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Abstract
Antibody testing is crucial for monitoring the evolution of the pandemic, providing a more complete picture of the total number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) than molecular diagnostic testing alone.1 All individuals with SARS-CoV-2-specific antibodies have been exposed to the virus, so antibody testing can highlight differences in past exposure between regions, demographic groups, and occupations.2 Seroprevalence estimates can also be used to estimate the infection fatality rate.3 Dashboards that visualise COVID-19 cases confirmed by diagnostic testing have been pivotal in enabling policy makers and researchers to monitor the pandemic.4 Yet, despite the value of antibody testing, there is no unified resource for seroprevalence estimates. SeroTracker integrates evidence from serosurveillance studies through a live systematic review.5 Each day, published articles (MEDLINE, Embase, Web of Science, and Cochrane), preprints (medRxiv and bioRxiv), government reports, and news articles are reviewed for newly reported SARS-CoV-2 seroprevalence estimates by a team of doctoral and medical students. Across both tabs, users can also filter data by geography, study characteristics (source type, study status, overall risk of bias), population demographics (age, sex, general population, health-care workers), and test information (test type, reported isotypes).
Details
1 Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK; Centre for Health Informatics, Cumming School of Medicine, University of Calgary, AB, Canada
2 Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
3 Centre for Health Informatics, Cumming School of Medicine, University of Calgary, AB, Canada; Schulich School of Engineering, University of Calgary, AB, Canada
4 Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
5 Centre for Evidence-Based Medicine, University of Oxford, Oxford OX3 7DQ, UK; Faculty of Medicine, University of Toronto, Toronto, ON, Canada
6 Department of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill University, Montreal, QC, Canada
7 McGill Interdisciplinary Initiative in Infection and Immunity, Faculty of Medicine, McGill University, Montreal, QC, Canada
8 Centre for Health Informatics, Cumming School of Medicine, University of Calgary, AB, Canada