key: cord-1004704-z8xjn6ta
authors: Rostami, A.; Sepidarkish, M.; Leeflang, M.; Riahi, S.M.; Shiadeh, M. Nourollahpour; Esfandyari, S.; Mokdad, A.H.; Hotez, P.J.; Gasser, R.B.
title: SARS-CoV-2 seroprevalence worldwide: a systematic review and meta-analysis
date: 2020-10-24
journal: Clin Microbiol Infect
DOI: 10.1016/j.cmi.2020.10.020
sha: 3520a0e1cfc392b900b61790d70a7b963a4114e7
doc_id: 1004704
cord_uid: z8xjn6ta

BACKGROUND: COVID-19 is arguably the most important public health concern in 2020 worldwide, and efforts are now escalating to suppress or eliminate its spread. OBJECTIVE: In this study, we undertook a meta-analysis to estimate the global and regional SARS-CoV-2 seroprevalence rates in humans, and to assess whether seroprevalence associates with geographical, climatic and socio-demographic factors. DATA SOURCES: We systematically reviewed PubMed, Scopus, Embase, medRxiv and bioRxiv databases for preprints or peer-reviewed articles (up to 14 August 2020). STUDY ELIGIBILITY CRITERIA: Population-based studies describing the prevalence of anti-SARS-CoV-2 (IgG and/or IgM) serum antibodies. PARTICIPANTS: People of different socio-economic and ethnic backgrounds – from the general population – whose prior COVID-19 status was unknown were tested for the presence of anti-SARS-CoV-2 serum antibodies. INTERVENTIONS: There were no interventions. METHODS: 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 socio-demographic, geographical and/or climatic factors. RESULTS: In total, 47 studies involving 399,265 people from 23 countries met the inclusion criteria. Heterogeneity (I(2) = 99.4%, P < 0.001) was seen among studies; the 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, geographical 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. CONCLUSION: 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. A. Rostami, Clin Microbiol Infect 2020.

COVID-19 -a severe, acute respiratory syndrome caused by the coronavirus SARS-CoV-2 -was first identified in Wuhan, China in December 2019 [1, 2] , and spread within months to most nations of the world [3] . By 16 August 2020, this pandemic disease was affecting people in 213 countries and territories, with ~ 21 million confirmed cases and ~ 800,000 deaths reported globally [4] . The diagnosis and management of COVID-19 is based on the detection of SARS-CoV-2 in nasopharyngeal swabs from patients presenting with clinical signs (including fever, dry cough and/or shortness of breath), or in suspected cases, by reverse transcription-polymerase chain reaction (RT-PCR) [5, 6] . Since the manifestation of SARS-CoV-2 infection ranges from asymptomatic to fatal, the surveillance of confirmed COVID-19 cases might not be representative for a particular community [7, 8] . Although RT-PCR is presently recognized as a "gold standard" for the diagnosis of SARS-CoV-2 infection [5] , a significant number of asymptomatic or subclinical, infected individuals is likely to remain undetected. Therefore, it is plausible or likely that the actual number of people exposed to, or infected with, is underestimated [7] [8] [9] . Serological screening represents a critical adjunct to PCR-based detection/diagnosis and is a key tool to evaluate the cumulative prevalence of SARS-CoV-2 infection, and to monitor seroconversion [10] and seroreversion [11, 12] in individuals and a community, to gain insight into the dynamics of specific antibody responses during and after the spread of the virus and, if undertaken routinely, to inform health authorities, politicians and policy makers about seroprevalence at any given stage during an epidemic [13, 14] . The prevalence of specific serum antibodies (IgG and/or IgM) against SARS-CoV-2 can provide a sound indication of exposure to SARS-CoV-2 in a population [7, 9] . Due to an apparent persistence of antibodies to SARS-CoV-2 (particularly IgG) after viral clearance [7] , it is expected that serological monitoring and surveillance provide relevant data sets to estimate the cumulative prevalence of SARS-CoV-2 infection/exposure in a population [7, 15] , and may even indicate the immune status of individuals or populations [8, 9] .

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) [16, 17] . 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 [18, 19] .

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 [9, [20] [21] [22] [23] [24] [25] [26] [27] . 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, six months after the commencement of the pandemic, we undertook a metaanalysis 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 socio-demographic factors impact on seroprevalence.

J o u r n a l P r e -p r o o f

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 (Fig. S1 ). Additional related articles were retrieved from Google Scholar and manually. All articles were imported to Endnote software X8 (Thompson and Reuters, Philadelphia, USA), and duplicates removed. Two independent reviewers (A.R., M.S.) 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, socioeconomic status, living in a defined geographical region, whose prior COVID-19 status was unknown). Articles were excluded if they (1) involved suspected, confirmed or hospitalised COVID-19 patients; (2) were performed in atrisk population (e.g., health-care workers) or individuals with known diseases (e.g., cancer or dialysis patients); (3) recorded prevalence based on clinical manifestation, computed tomography scan or PCR; (4) were comparative studies of diagnostic methods; (5) used data sets that overlapped with those of other articles; (6) were case reports or case studies; or (7) were editorials, commentaries, reviews or systematic reviews.

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 (A.R. and M.N.S) independently collated data from all eligible studies, and two (M.S. and S.E.) independently evaluated these data. Any inconsistencies were discussed and consensus decision 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 sero-positive for SARS-CoV-2 antibodies, and data regarding to age, sex and ethnicity.

All geographical areas (i.e. cities and countries) investigated were classified according to 'Sustainable Development Goal' (SDG)-regions or sub-regions defined by the United Nations [28] . For individual countries, we recorded information on the total numbers of confirmed cases and deaths (up to 15 August 2020) reported by World Health Organization (WHO) [29] , World Bank's income category [30] , gross national income per capita [31] and the human development index [HDI] [32] . Furthermore, we recorded total global, regional and national populations (both sexes combined) in 2020, estimated by the United Nations [33] . If sample size(s) and the numbers of sero-positive 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/sub-regions 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 [34] .

Individual articles were assessed as to whether they adequately described: 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 10 criteria was counted; the higher the number of "yes" answers, the lower the risk of bias in a study.

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) [35] . 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 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 I 2 statistic, and an I 2 of >75% and a p < 0.05 were considered to represent substantial heterogeneity [36] . 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 [28] .

To explore possible sources of heterogeneity and also effects of socio-demographic, 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 [37] . These analyses were performed considering 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 [37] . 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 [38] , it was not assessed. Results were considered as statistically significant if the P value was < 0.1.

Our search of electronic databases identified a total of 4,912 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 data sets representing 399,265 people from 23 countries in six SDGregions. Of these data sets, 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 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) .

Analysis of the 107 data sets 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 (I 2 = 99.4%, P < 0.001) was seen among studies. An extrapolation to a global population (2020) indicated that ~ 263.5 million (range: 237,741,369 to 289,966,523) people had been exposed to SARS-CoV-2 (14 July 2020). More detail on the overall and regional SARS-CoV-2 seroprevalences and burdens is given in Table 1 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) .

Of 47 studies, 18 studies utilised rapid LFIAs to detect specific serum antibodies against SARS-CoV-2, 11 used ELISA, 13 used CLIAs, four studies employed a virus neutralisation assay, and one used a microsphere immunoassay. Thirty-seven studies used commercial kits and 10 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 neutralisation 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 and 3.63% (2.79-4.48%) employing in-house assays ( Table  2) .

Seven studies (five from the USA, one from England and one from Brazil) had data sets 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 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 white/non-Hispanic, black/non-Hispanic, Hispanic and other backgrounds (Asian/other), respectively.

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 (coefficient [C] = 3.10e-07; P-value = 0.09) and HDI (C = 0.131; P-value = 0.01) levels ( Fig. 3A-B) .

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 (coefficient [C] = 0.0007; P-value = 0.03), increasing trend in seroprevalence with increasing geographical latitude (Fig. 3C) , and a non-significant (C = -0.00008; P-value = 0.316), decreasing trend with geographical longitude (Fig. S2A) . Furthermore, there was a significant (C = -0.0017; P-value = 0.02), decreasing seroprevalence trend with increasing, average environmental temperature (Fig. 3D ) and a non-significant (C = -0.0006, P-value = 0.12), decreasing trend with increasing relative humidity (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-value ˂ 0.001; Table 3 ). Random-effects meta-regression analysis showed a significant, increasing trend in seroprevalence over time (C = 0.002; P-value = 0.02; Fig. 4 ).

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

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 had been published (up to 14 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 [39] . Thus, our findings suggested (at the time of this study) that ~ 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 decisionmaking, policies, mitigation efforts, health infrastructure and prevention/control measures and/or the effectiveness of the implementation of such measures [40, 41] . Subgroup analysis suggested higher seroprevalences rates in countries with higher income levels and HDIs. Due to a lack of data for many disadvantaged countries, findings need to be interpreted with caution, but possible explanations might include increased urbanisation and population density, higher levels of social interaction and intensity of international travel. Morever, our analysis did not extend to a time when COVID-19 will accelerate in the Southern Hemisphere, especially in Africa and South America, or in South Asia.

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 compared with other ethnic minority groups, which is in accord with previous studies [42] [43] [44] [45] reporting that minority groups are being disproportionately impacted by COVID-19. According to the Centers for Disease Control and Prevention (CDC) [46] , factors suggested to contribute to this disproportionate impact include discrimination in health care, housing, education and finances; communication and language barriers; cultural differences between patients and health care providers; lack of health insurance; increased employment of ethnic minority groups in essential work settings such as healthcare facilities, farms, factories, grocery stores, and public transportation; and living in more crowded families or conditions. In this analysis, we did not attempt to distinguish prevalence rates within different regions of the USA or other nations. Doing so might have revealed increased seroprevalence in lower income areas due to the factors mentioned above. Indeed, it was noted that the "blue marble health" concept of poverty-related diseases amongst the poor living in high income nations might apply to COVID-19, just as it does for neglected tropical diseases, tuberculosis and other poverty-related conditions [47, 48] .

Regarding the serological tests, our analysis indicated some variation in diagnostic sensitivity (detecting IgG and/or IgM) among the serological assays used in published studies. A recent investigation indicated sensitivity and specificity of 85% and 99% for both antibody isotypes [49] . This review showed that similar seroprevalences were established by ELISA (3.9%) and LFIA (3.5%), while lower seroprevalence rates were obtained using CLIA (2.7%) and virus neutralisation (1.3%) assays. Two recent meta-analyses [50, 51] showed that sensitivities were consistently lower for the LFIA (66-80%) assay compared with ELISA (84-J o u r n a l P r e -p r o o f 93%) and CLIA (90-97%), while a specificity of > 95% was calculated for all methods. Variation in sensitivity could be attributable to differences in the antigens used (i.e. recombinant or purified protein), the antibody conjugate employed and cut-off set for an assay [50, 52] . A Cochrane review indicated that the combination of the detection of IgG and IgM achieved a sensitivity of 30.1% one to 7 days, 72.2% for 8 to 14 days, 91.4% for 15 to 21 days after the onset of symptoms [53] . In present study [22, 25, 54, 55] , four studies used virus neutralisation to detect serum antibodies to SARS-CoV-2, all exhibiting a sensitivity and specificity of > 98%. Neutralisation assays are more time consuming to perform (3-5 days) and are carried out in Biosafety Level-3 (BSL-3) laboratories [56] ; therefore, these assays might be less suited for routine use. One study [57] used a microsphere immunoassay to detect serum antibodies to SARS-CoV-2 and indicated a high seroprevalence (12.5%). Although this method has been approved by the Food and Drug Administration (FDA), results might be interpreted with caution, given that only one study has been published to date.

The present analyses indicated 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 [58] [59] [60] [61] , showing that environmental temperature and humidity play key roles in the survival and transmission of seasonal respiratory viruses. The highest seroprevalences were estimated for latitudes between 40°N and 56°N, in accordance with a previous study [61] , indicating a substantial community-spread of SARS-CoV-2 up to March 2020 in areas located in a narrow band in the 30° N and 50° N "corridor". The finding of higher seroprevalence rates in areas with a low mean relative humidity and temperature accords with recent epidemiological [61] and laboratory [62] studies of coronavirus survival. Both temperature and humidity are known as critical factors determining survival and community-transmission of SARS-CoV, MERS-CoV and influenza [61, 63, 64] . Processes or mechanisms proposed to be linked to cold temperatures and low humidity include airborne droplet-stabilisation, increased viral replication in the nasopharyngeal mucosa or respiratory epithelium and/or reduced local innate immune responses, as evidenced for other respiratory viruses [58, 61, [65] [66] [67] . However, much more research is needed to explore these proposals, and the effects of geographical locations and/or climate factors on SARS-CoV-2 seroprevalence and COVID-19 prevalence.

As the present study represents a first "snap shot" of SARS-CoV-2 seroprevalence based on a critical evaluation of published information, it has a number of limitations: First, there is a lack of peer-reviewed, population-based studies from many countries across the globe at an early phase of the pandemic, and some studies included here lacked data on sex and age of subjects tested. We hope that these limitations can be addressed over the next months and years, so that longitudinal investigations provide future estimates that will be more representative of the situation worldwide, and so that, eventually, conclusions might be drawn regarding endemic stability and instability in particular countries and regions. Second, different serological methods/assays (with varying sensitivities and specificities) were employed in different studies, which will have some effect on our global estimate, although subgroup analyses were undertaken to assess a potential effect of the serological methods used. Third, pooled analyses showed significant heterogeneity. As such heterogeneity was expected in meta-analyses of global prevalence estimates [68] [69] [70] , 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 of 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 suggest, 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 and, hence, overwhelm medical systems. We have seen in many locations that hospital beds, intensive care units (ICUs) and ventilators 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 [71] [72] [73] . Hence, until a vaccine(s) is (are) available, the focus needs to be on education and prevention and strict quarantine measures. Indeed, universal masks and safe distance are our only means of reducing exposure, infections, disease and deaths. A global meta-analysis [74] showed that applying physical distancing of ≥ 1 m, 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 rapid-spreading, high 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 two-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. Fig. 3 . Ecological random effects meta-regression analyses of 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; (D) the mean temperature during study implementation -a statistically significant downward trend in seroprevalence with increasing mean temperature. Fig. 4 . Random effects meta-regression analysis of 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-value = 0.02). Table 1 Global, regional and national pooled prevalence of serum antibodies to SARS-CoV-2 in the general population (results from 47 studies containing 107 datasets performed in 23 countries).

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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 

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

Supplementary data to this article can be found online at …….

Estimated number of people exposed to SARS-CoV-2 (95% CI) J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f Table 3 Prevalence of serum antibodies against SARS-CoV-2 in the general population based on sub-groups according to different socio-demographic geographic parameters and time during, calculated using a random effects model.