 1. Episode interpretation algorithm: the detail Results Of the 3988 participants, 2559 reported at least one episode of illness in the year post-randomisation, with a total of 5512 episodes. Of the 5512 episodes, 890 (16%) were classified as COVID-19, 3852 (70%) as Not COVID-19 and 770 (14%) as Uncertain ( Figure 1 ). The participant flow from the combined PCR/RAT result and from the pre-episode NCP serology to the final episode classification are shown in Figure 2 and Supplementary Figure 3 respectively. Download figure Open in new tab Figure 2. Episode interpretation outcome Of the 890 episodes defined as COVID-19, 792 (89%) had a positive PCR or RAT test result. Serology results were concordant (seroconversion) in 375 (47.4%) episodes, discordant (no seroconversion) in 38 (4.8%), and Indeterminate or No result in 379 (47.9%) ( Figure 2 ). The remaining 98 COVID-19 episodes comprised 56 (6.3%) with a missing PCR/RAT result and 42 (4.7%) with a negative PCR result (Supplementary Figure 4). The discordant PCR/RAT and serology results were not attributable to post-episode NCP titres being just above the threshold for seroconversion ( Figure 3 ). The post- episode NCP titres in relation to the time interval between the episode start and post-episode blood collection for the seroconverted PCR negative and PCR/RAT positive episodes are shown in Supplementary Figure 5. Median post-episode NCP titres were higher in seroconverting episodes with negative PCR results compared to those with positive PCR/RAT results (median 62.2, IQR: 15.9– 122.9 vs. 41.3, IQR: 14.3–101.4; p=0.3). Relevant to this, median time between episode onset and post-episode serological testing was higher in the negative PCR episode group compared to the positive PCR/RAT group (median 76.5 days, IQR: 33-102 days vs. 61 days, IQR: 37-82 days; p=0.1). Download figure Open in new tab Figure 3. Pre and post NCP titre serology results by PCR/RAT and episode results There were 305 episodes that had a negative RAT test. Of the RAT-negative episodes without a PCR result, 11/179 (6.2%) were categorised as COVID-19 based on seroconversion (Supplementary Figure 6). In comparison, of the episodes without a PCR result, 56/858 (7.0%) were categorised as COVID-19 based on seroconversion ( Figure 3 ). Among the 3852 episodes classified as Not COVID-19 by the final episode algorithm, 3050 (77%) had a negative PCR result ( Figure 2 & Supplementary Figure 3). Of these, 1622 (53.2%) had concordant negative serology and PCR results ( Figure 2 ). Additionally, 1351 (44.3%) episodes had a missing NCP serology result, but a negative PCR result. The remaining 77 (2.5%) episodes were discordant (seroconversion with a negative PCR result). However, the positive NCP serology result in these cases was attributed to participants’ other PCR- or RAT-positive episodes within the same timeframe. For the remaining 802 (20%) Not COVID-19 episodes with a missing PCR result, 744 had no seroconversion between pre- and post-episode blood samples. These episodes would have been classified as Uncertain if the trial had relied on PCR/RAT results only. In the remaining 58 (7.2%) episodes, participants seroconverted, but the episode algorithm attributed this to another PCR- or RAT-positive episode. Of the 770 episodes classified as Uncertain in the final episode result, 738 (95.8%) had both missing or indeterminant serology and PCR/RAT results. Of the remaining 32 episodes, participants seroconverted but the episodes were categorised as Uncertain because the participants had multiple episodes with missing PCR/RAT results within the same seroconversion timeframe. This precluded the attribution of the COVID-19 diagnosis to one of the episodes. At the participant level, Uncertain result episodes were similarly distributed between every combination of COVID-19 and Not COVID-19 episodes ( Figure 4 ). The sequential distribution of COVID-19, Not COVID-19 and Uncertain result episodes for each participant demonstrates the progression from algorithm processing to the final trial outcomes. It highlights the points at which participants were censored, which occurred at one of two events: their first COVID-19 episode or their first episode with an Uncertain result ( Figure 5 ). Download figure Open in new tab Figure 4. Overall episode interpretation Download figure Open in new tab Figure 5. Sequential episode interpretation: COVID-19, Not COVID-19 and Uncertain outcome episodes per participant Discussion The algorithms developed for the BRACE trial provided a novel and robust method for the classification of illness episodes and identification of COVID-19 cases. Unlike other trials that relied exclusively on PCR/RAT [ 9 – 11 ] or serological testing,[ 12 ] often classifying cases individually,[ 13 , 14 ] our three-step algorithm combined these test results with real-world considerations. These included timing of testing relative to symptom onset, variations in the sensitivity and specificity of PCR/RAT and serology, together with the effects of COVID-19 vaccination on serological results.[ 15 ] Critically, we accounted for the lower reliability of a negative RAT result due to its lower sensitivity and the impact of CoronaVac -induced anti-NCP antibodies.[ 8 ] Post-hoc analysis showed that less than 10% of participants with RAT-negative, PCR-missing episodes seroconverted and were classified as COVID-19, while nearly 15% of participants with RAT-negative, PCR-missing episodes were excluded from primary analysis due to missing serological results. Discordance between PCR/RAT and serology test results was observed in a small proportion (11%) of episodes. Possible explanations for the 38 (5%) PCR/RAT-positive episodes without seroconversion include mild infections that did not elicit a systemic response, early waning of immunity, limitations in the sensitivity of serology, or false positive PCR/RAT. Possible explanations for the 42 (6%) PCR- negative episodes with seroconversion include inadequate swabbing, technical issues, participant reporting errors, and undetected asymptomatic infections during the interval between blood samples. Serological testing proved especially valuable when PCR/RAT results were unavailable. Negative serology reclassified 744 episodes with missing PCR/RAT results as Not COVID-19 by demonstrating an absence of seroconversion. Additionally, serology alone identified 11% (98/890) of COVID-19 episodes in which the PCR/RAT result was negative or missing. The algorithm was particularly beneficial in mitigating the impact of incomplete data by reducing the proportion of episodes for which classification would not have been possible (and therefore classified as Uncertain) by more than half, from 30% (1628/5512) to 14% (770/5512). The addition of serology to PCR/RAT testing involves considerable cost and resources, including staff, consumables, shipment, laboratory assays and data management. However, the benefits included in- person follow-ups that encouraged trial retention, enhanced data accuracy, clarified discordances and the facilitation of additional research opportunities, such as additional immunological studies.[ 16 – 21 ] Our analyses have some limitations. First, our definition of symptomatic COVID-19 relied on the original case criteria, excluding non-febrile episodes without respiratory symptoms. Second, the timing and type of PCR/RAT testing were not standardised and varied depending on individual settings. Third, serological testing was done quarterly rather than at a consistent interval following illness episodes, which may have affected the detection of seroconversion. Finally, a proportion of illness episodes had incomplete data. However, it is among the COVID-19-related trials with the most complete data available. Our three-component algorithm developed in the BRACE trial has potential for adaption to diverse epidemiological scenarios beyond COVID-19. Infectious diseases such as influenza or respiratory syncytial virus also require accurate longitudinal monitoring to track transmission, assess vaccine efficacy, and evaluate intervention and treatment outcomes. In summary, despite robust data collection efforts, COVID-19 trials faced practical challenges, particularly during the early pandemic. Testing availability and participant adherence contributed to the 14% of episodes still classified as missing. Adding serological testing demanded substantial time, financial resources, and participant effort but effectively halved the number of unclassified episodes. Our study underscores the important role of algorithms in achieving comprehensive classification, particularly in complex real-world study settings. Data Availability Deidentified participant data and data dictionary are available to others on request and on completion of a signed data access agreement. Requests can be made in writing to braceresearch{at}mcri.edu.au. Authors’ Contributions EM, NC and NM were responsible for the original draft of this paper, with EM responsible for Visualization and analysis. All authors contributed to administration and/or investigation in the BRACE trial, and all were involved in the review and editing of this manuscript. NC, NM, LP and KG were responsible for Conceptualization, Funding acquisition and Supervision. NC, NM, LP and EM were responsible for Methodology. All authors had final responsibility for the decision to submit for publication. NC and NM attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Declaration of Interest Statements PCR has received investigator-initiated research grants to his institution from Sanofi outside this work and has received institutional funding from GlaxoSmithKline and Sanofi for local and international lectures and from AstraZeneca, Clover Biopharmaceuticals, GlaxoSmithKline, Novavax, Sanofi, and Pfizer for participation in vaccine scientific advisory boards unrelated to this work. He has been an investigator in sponsored, multicentre vaccine trials for AstraZeneca, Moderna, Novavax, Pfizer, and Sanofi with funding to his institution. He is Chief investigator on an investigator led COVID-19 vaccine trial and institution has received funding from the Medical Research Future Fund. HSM has received investigator-initiated research grants to her institution from Sanofi and Pfizer outside this work. She has been an investigator in sponsored, multicentre vaccine trials for Iliad, Seqirus and Pfizer, with funding to her institution. She is a Chief investigator on an investigator led COVID-19 vaccine trial and institution has received funding from the Medical Research Future Fund. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data sharing statement Deidentified participant data and data dictionary are available to others on request and on completion of a signed data access agreement. Requests can be made in writing to braceresearch{at}mcri.edu.au. Ethics Committee approval The study was approved by the Royal Children’s Hospital Melbourne Human Research Ethics Committee (No.62586); the protocol was approved by the ethics committee at each site and all participants provided informed consent. The trial was overseen by a steering committee and a data safety and monitoring board. Funding sources The trial is supported by the Bill & Melinda Gates Foundation [INV017302], the Minderoo Foundation [COV-001], Sarah and Lachlan Murdoch, the Royal Children’s Hospital Foundation [2020-1263 BRACE Trial], Health Services Union NSW, the Peter Sowerby Foundation, SA Health, the Insurance Advisernet Foundation, the NAB Foundation, the Calvert-Jones Foundation, the Modara Pines Charitable Foundation, the UHG Foundation Pty Ltd, Epworth Healthcare and individual donors. The Murdoch Children’s Research Institute (MCRI) leads the BRACE trial across 36 sites in five countries. It is supported by the Victorian Government’s Operational Infrastructure Support Programme. NC is supported by a National Health and Medical Research Council (NHMRC) Investigator Grant (GNT1197117). LFP is supported by the Swiss National Science Foundation (Early Postdoc Mobility Grant, P2GEP3_178155). The funders of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Acknowledgements We thank the trial participants and site personnel who were involved in assisting the trial (detailed in the Supplementary File). AJ Vaccines (Copenhagen) for facilitating BCG vaccine supplies; Devon Freewheelers (UK) for UK transport; Catalent (UK) for drug management; Fiocruz Clinical Research Platform for coordination and monitoring of sites in Brazil; the Pharmacy, Pathology and Immunisation Service teams from The Royal Children’s Hospital Melbourne, and the Orygen Group for their support. Footnotes ↵ † Members of the BRACE trial Consortium are listed in the Supplementary Appendix Email: ellie.mcdonald{at}mcri.edu.au , laure.pittet{at}mcri.edu.au , mbonten{at}umcutrecht.nl , anthony.byrne{at}svha.org.au , jlc80{at}aol.com , juliocroda{at}gmail.com , margarethdalcolmo{at}gmail.com , andrew.davidson{at}mcri.edu.au , glaucedossantos{at}gmail.com , kaya.gardiner{at}rch.org.au , amanda.gwee{at}rch.org.au , brunojardim89{at}hotmail.com , marcuslacerda.br{at}gmail.com , michaela.lucas{at}uwa.edu.au , david.lynn{at}sahmri.com , laurens.manning{at}uwa.edu.au , helen.marshall{at}adelaide.edu.au , kirsten.perrett{at}rch.org.au , cristina.prat{at}ecraid.eu , marco.m.puga{at}gmail.com , peter.richmond{at}uwa.edu.au , jesusrb{at}us.es , ushma.wadia{at}telethonkids.org.au , a.warris{at}exeter.ac.uk , nicholas.wood{at}health.nsw.gov.au , nigel.curtis{at}rch.org.au , nicole.messina{at}unimelb.edu.au Revised to more concisely describe the three-step algorithm used to identify participants COVID-19 illness episodes in the BRACE trial. 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Share Optimising COVID-19 Episode Identification Using Serology and PCR/Rapid Antigen Testing: Insights from the BRACE Trial Ellie McDonald , Laure F Pittet , Marc Bonten , Anthony Byrne , John Campbell , Julio Croda , Margareth Pretti Dalcolmo , Andrew Davidson , Glauce dos Santos , Kaya Gardiner , Amanda Gwee , Bruno Araújo Jardim , Marcus Vinícius Guimaraes Lacerda , Michaela Lucas , David J Lynn , Laurens Manning , Helen S Marshall , Kirsten P Perrett , Cristina Prat-Aymerich , Marco AM Puga , Peter C Richmond , Jesús Rodríguez-Baño , Ushma Wadia , Adilia Warris , Nicholas J Wood , Nigel Curtis , Nicole L Messina , the BRACE Trial Consortium Group medRxiv 2025.03.12.25323795; doi: https://doi.org/10.1101/2025.03.12.25323795 Share This Article: Copy Citation Tools Optimising COVID-19 Episode Identification Using Serology and PCR/Rapid Antigen Testing: Insights from the BRACE Trial Ellie McDonald , Laure F Pittet , Marc Bonten , Anthony Byrne , John Campbell , Julio Croda , Margareth Pretti Dalcolmo , Andrew Davidson , Glauce dos Santos , Kaya Gardiner , Amanda Gwee , Bruno Araújo Jardim , Marcus Vinícius Guimaraes Lacerda , Michaela Lucas , David J Lynn , Laurens Manning , Helen S Marshall , Kirsten P Perrett , Cristina Prat-Aymerich , Marco AM Puga , Peter C Richmond , Jesús Rodríguez-Baño , Ushma Wadia , Adilia Warris , Nicholas J Wood , Nigel Curtis , Nicole L Messina , the BRACE Trial Consortium Group medRxiv 2025.03.12.25323795; doi: https://doi.org/10.1101/2025.03.12.25323795 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Infectious Diseases (except HIV/AIDS) Subject Areas All Articles Addiction Medicine (569) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4442) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1511) Epidemiology (15230) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6610) Geriatric Medicine (668) Health Economics (998) Health Informatics (4542) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15923) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6607) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1146) Occupational and Environmental Health (957) Oncology (3337) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (664) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (712) Psychiatry and Clinical Psychology (5448) Public and Global Health (9238) Radiology and Imaging (2202) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (596) Sexual and Reproductive Health (714) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a01bf6ab6a95c479',t:'MTc3OTc4OTk0OA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();