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      Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: A systematic review and meta-analysis of standardized population-based studies

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      1 , * , , 2 , 2 , 1 , 1 , 3 , 1 , 4 , 2 , 5 , 2 , 6 , 1 , 3 , 1 , 7 , 8 , 9 , 4 , 9 , 9 , 10 , 11 , 11 , 12 , 13 , 14 , 2 , 15 , 16 , 16 , 16 , 17 , 13 , 18 , 2 , 15 , 1 , Unity Studies Collaborator Group
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          Abstract

          Background

          Our understanding of the global scale of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains incomplete: Routine surveillance data underestimate infection and cannot infer on population immunity; there is a predominance of asymptomatic infections, and uneven access to diagnostics. We meta-analyzed SARS-CoV-2 seroprevalence studies, standardized to those described in the World Health Organization’s Unity protocol (WHO Unity) for general population seroepidemiological studies, to estimate the extent of population infection and seropositivity to the virus 2 years into the pandemic.

          Methods and findings

          We conducted a systematic review and meta-analysis, searching MEDLINE, Embase, Web of Science, preprints, and grey literature for SARS-CoV-2 seroprevalence published between January 1, 2020 and May 20, 2022. The review protocol is registered with PROSPERO (CRD42020183634). We included general population cross-sectional and cohort studies meeting an assay quality threshold (90% sensitivity, 97% specificity; exceptions for humanitarian settings). We excluded studies with an unclear or closed population sample frame. Eligible studies—those aligned with the WHO Unity protocol—were extracted and critically appraised in duplicate, with risk of bias evaluated using a modified Joanna Briggs Institute checklist. We meta-analyzed seroprevalence by country and month, pooling to estimate regional and global seroprevalence over time; compared seroprevalence from infection to confirmed cases to estimate underascertainment; meta-analyzed differences in seroprevalence between demographic subgroups such as age and sex; and identified national factors associated with seroprevalence using meta-regression. We identified 513 full texts reporting 965 distinct seroprevalence studies (41% low- and middle-income countries [LMICs]) sampling 5,346,069 participants between January 2020 and April 2022, including 459 low/moderate risk of bias studies with national/subnational scope in further analysis. By September 2021, global SARS-CoV-2 seroprevalence from infection or vaccination was 59.2%, 95% CI [56.1% to 62.2%]. Overall seroprevalence rose steeply in 2021 due to infection in some regions (e.g., 26.6% [24.6 to 28.8] to 86.7% [84.6% to 88.5%] in Africa in December 2021) and vaccination and infection in others (e.g., 9.6% [8.3% to 11.0%] in June 2020 to 95.9% [92.6% to 97.8%] in December 2021, in European high-income countries [HICs]). After the emergence of Omicron in March 2022, infection-induced seroprevalence rose to 47.9% [41.0% to 54.9%] in Europe HIC and 33.7% [31.6% to 36.0%] in Americas HIC. In 2021 Quarter Three (July to September), median seroprevalence to cumulative incidence ratios ranged from around 2:1 in the Americas and Europe HICs to over 100:1 in Africa (LMICs). Children 0 to 9 years and adults 60+ were at lower risk of seropositivity than adults 20 to 29 ( p < 0.001 and p = 0.005, respectively). In a multivariable model using prevaccination data, stringent public health and social measures were associated with lower seroprevalence ( p = 0.02). The main limitations of our methodology include that some estimates were driven by certain countries or populations being overrepresented.

          Conclusions

          In this study, we observed that global seroprevalence has risen considerably over time and with regional variation; however, over one-third of the global population are seronegative to the SARS-CoV-2 virus. Our estimates of infections based on seroprevalence far exceed reported Coronavirus Disease 2019 (COVID-19) cases. Quality and standardized seroprevalence studies are essential to inform COVID-19 response, particularly in resource-limited regions.

          Abstract

          In a systematic review and meta analysis, Isabel Bergeri, Mairead Whelan, Harriet Ware, Lorenzo Subissi, and colleagues study global SARS-CoV-2 seroprevalence, two years into the COVID-19 pandemic.

          Author summary

          Why was this study done?
          • Serosurveys, or studies capturing information on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antibody prevalence, help us understand true rates of infection, vaccination, and indicators of immunity in the population against the virus causing Coronavirus Disease 2019 (COVID-19) and inform public health decision-making.

          • Previous global systematic reviews of seroprevalence have highlighted a lack of standardization in study methods and fewer datasets in some regions like low- and middle-income countries.

          • Recently, in part via WHO’s Unity studies, the quantity and quality of available seroprevalence data has increased, providing the opportunity to understand the true extent of exposure to SARS-CoV-2 and differences by demographic groups, region, and time.

          What did the researchers do and find?
          • We meta-analyzed standardized SARS-CoV-2 seroprevalence studies to estimate the proportion of the global population with antibodies against SARS-CoV-2, the virus causing COVID-19.

          • By September 2021, global SARS-CoV-2 seroprevalence from infection or vaccination was 59.2%, 95% CI [56.1% to 62.2%].

          • Overall seroprevalence rose steeply in 2021 due to infection in some regions (e.g., 26.6% [24.6 to 28.8] to 86.7% [84.6% to 88.5%] in Africa) and vaccination and infection in others (e.g., 9.6% [8.3% to 11.0%] to 95.9% [92.6% to 97.8%] in Europe high-income countries [HICs]). After the emergence of Omicron in March 2022, infection-induced seroprevalence rose to 47.9% [41.0% to 54.9%] in Europe HIC and 33.7% [31.6% to 36.0%] in Americas HIC.

          What do these findings mean?
          • Seroprevalence has increased over time, with heterogeneity in dynamics and data robustness between regions.

          • Estimates of COVID-19 infections based on seroprevalence data far exceed reported cases.

          • It remains important to continue investing in serosurveillance to monitor the COVID-19 pandemic and prepare for future potential emerging viruses.

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          Most cited references63

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

            David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: ResourcesRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                PLOS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                10 November 2022
                November 2022
                : 19
                : 11
                : e1004107
                Affiliations
                [1 ] World Health Organization, Geneva, Switzerland
                [2 ] Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
                [3 ] Epiconcept, Paris, France
                [4 ] World Health Organization, Regional Office for Africa, Brazzaville, Congo
                [5 ] Faculty of Engineering, University of Waterloo, Waterloo, Ontario, Canada
                [6 ] Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
                [7 ] School of Population and Global Health, McGill University, Montreal, Quebec, Canada
                [8 ] World Health Organization, Regional Office for the Eastern Mediterranean, Cairo, Egypt
                [9 ] World Health Organization, Regional Office for South-East Asia, New Delhi, India
                [10 ] World Health Organization, Regional Office for the Western Pacific, Manila, Philippines
                [11 ] World Health Organization Regional Office for Europe, Copenhagen, Denmark
                [12 ] World Health Organization, Regional Office for the Americas (Pan American Health Organization), Washington DC, United States of America
                [13 ] Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
                [14 ] COVID-19 Immunity Task Force Secretariat, McGill University, Montreal, Canada
                [15 ] Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
                [16 ] Division of Infectious Diseases and Medical Microbiology, McGill University Health Centre, Montreal, Quebec, Canada
                [17 ] Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
                [18 ] Department of Critical Care Medicine, University of Calgary, Calgary, Canada
                PLOS Medicine Editorial Board, UNITED STATES
                Author notes

                I have read the journal policy and the authors of this manuscript have the following competing interests: RKA, MW, HW, ZL, XM, CC, MYL, DB, JP, MPC, ML, MS, GRD, NI, CZ, SP, HPR, TY, KCN, DK, SAA, ND, CD, NAD, EL, RKI, ASB, ELB, AS, JC and NB report grants from Canada’s COVID-19 Immunity Task Force through the Public Health Agency of Canada, and the Canadian Medical Association Joule Innovation Fund. RKA, MW, HW, ZL, CC, MYL, NB also report grants from the World Health Organisation and the Robert Koch Institute. RKA reports personal fees from the Public Health Agency of Canada and the Bill and Melinda Gates Foundation Strategic Investment Fund, as well as equity in Alethea Medical (Outside the submitted work). MPC reports grants from McGill Interdisciplinary Initiative in Infection and Immunity and Canadian Institute of Health Research, and personal fees from GEn1E Lifesciences (Outside the submitted work), nplex biosciences (Outside the submitted work), Kanvas biosciences (Outside the submitted work). JP reports grants from MedImmune (Outside the submitted work) and Sanofi-Pasteur (Outside the submitted work), grants and personal fees from Merck (Outside the submitted work) and AbbVie (Outside the submitted work), and personal fees from AstraZeneca (Outside the submitted work). DB reports grants from the World Health Organization, Canadian Institutes of Health Research, Natural Sciences and Engineering Council of Canada (Outside the submitted work), Institute national d excellence en sante et service sociaux (Outside the submitted work), and personal fees from McGill University Health Centre (Outside the submitted work) and Public Health Agency of Canada (Outside the submitted work). CC reports funding from Sanofi Pasteur (Outside of the submitted work). TY reports working for Health Canada as a part-time Senior Policy Analyst with the COVID-19 Testing and Screening Expert Panel, from Nov 2020-Jun 2021 (Outside of the submitted work). TH reports funding recieved from the United States Centers for Disease Control and Prevention for Columbia University (Outside of the submitted work). Author HCL declares receiving funding as a WHO consultant from WHO Solidarity Response Fund and the German Federal Ministry of Health COVID-19 Research and Development.

                ‡ IB, MW, HW, and LS share first authorship on this work. NB, RKA, and MDVK are joint senior authors on this work.

                ¶ Membership of Unity Studies Collaborator Group is provided in S1 Acknowledgments.

                Author information
                https://orcid.org/0000-0002-1204-1753
                https://orcid.org/0000-0002-4895-9304
                https://orcid.org/0000-0003-3869-3060
                https://orcid.org/0000-0001-5147-575X
                https://orcid.org/0000-0003-1138-0937
                https://orcid.org/0000-0002-1925-3943
                https://orcid.org/0000-0002-0101-977X
                https://orcid.org/0000-0002-4165-3062
                https://orcid.org/0000-0003-2004-2251
                https://orcid.org/0000-0002-3343-6359
                https://orcid.org/0000-0002-4005-5183
                https://orcid.org/0000-0003-4127-5871
                https://orcid.org/0000-0002-5816-7589
                https://orcid.org/0000-0002-5966-2303
                https://orcid.org/0000-0001-5528-2517
                https://orcid.org/0000-0002-3615-8710
                https://orcid.org/0000-0002-0395-0381
                https://orcid.org/0000-0002-4867-2063
                https://orcid.org/0000-0003-4232-871X
                https://orcid.org/0000-0001-7883-4484
                https://orcid.org/0000-0003-3526-6338
                Article
                PMEDICINE-D-22-00442
                10.1371/journal.pmed.1004107
                9648705
                36355774
                8c85ad22-49c3-4858-bfe0-c4ec88552c07
                © 2022 Bergeri et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 8 February 2022
                : 12 September 2022
                Page count
                Figures: 4, Tables: 2, Pages: 24
                Funding
                Funded by: WHO COVID-19 Solidarity Response Fund
                Award Recipient :
                Funded by: German Federal Ministry of Health COVID-19 Research and Development Fund
                Award Recipient :
                Funded by: World Health Organisation
                Award Recipient :
                Funded by: Canada’s COVID-19 Immunity Task Force through the Public Health Agency of Canada
                Award ID: 2021-HQ-000056
                Award Recipient :
                Funded by: Canadian Medical Association Joule Innovation Fund
                Award Recipient :
                Funded by: Robert Koch Institute
                Award Recipient :
                This work was supported by WHO (WHO COVID-19 Solidarity Response Fund, to IB. https://covid19responsefund.org/en/; German Federal Ministry of Health COVID-19 Research and Development Fund, to IB; World Health Organisation funding, to RKA), the Public Health Agency of Canada (Canada’s COVID-19 Immunity Task Force through the Public Health Agency of Canada, to RKA, grant number 2021-HQ-000056 https://www.covid19immunitytaskforce.ca/), the Canadian Medical Association (Joule Innovation Fund, to RKA https://joulecma.ca/), and the Robert Koch Institute (funding to RKA https://www.rki.de). IB, LS, AnV, LA, AR, JO, TA, PW, LL, AiV, RP, MVK are employed and receive salaries from WHO (one of the funders of this study), and AN, MV, BC and HCL are WHO consultants. Authors who are members of the SeroTracker Group (led by RKA, including MW, HW, ZL, XM, TY, CC, MYL, JP, MPC, DB, ML, MS, GRD, NI, CZ, SP, HPR, TY, KCN, DK, SAA, ND, CD, NAD, EL, RKI, ASB, ELB, AS, JC) were supported through the aforementioned grants from WHO, Canada’s COVID-19 Immunity Task Force through the Public Health Agency of Canada, the Robert Koch Institute, and the Canadian Medical Association Joule Innovation Fund. WHO had a role in the study design, data collection, data analysis, data interpretation, and the writing of the report. No other funders had any such role.
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                Custom metadata
                Machine-readable data files for dataset 0, sub-dataset 1, and sub-dataset 2 are available on the following Zenodo link: https://doi.org/10.5281/zenodo.6915823. Raw early results submitted via the Unity Study Collaborators initiative are available on the following Zenodo link: https://zenodo.org/communities/unity-sero-2021?page=1&size=20. DOIs and citations for the studies in this Zenodo community is included in Table A to C in S1 Materials. The Python code used for our automated estimate prioritization is available on the following GitHub link: https://github.com/serotracker/iit-backend/blob/8059e9b905395de997f28a1a2dff5def795276ad/app/utils/estimate_prioritization/estimate_prioritization.py.
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