key: cord-0726856-ngxr27sy
authors: Jahan, Nuzrath; Brahma, Adarsh; Kumar, Muthusamy Santhosh; Bagepally, Bhavani Shankara; Ponnaiah, Manickam; Bhatnagar, Tarun; Murhekar, Manoj V
title: Seroprevalence of IgG antibodies against SARS-CoV-2 in India, March 2020-August 2021: a systematic review and meta-analysis
date: 2021-12-28
journal: Int J Infect Dis
DOI: 10.1016/j.ijid.2021.12.353
sha: 993bbeb5844d5e170e76f0dcbdfa9967ac989c0d
doc_id: 726856
cord_uid: ngxr27sy

Introduction India experienced two waves of Coronavirus disease 2019 (COVID-19) pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 [SARS-CoV-2] and reported second highest caseload globally. Seroepidemiological studies were done to track the course of the pandemic. We systematically reviewed and synthesized seroprevalence of SARS-CoV-2 among Indian population. Methods We included studies reporting seroprevalence of IgG antibodies against SARS-CoV-2 from March 1, 2020, to August 11, 2021 and excluded studies done only among COVID-19 patients, and vaccinated individuals. We searched published databases, preprint servers and government documents using a combination of keywords and Medical subheading (MeSH) terms of "Seroprevalence AND SARS-CoV-2 AND India". We assessed risk of bias using the Newcastle Ottawa scale, the Appraisal tool for cross-sectional studies (AXIS), the Joanna Briggs Institute (JBI) critical appraisal tool and WHO's statement on the Reporting of Seroepidemiological Studies for SARS-CoV-2 (ROSES-S). We calculated pooled seroprevalence along with 95% Confidence Intervals (CI) during the first (March 2020 to February 2021) and second wave (March to August 2021). We also estimated seroprevalence by selected demographic characteristics. Results We identified 3821 studies and included 53 studies with 905,379 participants after excluding duplicates, screening of titles and abstracts and full-text screening. Of the 53, 20 studies were of good quality. Some of the reviewed studies did not report adequate information on study methods [sampling=24% (13/53); laboratory=83% (44/53)]. Studies of ‘poor’ quality had more than one of the following issues: unjustified sample size, non-representative sample, non-classification of non-respondents, results unadjusted for demographics and methods insufficiently explained to enable replication. Overall pooled seroprevalence was 20.7% in the first (95% CI=16.1 to 25.3) and 69.2% (95% CI=64.5 to 73.8) in the second wave. Seroprevalence did not differ by age in first wave, whereas in second, it increased with age. Seroprevalence was slightly higher among females in second wave. In both the waves, the estimate was higher in urban than in rural areas. Conclusion Seroprevalence increased by threefold between the two waves of the pandemic in India. Our review highlights the need for designing and reporting studies using standard protocols.

Coronavirus 2 (SARS-CoV-2) virus has spread rapidly across the world since December 2019. The pandemic has overwhelmed the health systems of developed and developing nations alike (Chowdhury & Jomo, 2020) . Countries lacked the required ability to test, trace, treat and isolate/quarantine the infected population. It is well established that true community burden would remain higher than the reported caseload due to various reasons like asymptomatic infections, the differences in testing strategies by time and place, variable sensitivities of laboratory tests used for diagnosis and other factors influencing the healthseeking behaviour of the population (M. V. Murhekar & Clapham, 2021) . Hence, estimating the actual number of people previously infected with SARS-CoV-2, aids in understanding the extent of spread and burden. This, in turn, could guide public health strategies for controlling transmission and that of future policies (Lai et al., 2020a) .

In this context, seroprevalence studies help estimate the proportion of the population that has evidence of SARS-CoV-2 specific antibodies in their blood during a given time period (Clapham et al., 2020) , both due to infection and vaccination. WHO developed a standardized protocol for seroprevalence studies on COVID-19 (WHO, 2020) for systematic collection and rapid sharing of exposure data in a format that can be easily aggregated, tabulated, and analysed across many different settings as well as to inform public health responses and policy decisions. As such, many SARS-CoV-2 seroprevalence studies have been carried out around the world (Alserehi et al., 2021; Poustchi et al., 2021; Venugopal et al., 2021) . While the results have varied considerably between studies and locations, they have consistently indicated that the true burden of previously infected is considerably higher than that of the reported number of confirmed cases (Chen et al., 2021) .

Influenced by local epidemiological factors, COVID-19 manifested as several local epidemics of different stages at any point in time. In the Indian context, the initial nationwide lockdown from March to April 2020 seemingly delayed the spread of infection. Subsequent to relaxations of lockdown, the incidence increased from April 2020 and peaked in September 2020 (MoHFW, 2021) . Thereafter, the number of cases reported per day kept declining until mid-February 2021. In the meantime, COVID-19 vaccinations became available in India from January 2021 onwards, and the second wave of the COVID-19 pandemic in India during March-July 2021, was driven predominantly by the Delta variant of SARS-CoV-2 (INSACOG bulletin, 2021) ) because of its higher transmissibility (CDC, 2020) .

Meanwhile, in India, three nationwide seroprevalence studies had been conducted apart from a number of state, regional, and setting-specific studies (M. V. M. V. Murhekar et al., 2021a , M. V. Murhekar et al., 2021b . These were mostly commissioned by the public health authorities to guide the response. Though there is sufficient primary evidence, there are considerable variations in estimates. Hence, a synthesised or pooled information may be useful across India, not just limited to cities or states, for guiding public health decisions. Further, interpretation of such estimates from seroprevalence studies should account for the limitations and uncertainties in the design, conduct and analysis of these studies. If not considered, any flawed understanding could result in flawed decisions towards controlling the spread. Meta-research of such seroprevalence studies by systematically identifying methodological lacunae and limitations in each of the primary studies could be a way forward in this regard.

Also, systematic review of global COVID-19 seroprevalence documented considerable heterogeneity of the studies and hence, India-specific systematic review is warranted (Bobrovitz et al., 2021; Chen et al., 2021) . Further, global dashboard of seroprevalence studies, Serotracker, provides an automated country-wise pooled estimate, has duplicate entries of the same study from multiple sources, unverified sources and quality of data for many studies (SeroTracker, 2021). We conducted a systematic review to summarize the seroprevalence of SARS-CoV-2 and quantitatively estimate the pooled seroprevalence of SARS-CoV-2 among the Indian population.

This study was registered with PROSPERO, CRD42021254997, and reported in compliance with Preferred Reporting Items for Systematic review and Meta-analyses Protocol (PRISMA-P) checklist (The BMJ, 2021).

We included studies on seroprevalence of SARS-CoV-2 among humans conducted in India.

We searched MEDLINE (PubMed), Embase, SCOPUS, Web of Science core collection, and two preprint servers (medRxiv, bioMxiv) (BioRxiv.Org 2021; MedRxiv.Org, 2021) In addition to the repositories mentioned, government documents were also searched for in respective state government websites. When needed, subject experts were contacted to ensure the inclusion of potentially relevant studies that might have been missed while searching above mentioned repositories.

Period of searching: We searched the repositories for studies published between March 1, 2020, to August 10, 2021. The last search was performed on August 11, 2021.

Search terms and strategies: The search was done using a combination of keywords and MeSH terms for "Seroprevalence AND SARS-CoV-2 AND India" (Supplementary Table 1 ).

These combinations of terms were used for searching studies in each of the repositories.

Study selection: All English-language observational studies, including cross-sectional, casecontrol, and cohort studies published between March 1, 2020, and August 11, 2021, were screened for title and abstract by two independent reviewers with the following inclusion and exclusion criteria. We included seroprevalence studies, reporting seroprevalence of SARS-CoV-2 Ig-G antibodies among the general population in India, or in a specific well-defined population in India: healthcare workers, contacts of COVID-19 patients, and other welldefined cohorts, among all age groups. We excluded studies that report seroprevalence exclusively among COVID-19 positive individuals; people vaccinated for COVID-19; and animals. Further, we did not include abstracts of congress meetings or conference proceedings, study protocols, commentaries, reviews, and case reports.

Screening and selection of studies: We used Rayyan software (Rayyan -Intelligent Systematic Review, 2021) for screening, deduplication, and selection of studies for the review. Two independent reviewers [NJ, AB] screened titles and abstracts independently to include studies that met the eligibility criteria. Similarly, full texts of the screened studies were assessed in detail. A third reviewer [MSK] resolved any conflicts between the two independent assessors. When full-text documents of studies were not available on any internet-based sources, we contacted the corresponding authors of the respective studies.

Data extraction: We extracted the following data from eligible studies: the author's name, publication date, study design, study period, sampling period, study population, study setting (rural/urban), demographics of study participants (age, gender, and occupation), frequency and type of exposures (travel history, contact history, comorbidities), laboratory methodology for serologic confirmation of SARS-CoV-2, and predefined outcomes (i.e., the total number of participants, the number of participants provided single or paired sera, the number of seropositive participants). From eligible studies, we extracted the data on the number of participants who provided specimens and the number of seropositive individuals to calculate the pooled seroprevalence. From serial cross-sectional studies, we calculated the sum of the total number of participants who provided specimens and the total number of seropositive participants during the whole study period. For cohort studies (with paired serum), we extracted only the data from the first blood collection. Although many studies did adjust for various factors, we decided to use the unadjusted estimates in our analyses for ease of interpretation across different studies.

We assessed the risk of bias using multiple scales as we included studies with different study designs. We used the following scales and criteria to assess bias critical appraisal tool (Martin, 2017) , and the WHO's -Reporting Of Sero-Epidemiologic Studies-SARS-CoV-2 (ROSES-S) statement (Group WHOSTW, 2021) . The Newcastle-Ottawa Scale (NOS) contains three domains that are graded on 10-point stars. Domain 1 evaluates the methodological quality of each study (5 stars), domain 2 assesses the comparability of the study (2 stars) and domain 3 evaluates the outcome measure and related statistical analysis (3 stars). Based on the total score, the studies were categorized into four groups: unsatisfactory (0 to 4 stars), satisfactory (5 to 6 stars), good (7 to 8 stars), and very good studies (8 to 10 stars). AXIS tool, JBI scale, and WHO CONSISE-ROSES-S statement comprehensively assess study design (representativeness of study participants), laboratory analysis, steps taken for internal validation, and outcome adjustment (correction for demographics and/or test performance for external validation). Two independent reviewers assessed the quality of studies. In the case of NOS, the average score for that particular study was taken as the final score. For other scales, a third reviewer resolved any conflicts between the two independent assessors.

Effect measure: In the meta-analysis, we estimated overall pooled seroprevalence of SARS-CoV-2 in India and that of stratum-specific estimates by pandemic waves, age, gender, residence, and study population Statistical analysis: We described characteristics of the identified studies like study population, study period, study setting, study design, laboratory methods, and statistical analysis employed. For all eligible studies included in the analysis, we calculated pooled seroprevalence with 95% Confidence Interval (CI) using a random-effects model and the inverse variance weighting. We visually represented the prevalence estimates using the forest plot. To explore heterogeneity, we calculated pooled seroprevalence by various sub-groups such as by age and gender, type of study participants: general population, specific, welldefined population like healthcare workers, in-patients of hospitals admitted for reasons other than COVID-19, and contacts of COVID-19 patients, time period: based on epi-curve of COVID-19 for India(COVID-19 India, 2021), we defined two periods as, first wave (March 2020 to February 2021) and second wave (March to August 2021), three sub-periods in first wave: pre-peak (March to August 2020), peak (September-October 2020),post-peak (November 2020 to February 2021) and study setting: rural and urban. Heterogeneity was assessed by the I 2 statistics, which measures the variation across studies due to heterogeneity rather than chance. I 2 > 50% was considered as substantial heterogeneity, and Cochrane p < 0.05 was taken as a cut-off for significant heterogeneity. All statistical analyses were done using STATA 16 (Stata 16, 2021) .

We identified a total of 3,821 studies after systematically searching multiple data sources, with 3,213 identified from peer-reviewed repositories, 605 from preprint servers, and three identified from government websites or contacting relevant health experts. After excluding 1,031 duplicates and a further 2674 following the screening of titles and abstracts, 116 studies of SARS-CoV-2 serological evidence were assessed for eligibility. Four such full texts could not be retrieved online and were obtained directly from the study authors. 63 studies were excluded, resulting in a total of 53 studies (Babu et al., 2020; George et al., 2021; Ghose et al., 2020; Ghosh et al., 2021; Goenka et al., 2020; Ramasamy S, 2021; Gupta et al., 2021; Inbaraj et al., 2021; V. Jain et al., 2021; Jessy et al., 2021; Joshi et al., 2021; Kar et al., 2021; Kerala, 2021; M. S. Khan et al., 2021; S. M. S. Khan et al., 2020; Kshatri, 2021; Kshatri et al., 2021; Laxmaiah et al., 2021; Madhusudan et al., 2021; Mahto et al., 2021; Makadia et al., 2021; Malani, Ramachandran, et al., 2021; Malani, Shah, et al., 2021; Mishra et al., 2021; Mohanan et al., 2021; M. Murhekar et al., 2020; M. V. Murhekar et al., 2021; M. V. Murhekar, et al., 2021; Naushin et al., 2021; Padma et al., 2021; Parai et al., 2021; Prakash et al., 2021a.; Prakash et al., 2021b; Prakash et al., 2021c; Prakash et al., 2021d; Rafi et al., 2021; Ray et al., 2020; Satpati et al., 2020; Selvaraju et al., 2021; Sen et al., 2021; N. Sharma et al., 2020.; Siddiqui et al., 2020; Singhal et al., 2020; Siva Ganesa Karthikeyan et al., 2021; Velumani et al., 2021; Venkataraman et al., 2021) with 831,010 participants in the first wave (COVID-19sero survey-III (July 2021) -Tamil Nadu, 2021.; Misra et al., 2021; Selvaraju et al., personal communication; M. V. Murhekar, et al., 2021) with 74,369 participants in the second wave included in the systematic review and meta-analysis after full-text scrutiny (Figure 1,   Supplementary table 1) .

Most studies were done exclusively in the Indian states of Tamil Nadu (n=8), Maharashtra, Karnataka (n=6 in each) and Odisha (n=5) and by multiple stakeholders. The majority of the studies were conducted exclusively in urban (n=38) areas. Studies primarily focused on the general population (n=28) or healthcare workers (n=18) or both (n=4).

Most of the studies were of good quality (n=21) on the NOS, with 17 of them categorised as 'satisfactory' quality and only four were classified as 'very good' quality. One-fifth of the studies (n=11) were categorised as 'unsatisfactory or poor' quality ( Figure 2 ). Almost all of the high-quality (good and very good categories) seroprevalence studies identified were done in general population. There were very few high-quality studies among specific subpopulations like healthcare workers (n=4) and none among close contacts of COVID-19.

More than two-thirds of prevalence estimates (n=35) were obtained from an appropriately sized sample and equal number of studies used non-random sampling (n=33) while, one study used a non-representative sampling frame (laboratory blood samples collected for thyroid profiling). (Supplementary table 2) In addition, the studies included in the analyses provided inadequate information about the demographic, health, and exposure variables.

Importantly, the distinction of exposures reported (e.g., symptomatic and asymptomatic) in the studies relied on self-reported health status, which may not be reliable. Studies of 'unsatisfactory or poor' quality, in particular had multiple features of unjustified sample size, non-representative sample, non-classification of non-respondents, results unadjusted for demographics and methods insufficiently explained to enable replication.

Most studies used serological tests with good sensitivity (>90%) and specificity (>95%), while four studies did not report test accuracy. Estimates of many of the general population-based seroprevalence studies (17 out of 38) neither adjusted for the demographics of the study population nor for the diagnostic performance of the assay. While the response rate of 19 studies raises concerns about non-response bias, only six studies took measures to address or characterise non-responders. There were conflicts of interest in 16 studies that may affect the authors' interpretation of the results, of which only one study acknowledged it.

Information regarding ethics approval was missing in one of the studies.

Seroprevalence studies did not provide adequate information on their methods. Some of the reviewed studies did not report adequate information on study methods [sampling=24% (13/53); laboratory=83% (44/53)]. Nearly half the studies did not mention the assumptions for sample size calculation. While two-thirds (n=33) studies justified sample size (Figure 2 ), five studies did not clearly define the study population, and ten studies did not sufficiently describe the methods (including statistical methods) to enable them to be repeated. Six studies did not provide the inclusion or exclusion criteria.

Various diagnostic assays were used for the determination of seropositivity. None of the studies reviewed reported a rationale for their testing approach, and four of them did not state the sensitivity and specificity of the assays. Of the 53 studies, 25 did not describe the timing of the biological sampling in relation to the disease epidemiology in the study population (the beginning, peak, and fall of virus transmission).

Laboratory methods were not reported as per reporting standards as well. None of the studies described positive and negative controls used, starting and end dilutions, and laboratory biosafety conditions. One-third (n=17) did not describe the threshold, or other parameters used to define 'seropositivity'. A major proportion (n=44) did not describe the specimen storage conditions, while two-fifth of the studies (n=21) did not specify the antigen(s) used in the assay. Of all 53, only two studies described any known or potential immunological crossreactivity that may have biased the outcome measures.

Many studies failed to report the participant flow, number of participants at each stage, and reasons for non-response. Thirty-eight studies reported the number of individuals at each stage of the study; a little over one-fourth (n=16) used flow diagrams to illustrate the participant flow, and 11 gave reasons for non-participation at each stage. Yet, 11 out of 29 relevant studies didn't state how the non-independence of data was managed. Additionally, 19 studies did not discuss the limitations of the study.

First wave (March 2020 -February 2021 : We estimated the pooled seroprevalence to be 20.7% (95% CI=16.1% to 25.3%) in the first wave. (Figure 3a ) Seroprevalence did not differ by age and gender. Seroprevalence in the urban areas (21.6%; 95% CI=16.8% to 26.4%) was higher than in the rural areas (14.6%; 6.2% to 23.0%). (Table 1) The overall pooled seroprevalence among the general population was 22.7% (95% CI=17.4% to 27.9%), and among healthcare workers was 16.9% (95% CI=12.8% to 21.0%).

Symptomatic individuals had higher seroprevalence among both general population (41.6%, 95% CI=29.9% to 53.4%) and healthcare workers (33.3%; 95% CI=22.6% to 44.0%) than asymptomatic individuals (General population=23.4%; 95% CI=16.5% to 30.3%; healthcare workers=14.2%; 95% CI=8.1% to 20.2%). The seroprevalence among the general population also varied across states, with a higher seroprevalence in the Southern (22.2%) as compared to Northern region (12.0%). (Supplementary table 3) Second wave (March-August 2021) : We estimated pooled seroprevalence as 69.2% (95% CI= 64.5% to 73.8%) during the second wave (Figure 3b ). In contrast to the first wave, seroprevalence was lowest in the age group less than 20 years (63.1%; 95% CI: 52.8% to 73.5%) and highest in age group between 41 and 60 years (81.2%; 95% CI: 75.5% to 87.0%).

Similar to the first wave, seroprevalence was not different by gender. Seroprevalence in the urban areas (73.7%; 95% CI=67.2% to 80.1%) was higher than in the rural areas (65.1%;

95%CI=64.6% to 65.7%). (Table 1 )

We did a systematic review and meta-analysis of published reports or preprints of studies of seroprevalence of SARS-CoV-2 done among different populations across India. Majority of these were cross-sectional studies conducted in urban settings, among the general population in few Indian States, and were of satisfactory quality. Overall, one-fifth in the first wave and two in three individuals in the second wave had evidence of SARS-CoV-2 infection. Seroprevalence did not differ by age in the first wave consistent with epidemic transmission.

Whereas higher seroprevalence observed in the older age groups in the second wave could be due to prioritization of the elderly for COVID-19 vaccination. High seroprevalence observed in urban areas as compared to rural areas in both the waves could be due to high population density.

The majority of the seroprevalence studies were done in urban settings and in a limited number of Indian States by multiple stakeholders. This could have been necessitated by the significant burden of COVID-19 reported from urban settings during the first wave in contrast to the reported higher burden from small towns and rural areas during the second wave (Ranjan et al., 2021) Such results obtained by the multiple stakeholders remained unsynthesised. Additionally significant proportion of COVID-19 cases were reported in a wide variety of occupations apart from just the healthcare workers, for instance, the tourism staff (Kaushal & Srivastava, 2021) , workers from retail and hospitality industry ( Business Excellence and Management 2020), transport and security staff (Koh, 2020a) , and construction workers (Koh, 2020b) . Further, SARS-CoV-2 infection and its impact are likely to be more among those from already disadvantaged ethnic and community groups (Raju et al., 2021) . Thus, fewer studies in specific regions, populations, and exposure groups limits our understanding of SARS-CoV-2 transmission in the country. Moving forward, more appropriate sampling frames tailored to the highly uneven and inequitable distribution of COVID-19 (as determined by RT-PCR) are needed to obtain more representative estimates of seroprevalence in different study settings and among variety of exposed population groups.

Poor methodological quality in certain studies identified in the current review is reflected in a similar global review (Galanis et al., 2021) , (Bobrovitz et al., 2020) . The need for rapid assessment could have led to lack of methodological rigour and hence the validity of the generated estimates. In addition, lack of standardisation of the obtained estimates in these studies, made it difficult to compare the estimates between the studies. Though such adjustments of estimates provide a small degree of correction with large samples, it cannot be a substitute for robust sampling method. It would be prudent to use standard serosurvey protocols published by WHO (WHO, 2020) for conducting studies of methodological quality and for generating valid and somewhat comparable estimates.

The poor reporting of studies misses out on key information necessary for interpretation of study methods for potential selection and information bias. Sometimes, media reports (Hindustan Times, 2021.; India News -Times of India, 2021.; Livemint, 2021) were used for early and timely dissemination of seroprevalence study results well before they could be published in peer-reviewed literature. It is therefore essential that these media sources also provide the essential information needed for their readers to interpret their findings and give them the complete picture. Where standardisation of estimates is not done, it would be ideal to report the test details such as type of test, company name, test sensitivity and specificity to enable informed comparison of results. Use of WHO ROSES-S statement for reporting such studies would address reporting related issues.

The pooled estimates for the two waves from the current meta-analysis are different from that reported in Serotracker (SeroTracker, 2021) . This could be due to pooling of estimates from studies done during first and second waves in the Serotracker and different inclusion/exclusion criteria. For instance, Serotracker included studies among convalescent blood donors, whereas we did not. For subgroup analysis, while our categories were restricted to general population and specific special exposure groups, Serotracker categorises them into many overlapping categories (for instance: 'household and community samples' and 'multiple general populations'), which could have led to biased sub-group estimates.

According to the pooled seroprevalence estimate, more than two-thirds of the country's people had SARS-CoV-2 antibodies by August 2021, with variations by place and person characteristics. This pooled seroprevalence is higher than that of the global or regional levels (Chen et al., 2021) . This could be due to differences in the types, scale, intensity, and adherence to the public health strategies implemented across these settings (Lai et al., 2020b) .

Our study had several limitations. First, each of the included studies could have used varying thresholds for seropositivity. This makes it difficult to predict direction of bias in the generated estimates. Pooled estimate could have been influenced by different serological assays and methods with varying sensitivities and specificities employed in the included studies. Secondly, cross-reactive immunity cannot be ignored, which could have led to the overestimation of SARS-CoV-2 seroprevalence. Thirdly, seroprevalence of antibodies would have been influenced by the vaccination coverage during the second wave. However, limited information provided in the studies did not allow us to distinguish between natural and vaccine-induced antibodies. Finally, the result of the sub-group analysis did not explain the source of heterogeneity. Hence, we did not do a meta-regression to further explore the heterogeneity.

To conclude, we systematically reviewed SARS-CoV-2 seroprevalence studies in India and identified that the overall quality of the reviewed studies was of 'satisfactory' level. Our meta-analysis documented threefold increase in seroprevalence between the two waves of infection. More than two-thirds of the India's population had IgG antibodies against SARS-CoV-2 by August 2021 and was higher than that of the global or regional levels. On the basis of the findings, we recommended that studies need to be designed based on standard protocols adapted to the study setting including the use of validated standardized assays and reporting. Secondly, central registries of seroprevalence initiatives might facilitate coordinated design and conduct of studies and facilitate evidence synthesis. Finally, we recommend serial seroprevalence studies following vaccine introduction to investigate the early serological response to vaccination and track the population immunity against SARS-CoV-2.

This study was registered with PROSPERO, CRD42021254997 and reported in compliance with Preferred Reporting Items for Systematic review and Meta-analyses Protocol (PRISMA-P) checklist. The review protocol has not been published and is not available publicly.

Non-financial support for the review was provided by the ICMR-National Institute of Epidemiology.

Manoj V Murhekar, Tarun Bhatnagar, Muthusamy Santhosh Kumar were part of National COVID-19 Serosurvey group and coordinated four rounds of serosurveys in India. These authors were not involved in risk of bias assessment. 

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Ayeb-Karlsson S. COVID-19 in India: Who are we leaving behind?

Cross-sectional Study on Sero-Prevalence of SARS-CoV-2 Infection in Jabalpur

Characterization of the Second Wave of COVID-19 in India

Seroprevalence of anti-SARS-CoV-2 IgG antibodies in hospitalized patients at a tertiary referral center in North India 2020

Intelligent Systematic Review

findings from the second nationwide household serosurvey -The Lancet Global Health

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Sero-surveillance (IgG) of SARS-CoV-2 among Asymptomatic General population of Paschim Medinipur

Population-Based Serosurvey for Severe Acute Respiratory Syndrome Coronavirus 2

Prevalence of IgG antibodies against SARS-CoV-2 among the general population and healthcare workers in India

Estimating the proportion and IgG antibody response to SARS-CoV-2 in individuals joining a central educational institute from different parts of India by Enzyme-linked immunosorbent assay (ELISA) based serology

The seroprevalence and trends of SARS-CoV-2 in Delhi, India: A repeated population-based seroepidemiological study

Seroprevalence of antibodies to SARS-CoV-2 and predictors of seropositivity among employees of a teaching hospital

SARS-CoV-2 Antibody Seroprevalence and Stability in a Tertiary Care Hospital-Setting

Prevalence of COVID-19 Antibodies in Healthcare Workers at the Peak of the Pandemic in Mumbai, India: A Preliminary Study

Seroprevalence of SARS-CoV-2 specific IgG antibodies among eye care workers in South India

The Delta SARS-CoV-2 variant has a higher viral load than the Beta and the historical variants in nasopharyngeal samples from newly diagnosed COVID-19 patients

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Two-third of population surveyed in 11 states have coronavirus antibodies: ICMR serosurvey

SARS-CoV-2 Seroprevalence in 12 Cities of India from

Correlation of SARS-CoV-2 Serology and Clinical Phenotype Amongst Hospitalised Children in a Tertiary Children's Hospital in India

SARS-CoV-2 seroprevalence among health care workers in a New York City hospital: A cross-sectional analysis during the COVID-19 pandemic

Population-based age-stratified seroepidemiological investigation protocol for coronavirus 2019 (COVID-19) infection. 2021