SARS-CoV-2 seroprevalence worldwide: a systematic review and meta-analysis

Published:October 23, 2020DOI:https://doi.org/10.1016/j.cmi.2020.10.020

      Abstract

      Objectives

      COVID-19 has been arguably the most important public health concern worldwide in 2020, and efforts are now escalating to suppress or eliminate its spread. In this study we undertook a meta-analysis to estimate the global and regional seroprevalence rates in humans of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to assess whether seroprevalence is associated with geographical, climatic and/or sociodemographic factors.

      Methods

      We systematically reviewed PubMed, Scopus, Embase, medRxiv and bioRxiv databases for preprints or peer-reviewed articles (up to 14 August 2020). Study eligibility criteria were population-based studies describing the prevalence of anti-SARS-CoV-2 (IgG and/or IgM) serum antibodies. Participants were people from different socioeconomic and ethnic backgrounds (from the general population), whose prior COVID-19 status was unknown and who were tested for the presence of anti-SARS-CoV-2 serum antibodies. We used a random-effects model to estimate pooled seroprevalence, and then extrapolated the findings to the global population (for 2020). Subgroup and meta-regression analyses explored potential sources of heterogeneity in the data, and relationships between seroprevalence and sociodemographic, geographical and/or climatic factors.

      Results

      In total, 47 studies involving 399 265 people from 23 countries met the inclusion criteria. Heterogeneity (I2 = 99.4%, p < 0.001) was seen among studies; SARS-CoV-2 seroprevalence in the general population varied from 0.37% to 22.1%, with a pooled estimate of 3.38% (95%CI 3.05–3.72%; 15 879/399 265). On a regional level, seroprevalence varied from 1.45% (0.95–1.94%, South America) to 5.27% (3.97–6.57%, Northern Europe), although some variation appeared to relate to the serological assay used. The findings suggested an association of seroprevalence with income levels, human development indices, geographic latitudes and/or climate. Extrapolating to the 2020 world population, we estimated that 263.5 million individuals had been exposed or infected at the time of this study.

      Conclusions

      This study showed that SARS-CoV-2 seroprevalence varied markedly among geographic regions, as might be expected early in a pandemic. Longitudinal surveys to continually monitor seroprevalence around the globe will be critical to support prevention and control efforts, and might indicate levels of endemic stability or instability in particular countries and regions.

      Keywords

      Introduction

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      ]. Due to an apparent persistence of antibodies to SARS-CoV-2 (particularly IgG) after viral clearance [
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      Seroprevalence of immunoglobulin M and G antibodies against SARS-CoV-2 in China.
      ], it is expected that serological monitoring and surveillance provide relevant datasets to estimate the cumulative prevalence of SARS-CoV-2 infection/exposure in a population [
      • Xu X.
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      Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study.
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      Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study.
      ].
      Several commercial and in-house immunoassays are being used for the detection of IgG and/or IgM serum antibodies to SARS-CoV-2; these are mainly enzyme-linked immunosorbent assays (ELISAs), chemiluminescence immunoassays (CLIAs) or lateral flow assays (LFIAs) [
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      Evaluation of two automated and three rapid lateral flow immunoassays for the detection of anti-SARS-CoV-2 antibodies.
      ]. The diagnostic specificity and sensitivity of these methods vary and depend on the use of recombinant or purified protein antigens—e.g. spike (S), envelope (E), membrane (M), nucleocapsid (N) or receptor binding domain (RBD) proteins—and the rigor of assay optimization [
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      Neutralizing antibodies against SARS-CoV-2 and other human coronaviruses.
      ].
      Since April 2020, sero-epidemiological studies have been reported from a number of countries most affected by COVID-19, including Brazil, China, France, Germany, Iran, Italy, Spain, England and the USA [
      • Pollán M.
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      • Hernán M.A.
      • Pérez-Olmeda M.
      • et al.
      Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study.
      ,
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      Remarkable variability in SARS-CoV-2 antibodies across Brazilian regions: nationwide serological household survey in 27 states.
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      • Sun J.
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      • Li H.
      • Kong Y.
      • Liang M.
      • et al.
      Seroprevalence of immunoglobulin M and G antibodies against SARS-CoV-2 in China.
      ,
      • Gallian P.
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      Lower prevalence of antibodies neutralizing SARS-CoV-2 in group O French blood donors.
      ,
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      SARS-CoV-2 IgG seroprevalence in blood donors located in three different federal states, Germany, March to June 2020.
      ,
      • Shakiba M.
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      ,
      • Percivalle E.
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      Prevalence of SARS-CoV-2 specific neutralising antibodies in blood donors from the Lodi Red Zone in Lombardy, Italy, as at 06 April 2020.
      ,
      • Ward H.
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      Antibody prevalence for SARS-CoV-2 following the peak of the pandemic in England: REACT2 study in 100,000 adults.
      ,
      • Havers F.P.
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      • Hall A.J.
      • et al.
      Seroprevalence of antibodies to SARS-CoV-2 in 10 sites in the United States, March 23–May 12, 2020.
      ]. As the pandemic spreads, it is crucial that a rapid and thorough analysis be undertaken to estimate global seroprevalence at a moment in time. In this study, 6 months after the commencement of the pandemic, we undertook a meta-analysis to estimate the global and regional seroprevalences of SARS-CoV-2 in people of the general population (whose prior COVID-19 status was unknown), and assessed whether geographical, climatic and sociodemographic factors impact on seroprevalence.

      Methods

       Search strategy and selection criteria

      This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (cf. Fig. 1). We performed a systematic literature search in the databases PubMed, Scopus, Embase, medRxiv and bioRxiv in August 2020 using the following terms: “SARS-CoV-2”, “COVID-19”, “coronavirus”, “antibody”, “ELISA”, “seroprevalence” and “population”, without language or geographical restriction (Supplementary Material Fig. S1). Additional related articles were retrieved manually from Google Scholar and critically evaluated. All articles were imported to Endnote software X8 (Thompson and Reuters, Philadelphia, USA), and duplicates were removed. Two independent reviewers (AR, MS) studied all titles and abstracts for eligibility. Included were all peer-reviewed population-based studies, preprints, and research reports which reported the prevalence of anti-SARS-CoV-2 serum antibodies in the ‘general population’ (i.e. randomly selected people of different ages, occupations, educational and ethnic backgrounds, and socioeconomic status, living in a defined geographical region, whose prior COVID-19 status was unknown). Articles were excluded if they (a) involved suspected, confirmed or hospitalized COVID-19 patients, (b) were performed in at-risk populations (e.g. healthcare workers) or individuals with known diseases (e.g. cancer or dialysis patients), (c) recorded prevalence based on clinical manifestation, computed tomography scan or PCR, (d) were comparative studies of diagnostic methods, (e) used datasets that overlapped with those of other articles, (f) were case reports or case studies, or (g) were editorials, commentaries, reviews or systematic reviews.
      Fig. 1
      Fig. 1Search strategy and study selection process, indicating numbers of studies (and associated datasets) excluded or included.

       Extraction of data and quality evaluation

      After the screening of published articles for eligibility, relevant data and information from each eligible study were entered into a specific form in Microsoft Excel (version 2016; Microsoft Corporation, Redmond, USA). Two co-authors (AR and MNS) independently collated data from all eligible studies, and two (MS and SE) independently evaluated these data. Any inconsistencies were discussed and a consensus decision was made. The following items were obtained from each study (if described): primary author; publication year; country; city; study design and period; type of serological methods used; sensitivity and specificity of diagnostic methods; number of people screened; the number of people seropositive for SARS-CoV-2 antibodies; and data regarding age, sex and ethnicity.
      All geographical areas (i.e. cities and countries) investigated were classified according to ‘Sustainable Development Goal’ (SDG) regions or subregions defined by the United Nations [
      The sustainable development goals (SDGs) report 2019. Regional groupings.
      ]. For individual countries, we recorded information on the total numbers of confirmed cases and deaths (up to 15th August 2020) reported by the World Health Organization (WHO) [
      World Health Organization (WHO)
      Coronavirus disease (COVID-19), Situation Report–198.
      ], World Bank's income category [
      World Bank Group databaseWorld Bank Country and Lending Groups
      ], gross national income per capita [
      World Bank Group database
      Gross national income per capita 2019.
      ], and the human development index (HDI) [
      United Nations Development Program
      ]. Furthermore, we recorded total global, regional and national populations (both sexes combined) in 2020, estimated by the United Nations [
      • United Nations
      Total population (both sexes combined) by region, subregion and country, annually for 1950–2100 (thousands).
      ]. If sample size(s) and the numbers of seropositive people were specified in studies, we extracted and critically appraised data for separate geographic regions. We also recorded latitude, longitude, mean relative humidity, and mean environmental temperature in geographic regions/subregions during the study period using the database timeanddate.com (weblink: https://www.timeanddate.com). The quality of studies included in the meta-analysis was assessed using the Joanna Briggs Institute (JBI) Prevalence Critical Appraisal Tool [
      • Munn Z.
      • Moola S.
      • Riitano D.
      • Lisy K.
      The development of a critical appraisal tool for use in systematic reviews addressing questions of prevalence.
      ]. Individual articles were assessed as to whether they adequately described the following: sample collection, recruitment method, subjects and the setting, number of subjects, information on subjects, results, reliability of results, statistical analysis method(s), subpopulation analysis and confounder adjustment (‘yes’ or ‘no’ answer). For each study, the number of ‘yes’ answers to these ten criteria was counted; the higher the number of ‘yes’ answers, the lower the risk of bias in a study.

       Meta-analysis

      All analyses were carried out using Stata statistical software (v.13 Stata Corp., College Station, TX, USA). To conservatively estimate the pooled seroprevalence of SARS-CoV-2 in the general population, we used a DerSimonian and Laird random-effects model (REM) [
      • DerSimonian R.
      • Laird N.
      Meta-analysis in clinical trials.
      ]. For this purpose, first, we estimated the seroprevalence in individual countries by synthesizing the seroprevalence rates of all studies from the same country, and then we calculated the seroprevalences of SARS-CoV-2 for the WHO-defined regions (if studies were available for at least two countries) by synthesizing the data for countries within the same SDG region. We calculated the pooled seroprevalence rates at a 95% confidence interval (CI) using the ‘metaprop’ command in Stata software. We estimated heterogeneity using the I2 statistic; an I2 >75% and p < 0.05 were considered to represent substantial heterogeneity [
      • Higgins J.P.
      • Thompson S.G.
      Quantifying heterogeneity in a meta-analysis.
      ]. To estimate the number of people exposed to SARS-CoV-2, we extrapolated seroprevalence estimates to the total human population (in 2020) living in a country and a region according to the UN Population Division [
      The sustainable development goals (SDGs) report 2019. Regional groupings.
      ].
      To explore possible sources of heterogeneity and also effects of sociodemographic, geographical and climatic parameters on SARS-CoV-2 seroprevalence, we undertook several subgroup analyses by REM as well as random effects meta-regression ecological analyses using the ‘metareg’ command in Stata [
      • Harbord R.M.
      • Higgins J.P.
      Meta-regression in Stata.
      ]. These analyses were performed considering the following: SDG regions; serological method used; age, sex and ethnicity of people; country income level, country HDI; latitude, longitude; mean environmental temperature; mean relative humidity; and time during the pandemic. To assess the effect of these variables on seroprevalence, we carried out random effects meta-regression analyses using the ‘metareg’ command in Stata [
      • Harbord R.M.
      • Higgins J.P.
      Meta-regression in Stata.
      ]. Further meta-regression analyses were performed to assess whether seroprevalence was associated with the total number of confirmed cases or deaths in individual countries. As publication bias is not relevant for prevalence studies [
      • Hunter J.P.
      • Saratzis A.
      • Sutton A.J.
      • Boucher R.H.
      • Sayers R.D.
      • Bown M.J.
      In meta-analyses of proportion studies, funnel plots were found to be an inaccurate method of assessing publication bias.
      ], it was not assessed. Results were considered as statistically significant if p < 0.1.

      Results

       Study characteristics

      Our search of electronic databases identified a total of 4912 articles; following the removal of duplicate articles and a critical appraisal of article titles and abstracts, 133 potentially relevant articles were identified for full-text evaluation (Fig. 1). After applying the eligibility criteria, 47 articles were included in the quantitative synthesis; these 47 eligible articles contained 107 datasets representing 399 265 people from 23 countries in six SDG regions. Of these datasets, 74 were from Europe and Northern America, 17 from Latin America and the Caribbean, 13 from Eastern and South-Eastern Asia, one from Central and Southern Asia, one from North Africa and Western Asia, and one from Sub-Saharan Africa. We did not identify a published study from Oceania. Information on the studies included is provided in the Supplementary Material Table S1. Most articles included (44 studies) had a low risk of bias (score: 7–10/10), and only three studies had a moderate risk (6/10) of bias (Table 2).
      Table 1Global, regional and national pooled prevalence of serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the general population (results from 47 studies containing 107 datasets performed in 23 countries)
      WHO regions/countryNumber datasetsNumber of people screened (total)Number of seropositive peoplePooled seroprevalence

      % (95%CI)
      Estimated global or country's population (2020)Estimated number of people exposed to SARS-CoV-2 (95%CI)
      Global107399 26515 8793.38 (3.05–3.72)7 794 799 000263 565 606 (237 741 369–289 966 523)
      Europe and northern America74272 26513 1094.21 (3.52–4.90)1 116 506 00047 004 902 (39 301 011–54 708 794)
      Northern America2251 54431464.41 (3.03–5.79)368 870 00016 267 167 (11 176 761–21 357 573)
      United states2251 54431464.41 (3.03–5.79)331 003 00014 597 232 (10 029 390–19 165 073)
      Western Europe1316 9336583.17 (1.96–4.38)196 146 0006 217 828 (3 844 461–8 591 194)
      Belgium273912933.46 (3.04–3.88)11 590 000401 014 (352 336–449 692)
      France51198302.19 (1.20–3.18)65 274 0001 429 500 (783 288–2 075 713)
      Germany43806812.23 (0.793.67)83 784 0001 868 388 (661 893–3 074 872)
      Switzerland127662197.92 (6.948.99)8 655 000685 476 (600,657–778,084)
      Luxembourg11862351.88 (1.312.60)626 00011 768 000 (8 200–16 276)
      Southern Europe2671 47832424.41 (2.20–6.61)152 215 0006 712 681 (3 348 730–10 061 411)
      Croatia21494191.05 (0.561.60)4 105 00043 102 (22 988–65 680)
      Italy423231457.27 (2.4811.9)60 462 0004 395 587 (1 499 457–7 249 393)
      Spain1961 07530545.01 (4.835.18)46 755 0002 342 425 (2 258 266–2 421 909)
      Greece16586240.36 (0.23–0.54)10 423 00037 522 (23 972–56 284)
      Eastern Europe110 474690.66 (0.51–0.83)293 013 0001 933 885 (1 494 366–2 432 007)
      Hungary110 474690.66 (0.510.83)9 660 00063 756 (49 266–80 178)
      Northern Europe12121 83659945.27 (3.97-6.57)106 261 0005 599 954 (4 218 561–6 981 347)
      England999 90855445.65 (4.61–6.69)67 886 0003 835 559 (3 129 544–4 541 573)
      Denmark221 7154181.77 (1.601.95)5 792 000102 518 (92 672–112 944)
      Sweden12133215.0 (10.5–20.5)10 099 0001 516 869 (1 061 405–2 074 334)
      Eastern and south-eastern Asia1389 64818552.02 (1.56–2.49)2 346 709 00047 403 521 (36 608 660- 58 433 054)
      Eastern Asia1288 83218522.02 (1.56–2.49)1 678 090 00033 897 418 (26 178 204- 41 784 441)
      China886 41617561.63 (1.132.13)1 439 324 00023 460 981 (16 264 361 – 30 657 601)
      Japan32218813.62 (2.844.39)126 476 0004 578 431 (3 591 918 – 5 552 296)
      South-Korea1198157.58 (4.3012.2)51 269 0003 886 190 (2 204 567 – 6 249 691)
      South-Eastern Asia181630.37 (0.08-1.07)668 620 0002 473 894 (534 896–7 154 234)
      Malaysia181630.37 (0.08–1.07)32 366 000119 754 (25 893–346 316)
      Latin America and the Caribbean1733 5966181.45 (0.95–1.94)653 962 0009 482 449 (6 212 639–12 686 862)
      South America1733 5966181.45 (0.95-1.94)430 760 0006 246 020 (4 092 220–8 356 744)
      Brazil1532 3524790.96 (0.52–1.40)212 559 0002 040 566 (1 105 306- 2 975 826)
      Chile2124413910.78 (9.1–12.5)19 116 0002 060 704 (1 731 909 -2 389 500)
      Sub-Saharan Africa130981745.62 (4.83-6.49)1 094 366 00061 503 369 (52 857 878–71 024 353)
      Kenya130981745.62 (4.83–6.49)53 771 0003 021 930 (2 597 139–3 489 738)
      Central and southern Asia152811722.16 (18.7–26.0)2 014 709 000446 459 514(376 549 112- 522 816 985)
      Iran152811722.16 (18.7–26.0)83 993 00018 612 848 (15 698 291–21 796 183)
      Northern Africa and western Asia113064.62 (1.71–9.78)525 869 00024 295 147(8 992 359- 51 429 988)
      Libya113064.62 (1.71–9.78)6 871 000317 440 (117 494 – 671 983)
      Table 2Prevalence of serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the general population according to a priori defined subgroups
      Variable/subgroupsNumber of datasetsNumber of people screened (total)Number of seropositive peoplePooled seroprevalence

      % (95%CI)
      Gender
      Male29145 36861865.33 (4.356.31)
      Female29151 79069585.05 (4.066.04)
      Age
      ≤191118 3335352.28 (1.013.56)
      20–491596 10942683.22 (1.904.55)
      50–641575 58937692.98 (1.594.36)
      ≥651241 42116342.57 (1.393.76)
      Type of population
      General68227 42864832.43 (2.162.70)
      General adult18169 01692015.31 (4.126.50)
      General children218211628.76 (7.4610.06)
      Serological method
      LFIA58224 92210 0233.95 (3.17-4.74)
      ELISA2338 15914173.53 (2.654.40)
      CLIA1580 43519072.73 (2.033.42)
      Virus neutralisation assay1040 6486451.32 (0.901.74)
      Microsphere immunoassay115 101188712.50 (11.97–13.03)
      Type of procedure
      Commercial kit83334 33413 8703.33 (2.953.71)
      In-house2464 93120093.63 (2.794.48)
      Race/ethnicity
      White, non-Hispanic7114 54456623.76 (1.436.08)
      Black, non-Hispanic772876499.96 (2.9516.97)
      Brown/Hispanic714 34710168.76 (0.0118.65)
      Multiple race/Asian/other/unknown781397095.78 (1.769.79)
      LFIA, lateral flow immunoassay; ELISA, enzyme-linked immunosorbent assay; CLIA, chemiluminescence immunoassay.

       SARS-CoV-2 seroprevalence

      Analysis of the 107 datasets selected from the 47 articles showed that 15 879 people from a general population of 399 265 had specific serum antibodies to SARS-CoV-2, indicating a pooled seroprevalence of 3.38% (95%CI 3.05–3.72%). Significant heterogeneity (I2 = 99.4%, p < 0.001) was seen among studies. An extrapolation to the global population (2020) indicated that ~263.5 million (range: 237 741 369 to 289 966 523) people had been exposed to SARS-CoV-2 (14th July 2020). More details on the overall and regional SARS-CoV-2 seroprevalences and burdens are given in Table 1. According to SDG subregions (for which two or more countries were represented), seroprevalences were: 5.27% (3.97–6.57%) in Northern Europe; 4.41% (2.20–6.61%) in Southern Europe; 4.41% (3.03–5.79%) in Northern America; 3.17% (1.96–4.38%) in Western Europe; 2.02% (1.56–2.49%) in Eastern Asia; and 1.45% (0.95–1.94%) in South America. Countries with the highest seroprevalences were Iran (22.1%), Sweden (15.02%), Chile (10.7%), Switzerland (7.9%), Italy (7.27%), South Korea (7.5%), Spain (5.0%) and the USA (4.4%). Fig. 2 shows the SARS-CoV-2 seroprevalence estimates for individual countries.
      Fig. 2
      Fig. 2Estimated seroprevalence rates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the general human population in different countries using the geographic information system (GIS).

       Seroprevalence according to sex, age and population

      Of the 47 studies included, 29 reported separate, pooled seroprevalences for males and females. Of 145 368 males and 151 790 females, 6186 males (5.33%, 4.35–6.31%) and 6958 females (5.05%, 4.06–6.04) had specific serum antibodies against SARS-CoV-2. Fifteen studies reported pooled seroprevalences for different age groups; subgroup analyses revealed pooled seroprevalences of 2.28% (1.01–3.56%), 3.22% (1.90–4.55%), 2.98% (1.59–4.36%) and 2.57% (1.39–3.76%) in people aged ≤19, 20–49, 50–64 and ≥ 65 years, respectively (Table 2).
      Of the 47 studies, 36 tested people of all age groups, whereas nine and two studies tested only adults and children, respectively (Table 2). Subgroup analysis revealed pooled seroprevalences of 2.43% (2.16–2.70%) in people of all ages, 5.31% (4.12–6.50%) in adults only, and 8.76% (7.46–10.06%) in children only (Table 2).

       Seroprevalence in relation to serological assay used

      Of 47 studies, 18 utilized rapid LFIAs to detect specific serum antibodies against SARS-CoV-2, 11 used ELISA, 13 used CLIAs, four studies employed a virus neutralization assay, and one used a microsphere immunoassay. Thirty-seven studies used commercial kits and ten employed in-house serological methods. Subgroup analyses, conducted considering the type of serological method employed, revealed pooled seroprevalences of 3.95% (3.17–4.74%), 3.53% (2.65–4.40%), 2.73% (2.03–3.42%) and 1.32% (0.90–1.74%) using LFIA, ELISA, CLIA and neutralization assays, respectively. One study in the USA, which used a microsphere immunoassay, indicated a seroprevalence of 12.5% (11.97–13.03%). Subgroup analysis revealed pooled seroprevalence rates of 3.33% (2.95–3.71%) using commercial assays and 3.63% (2.79–4.48%) employing in-house assays (Table 2).

       Seroprevalence in relation to ethnicity

      Seven studies (five from the USA, one from England and one from Brazil) had datasets that were stratified according to ethnicity. Subgroup analysis revealed pooled seroprevalences of 3.76% (1.43–6.08%), 9.96% (2.95–16.97%), 8.76% (0.01–18.65%) and 5.78% (1.76–9.79%) in people of white, black, Hispanic and other ethnic backgrounds (Asian/other), respectively (Table 2). In the USA, subgroup analysis revealed pooled seroprevalences of 4.11% (1.45–6.78%), 10.83% (4.81–16.85%), 12.79% (2.33–27.91%) and 5.86% (1.12–10.60%) in people of white/non-Hispanic, black/non-Hispanic, Hispanic and other backgrounds (Asian/other), respectively.

       Relationship between seroprevalence and sociodemographic variables

      Thirty-five studies represented countries with high income and very high HDI levels; 11 represented countries with upper-middle income levels and high HDIs, and one country had lower-middle income and medium HDI levels. No study was from a low-income or low-HDI country. Subgroup analysis (Table 3), according to income and HDI level, revealed higher seroprevalences in countries with high income (4.44%, 3.77–5.1%) and very high HDI levels (4.37%, 3.71–5.02%) than in countries with upper-middle income (1.31%, 1.02–1.59%) and high HDI levels (1.35%, 1.06–1.64%). Random-effects meta-regression analyses showed a significant, increased trend in seroprevalence with higher income levels (coefficient, C = 3.10e-07; p = 0.09) and HDI levels (C = 0.131; p = 0.01) (Figs. 3A,B).
      Table 3Prevalence of serum antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the general population based on subgroups according to different sociodemographic geographic parameters and time during, calculated using a random effects model
      Parameters/subgroupsNumber of datasetsNumber of people screened (total)Number of seropositive peoplePooled seroprevalence

      % (95%CI)
      Income
      Lower middle130981745.62 (4.83–6.49)
      Upper middle26120 24223611.31 (1.021.59)
      High80275 92513 3444.44 (3.775.1)
      Human development index
      Medium13 0981745.62 (4.83-6.49)
      High26119 42623581.35 (1.061.64)
      Very high80276 74113 3474.37 (3.715.02)
      Latitude
      0–20°518 0074962.99 (0.715.28)
      20–40°49160 89040342.29 (2.032.56)
      40–60°53220 36811 3494.68 (3.925.43)
      Longitude
      0–30°53220 85199694.15 (3.494.82)
      30–60°1635 4787691.76 (1.182.34)
      60–90°1027 92724356.36 (3.079.66)
      90–120°18102 37824972.80 (2.373.22)
      ≥1201012 6312091.63 (1.012.25)
      Relative humidity (%)
      <601562 69237635.84 (3.817.87)
      60–7976306 05711 1593.41 (2.963.85)
      ≥801630 5169572.77 (2.013.55)
      Mean temperature (°C)
      <7417651027.87 (1.5414.20)
      7.1–1336111 68353514.27 (3.235.32)
      13.1–1943232 76393324.16 (3.534.78)
      19.1–251829 5503030.85 (0.601.11)
      25.1–30623 5047913.79 (1.755.84)
      The time from the beginning of the pandemic (days)
      <15582221293.54 (1.845.23)
      16–3021943693.05 (2.293.81)
      31–451268 37014702.51 (2.052.97)
      46–602449 66310091.76 (1.282.24)
      60–751522 4526142.59 (1.833.34)
      76–9027105 31754193.97 (2.984.96)
      90–105836 94511893.80 (2.225.39)
      106–120235573898.63 (7.729.54)
      121–13524114710.34 (7.4213.26)
      136–150999 90855445.65 (4.616.69)
      Fig. 3
      Fig. 3Ecological random effects meta-regression analyses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in the general population in relation to: (A) a country's income level (a statistically significant upward trend in seroprevalence in countries with higher income levels); (B) human development index (HDI) (a statistically significant upward trend in seroprevalence in higher HDI countries); (C) geographical latitude (a statistically significant upward trend in seroprevalence with increasing geographical latitude); and (D) the mean temperature during study implementation (a statistically significant downward trend in seroprevalence with increasing mean temperature).

       Relationship between seroprevalence and geographical location, climate or time

      At geographical latitudes of 0–20°, 20-40° and 40-60°, seroprevalences were 2.99% (0.71–5.28%), 2.29% (2.03–2.56%) and 4.68% (3.92–5.43%), respectively; the highest and lowest seroprevalences were at longitudes 60–90° (6.36%, 3.07–9.66%) and ≥120° (1.63%, 1.01–2.25%). In relation to climate, seroprevalences were 5.48% (3.81–7.87%), 3.41% (2.96–3.85%) and 2.77% (2.01–3.55%) in regions with mean relative humidities of <60%, 60–80%, and >80%, respectively. Subgroup analysis indicated that the highest and lowest seroprevalence rates occurred in climes with average environmental temperatures of <7°C (7.87%, 1.54–14.20%) and 19–25°C (0.85%, 0.60–1.11%), respectively (Table 3). There was a significant (C = 0.0007, p = 0.03), increasing trend in seroprevalence with increasing geographical latitude (Fig. 3C), and a non-significant (C = –0.00008, p = 0.316) decreasing trend with geographical longitude (Fig. S2A). Furthermore, there was a significant (C = –0.0017, p = 0.02), decreasing seroprevalence trend with increasing average environmental temperature (Fig. 3D) and a non-significant (C = –0.0006, p = 0.12), decreasing trend with increasing relative humidity (Supplementary Material Fig. S2B). Another subgroup analysis was conducted to explore SARS-CoV-2 seroprevalence over time from the start of the pandemic to the time of sampling/testing in individual studies. The results indicated that seroprevalence in a country was lowest at the beginning of a COVID-19 epidemic, higher at 70 days, and highest 4 months after the start of such an epidemic (p = 0.001, Table 3). Random-effects meta-regression analysis showed a significant increasing trend in seroprevalence over time (C = 0.002, p = 0.02, Fig. 4).
      Fig. 4
      Fig. 4Random effects meta-regression analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in the general human population in relation to time from the start of the pandemic to the time of sampling/testing in individual studies (articles) included in the present review. A statistically significant upward trend in seroprevalence is seen over time (C = 0.002, p 0.02).

       Association between seroprevalence and confirmed COVID-19 cases (i.e. disease) and death

      Meta-regression analyses revealed a non-significant increasing trend in the number of confirmed cases (C = 0.0002, p = 0.921) and of deaths (C = 0.0001, p = 0.640) with increasing seroprevalences (Supplementary Material Fig. S3A,B).

      Discussion

      Currently, COVID-19 is the number one public health concern worldwide. Here we provide a comprehensive appraisal of SARS-CoV-2 seroprevalence in the ‘general’ human population from continents from which peer-reviewed investigations have been published (up to 14th August 2020), and excluding studies of high-risk patient groups to avoid a overestimation of seroprevalence. The meta-analysis revealed a pooled SARS-CoV-2 seroprevalence of 3.38% (95%CI 3.05–3.72%) relating to ~264 million individuals worldwide at the time of drafting this manuscript. Our findings are in accord with the World Health Organization (WHO) report prediction that 2–3% of the global population might have been infected by the end of the first epidemic wave [
      World Health Organization (WHO)
      Coronavirus disease (COVID-2019) press briefing 20 April 2020.
      ]. Thus, our findings suggest that (at the time of this study) ~97% of the world's population was susceptible to SARS-CoV-2 and COVID-19.
      Overall seroprevalence varied markedly among countries and regions, which may be attributable to many factors, including chance variation, cultural practices, political decision-making, policies, mitigation efforts, health infrastructure and prevention/control measures and/or the effectiveness of the implementation of such measures [
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      Due to variability in the data and the lack of detailed reporting, the present findings should be interpreted with some caution. We did see a lower seroprevalence in white people than in other ethnic minority groups, which is in accord with previous studies [
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      The present analyses indicate an increasing trend for seroprevalence at higher latitudes and lower mean environmental temperatures and relative humidities. This trend seems consistent with some previous laboratory, epidemiological and mathematical modelling studies [
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      • Sabin K.
      • McGowan C.
      • et al.
      Prevalence and burden of HCV co-infection in people living with HIV: a global systematic review and meta-analysis.
      ], we explored possible sources of heterogeneity, including geographic region and diagnostic methods. However, we did not find the source of this heterogeneity.
      This study reinforces the major global health threat posed by SARS-CoV-2 infection and its very rapid spread, with the global seroprevalence rising to 3.38% only months after the commencement of the pandemic. This prevalance suggests, though, that ~96% of the world's population are still susceptible to infection, which is alarming. This means that many countries could still face multiple surges in cases, overwhelming medical systems. We have seen in many locations that hospital beds, intensive care units (ICUs) and ventilators have reached capacity. For instance, early on in New York City the USA had to send mercy ships to handle the surge in need. Therefore, countries should have plans and medical resources in place for future, unexpected waves of COVID-19.
      There are indications from some countries that mortality rates for COVID-19 are higher than those officially reported [
      • Burn-Murdoch J.
      • Romei V.
      • Giles C.
      Global coronavirus death toll could be 60% higher than reported.
      ,
      • Modi C.
      • Boehm V.
      • Ferraro S.
      • Stein G.
      • Seljak U.
      How deadly is COVID-19? A rigorous analysis of excess mortality and age-dependent fatality rates in Italy.
      ,
      • Baud D.
      • Qi X.
      • Nielsen-Saines K.
      • Musso D.
      • Pomar L.
      • Favre G.
      Real estimates of mortality following COVID-19 infection.
      ]. Hence, until a vaccine or vaccines is/are available, the focus needs to be on education and prevention and strict quarantine measures. Presently, masks and safe physical distancing are our key means of reducing exposure, infections, disease and deaths. A global meta-analysis [
      • Chu D.K.
      • Akl E.A.
      • Duda S.
      • Solo K.
      • Yaacoub S.
      • Schünemann H.J.
      • et al.
      Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis.
      ] showed that applying physical distancing of ≥1 m and usage of personal protective equipment (PPE, including face mask, eye and body protection) results in a major reduction in transmission/infection risk. However, the lack of preparedness in many countries to control a rapidly spreading, highly virulent and pathogenic virus, combined with limited or no biosecurity strategies/policies on how to deal with pandemics in populations, meant that such simple measures were not introduced initially. Our study calls for routine surveys to monitor temporal changes in seroprevalence in a location. In the context of epidemics and pandemics, such surveys might be conducted on a monthly or 2-weekly basis to allow authorities to assess the spread of the virus and exposure levels in populations. A global plan is required to monitor SARS-CoV-2 seroprevalence to assist prevention and control efforts. We aim to continue to follow the global seroprevalence situation over time, and to report on trends and changes.

      Author contributions

      AR, RBG and PJH conceived the study. AR, MS, MNS and SE conducted the searches and collected data. AR, MS, SMR and ML analysed and interpreted the datasets. AR, AHM, RBG and PJH drafted and edited the manuscript. All authors commented on or edited drafts and approved the final version of the manuscript.

      Transparency declaration

      The authors declare no conflict of interest. This study was supported by the Health Research Institute at the Babol University of Medical Sciences , Babol, Iran (IR.MUBABOL.REC.1399.304). RBG's research programme is supported by the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), Yourgene Health Singapore , and Melbourne Water.

      Acknowledgements

      Sincere thanks to Constantine E. Gasser for critical reading of the manuscript and comments.

      Appendix A. Supplementary data

      The following is the Supplementary data to this article:

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