About the Author(s)


Maemu P. Gededzha Email symbol
Department of Virology, School of Medicine, Sefako Makgatho Health Sciences University, Pretoria, South Africa

Mixo Sibiya symbol
Department of Immunology, National Health Laboratory Service, Johannesburg, South Africa

Celine Pellaton symbol
Service of Immunology and Allergy Unit, University of Lausanne, Lausanne University Hospital, Lausanne, Switzerland

Kubashni Woeber symbol
HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa

Duduzile Nsibande symbol
HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa

Nobuhle Mchunu symbol
HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa

Biostatistics Research Unit, South African Medical Research Council, Durban, South Africa

Brodie Daniels symbol
HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa

Terusha Chetty symbol
HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa

Reshmi Dassaye symbol
HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa

Khanya Mohlabi symbol
HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa

Shameem Jaumdally symbol
Centre for Lung Infection and Immunity, Division of Pulmonology, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

South African MRC Centre for the Study of Antimicrobial Resistance, University of Cape Town, Cape Town, South Africa

Keertan Dheda symbol
Centre for Lung Infection and Immunity, Division of Pulmonology, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

South African MRC Centre for the Study of Antimicrobial Resistance, University of Cape Town, Cape Town, South Africa

Faculty of Infectious and Tropical Diseases, Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom

Ruth Lekalakala symbol
Department of Pathology, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa

Medical Microbiology, Polokwane Laboratory, National Health Laboratory Service, Polokwane, South Africa

Shabir A. Madhi symbol
South African Medical Research Council: Wits Vaccines and Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Wits Infectious Diseases and Oncology Research Institute, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Elizabeth Mayne symbol
Division of Immunology, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

National Health Laboratory Service, Groote Schuur Hospital, Cape Town, South Africa

Glenda Gray symbol
HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa

Wits Infectious Diseases and Oncology Research Institute, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Yves Levy symbol
The Vaccine Research Institute, Faculty of Médicine, Université Paris-Est Créteil, Créteil, France

Craig Fenwick symbol
Department of Immunology, Henri-Mondor Albert-Chenevier Hospital, Créteil, France

Song Ding symbol
EuroVacc Foundation, Lausanne, Switzerland

Penny L. Moore symbol
SAMRC Antibody Immunity Research Unit, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa

Centre for the AIDS Programme of Research in South Africa, Durban, South Africa

Ameena Goga symbol
HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa

Department of Paediatrics and Child Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa

Citation


Gededzha MP, Sibiya M, Pellaton C, et al. Kinetics of anti-SARS-CoV-2 IgG in saliva and serum post infection, and subsequent vaccination in South Africa. Afr J Lab Med. 2026;15(1), a3052. https://doi.org/10.4102/ajlm.v15i1.3052

Original Research

Kinetics of anti-SARS-CoV-2 IgG in saliva and serum post infection, and subsequent vaccination in South Africa

Maemu P. Gededzha, Mixo Sibiya, Celine Pellaton, Kubashni Woeber, Duduzile Nsibande, Nobuhle Mchunu, Brodie Daniels, Terusha Chetty, Reshmi Dassaye, Khanya Mohlabi, Shameem Jaumdally, Keertan Dheda, Ruth Lekalakala, Shabir A. Madhi, Elizabeth Mayne, Glenda Gray, Yves Levy, Craig Fenwick, Song Ding, Penny L. Moore, Ameena Goga

Received: 08 Oct. 2025; Accepted: 30 Mar. 2026; Published: 26 June 2026

Copyright: © 2026. The Authors. Licensee: AOSIS.
This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

Abstract

Background: Understanding mucosal immune response to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and vaccination, and its correlation to systemic responses could improve vaccine development. There is limited data on mucosal and systemic antibody responses to SARS-CoV-2 infection and vaccination in African populations.

Objective: This study assessed serum and saliva antibody ratios in a cohort of SARS-CoV-2 infected and vaccinated participants followed up for 12 months.

Methods: This longitudinal study comprised of 67 SARS-CoV-2, PCR-confirmed participants who were recruited as part of a COVID-19 Point of Care Study, between January 2021 and September 2022. For this analysis, 254 serum and 214 saliva samples (205 of which were matched), were collected at baseline, 6, 9, and 12 months. Antibodies against Spike (S) and nucleocapsid (N) SARS-CoV-2 were assessed by in-house Luminex assay.

Results: Serum and saliva anti-S IgG from were detected for up to 12 months in majority of participants (93.5% and 90%), regardless of vaccination status. There was a concordance between paired serum and saliva antibody in 80.5% anti-S IgG and 41.7% anti-N IgG of samples. There was a weak but significant correlation between the matched serum and saliva samples (ρ = 0.42, p < 0.001 for anti-S and ρ = 0.33, p < 0.001 for anti-N). Longitudinal analysis in participants who were vaccinated with either the Ad26.COV2 or BNT162b2 COVID-19 vaccine after their baseline visit, serum anti-S IgG significantly increased (p = 0.023 and p = 0.038), compared with unvaccinated participants. Breakthrough infections or reinfections were identified in (35/62) 56.5% of vaccinated and unvaccinated participants.

Conclusion: This study reports the correlation of the anti-S and anti-N IgG responses in serum and saliva following SARS-CoV-2 infection and subsequent vaccination in low-middle income setting. Furthermore, high breakthroughs and reinfection were reported.

What this study adds: The study findings are consistent with those from high-income settings, supporting correlation between systemic and mucosal immunity and booster vaccination as a long-term strategy to control SARS-CoV-2.

Keywords: SARS-CoV-2; anti-S IgG; anti-N IgG; saliva; vaccination; breakthrough infection.

Introduction

South Africa, a middle-income country with high HIV prevalence was affected severely by the coronavirus disease 2019 (COVID-19) pandemic,1 and has experienced low uptake of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination. Despite the roll-out and effectiveness of COVID-19 vaccines in many countries, uncertainties remain regarding their long-term protection.2 SARS-CoV-2 infection induces humoral and cellular immune responses.3,4 Humoral immune responses include immunoglobulins (Ig) targeting SARS-CoV-2 proteins, especially the Spike (S) following vaccination, and both S and nucleocapsid (N) proteins following natural infection.5 Of these responses, anti-spike antibodies may neutralise the virus-retarding transmission.6

IgG is the most abundant immunoglobulin in the serum, and detection of anti-SARS-CoV-2 IgG contributes to evaluating infection and vaccine-induced immune responses.7,8 Severe acute respiratory syndrome coronavirus 2 anti-S IgG persists for several months after infection and correlates strongly with neutralising antibody activity.9,10 Both binding and neutralising antibodies have been associated with protection against different SARS-CoV-2 variants.11,12 Nevertheless, a recent population study highlighted that systemic antibody responses only account for a moderate proportion of protection against infection, suggesting that other immune responses may be better correlates of protection.13 SARS-CoV-2 serum antibody titres wane with time, with consequent decreased protection from infection and severe disease.13,14 Peripheral blood antibody responses may not reflect mucosal (nasal mucus and salivary) antibody responses, which may reflect better immunological protection.13,15 Mucosal neutralising antibodies are correlated with the protective efficacy of SARS-CoV-2 infection.16 Studies exploring the differences in mucosal and systemic antibody responses after SARS-CoV-2 infection, vaccination and infection mixed with vaccination, reported variability in mucosal antibody levels and durability, influenced by the type of exposure.17,18,19,20,21,22,23

Salivary IgG peaks within a month, persists for up to 9 months after SARS-CoV-2 infection,17,24 and can persist for 15 months after vaccination.25 Moreover, mucosal vaccination, which would more closely resemble natural infection, may produce more durable mucosal immunity than vaccines given intramuscularly.13,26 There are limited data on mucosal and systemic antibody responses to SARS-CoV-2 infection and vaccination in African populations. Therefore, understanding the mucosal immune response to SARS-CoV-2 infection and vaccination, and its correlation to systemic responses, is important to inform vaccine development.

This study assessed the correlation between SARS-CoV-2 serum and saliva IgG responses after vaccination, breakthrough infections and reinfections in a cohort of SARS-CoV-2 infected and vaccinated participants followed up for 12 months.

Methods

Ethical considerations

Written informed consent was obtained from participants, which included: ascertaining demographic information, COVID-19 vaccination status, HIV status, symptoms and comorbidities, and to collect venous blood in serum separator tubes and saliva samples at different time points. This study was approved by the human research ethics committee of the South African Medical Research Council (EC 005-4/2020), University of Witwatersrand (M200656), University of Limpopo (TREC/67/2020) and University of Cape Town (387/2020). The participants were allocated a unique identification number and no information was released or published by which samples can be traced back to patients.

Study design

This was a longitudinal study using available serum and saliva samples, including both paired and unpaired samples collected at different time points (baseline, 6 months, 9 months, and 12 months).

Study population and setting

The study population comprised 67 volunteers who were recruited as part of the South African Medical Research Council (SAMRC) led COVID-19 Point of Care (POC) study.27,28 The POC study included symptomatic persons under investigation (PUI) with possible acute COVID-19, and asymptomatic contacts suspected of having acute COVID-19 who presented at health facilities in two urban sites in Gauteng and the Western Cape, and one peri-urban site in Limpopo, South Africa in the period January 2021 to September 2022. During this period, South Africa experienced multiple COVID-19 waves caused by distinct SARS-CoV-2 variants. The Beta variant (B.1.351) dominated from early 2021 to May 2021, the Delta variant (B.1.617.2) from May 2021 to September 2021, and the Omicron variant (B.1.1.529) from November 2021 to September 2022.

For this study, 254 serum samples and 214 saliva samples (205 of which were matched), were considered sufficient for exploration and comparative analyses. This approach is consistent with similar studies17,19,21 where sample numbers were limited.

Participants who tested positive for COVID-19 underwent the POC rapid tests,28 and were then tested routinely for SARS-CoV-2 with real-time quantitative polymerase chain reaction on nasal or oropharyngeal swabs using a GeneXpert SARS-CoV-2 (Cepheid, Sunnyvale, California, USA), ThermoFisher TaqPath assay (Thermo Fisher Scientific, Waltham, Massachusetts, USA), or Seegene Allplex SARS-CoV-2 assay (Seegene Inc., Seoul, South Korea) at the National Health Laboratory Service per routine national protocols. The assays were used for qualitative SARS-CoV-2 detection, and results were reported as positive or negative. The participants were recruited based on laboratory-confirmed SARS-CoV-2 infection, regardless of possible prior infection.

Sample collection

The POC study enrolled symptomatic PUI with possible acute COVID-19, and asymptomatic contacts suspected of having acute COVID-19 presenting to health facilities in two urban sites in Gauteng, and Western Cape and one peri-urban site in Limpopo in South Africa between January 2021 and September 2022.

Venous blood (up to two tubes of venous blood in serum separator tubes) and 1 mL saliva samples were collected from participants at baseline 6 months, 9 months, and 12 months. The samples were transported in coolers with icepacks to the Clinical Laboratory Services (CLS) biorepository, Braamfontein. Serum tubes were centrifuged at 3500 rpm for 15 min on either a ROTINA 420R or 460R centrifuges (Andreas Hettich GmbH & Co. KG, Tuttlingen, Germany). Serum and saliva were stored at −80 °C in 200 µL aliquots.

Laboratory analysis: Luminex serological binding assay

Antibodies against Spike (S) and nucleocapsid (N) SARS-CoV-2 were assessed by Luminex xMAP multiplexed bead-based technology,29 where S and N proteins were coupled to Luminex beads as described by the manufacturer (Bio-Plex amine coupling kit, Bio-Rad Laboratories, Hercules, California, USA). Briefly, 50 µL of the 1/300 diluted serum samples, together with negative and positive controls, were incubated with 1:100 S and N beads and read on a Luminex 200 (Bio-Rad Laboratories, Hercules, California, USA). In this semi-quantitative assay, the mean fluorescent intensity signals for the anti-S and anti-N IgG serum binding were expressed as ratios compared to a negative internal control of pooled pre-COVID-19 pandemic human serum. The mean fluorescent intensity signal (in relative fluorescence units) for each test serum sample was divided by the mean signal for the negative-control samples to yield a mean fluorescent intensity ratio that was used as normalised units or values between plates and the different Luminex instruments tested.29 Saliva samples (250 µL) were treated with 1 × phosphate-buffered saline (Sigma-Aldrich, St. Louis, Missouri, USA), 0.5% Triton X 100 (Sigma-Aldrich, St. Louis, Missouri, USA), and 0.02% sodium azide (Sigma-Aldrich, St. Louis, Missouri, USA). They were vortexed briefly, centrifuged at 14 000×g for 10 min at 4 °C and the 1/27 dilution supernatants, together with the negative control (phosphate-buffered saline), were assayed for anti-S and anti-N IgG, as described above.29

Data analysis

Data were captured, cleaned and validated using Microsoft Access (Microsoft Office, 2021; Microsoft, Redmond Washington, USA). Continuous variables were tested for normality using the Shapiro-Wilk test. All showed departures from normality. The Spearman correlation (ρ) was used to assess correlations between saliva and serum ratios. Continuous variables were assessed using alpha and were thus presented as medians with interquartile ranges (IQRs). The Wilcoxon matched-pairs signed rank test was used to compare differences in antibody ratios in groups (i.e. within vaccine comparisons of different follow-up time points and saliva and serum comparisons within different follow-up time points). Categorical variables were summarised using frequencies and percentages. The Kruskal–Wallis test was used to assess the association between the re-infection and breakthrough infection individuals. A p ≤ 0.05 was considered significant. All statistical analyses were performed using GraphPad Prism 10 (GraphPad Software, San Diego, California, USA) and Stata software version 18 (Stata Statistical Software, StataCorp LLC, College Station, Texas, USA).

Results

Characteristics of the participants

The study included 67 participants, comprising 42 women with median ages of 48 years old, and 25 men with median ages of 45 years old. Symptoms were scored as mild (upper respiratory tract infections only) and moderate (lower respiratory tract symptoms, high fever or severe gastrointestinal symptoms).30 Most participants had mild-to-moderate COVID-19 symptoms, with both mild and moderate symptoms being slightly more common among men (40% and 48%) than women (26.2% and 40.5%). HIV positivity was self-reported only in women (11.9%), while seems an odd word to use here, seeing as everything refers to female patients comorbidities such as diabetes and hypertension were more common in women, with six individuals reporting both conditions. Only men reported having tuberculosis, asthma, and chronic obstructive pulmonary disease. Among the vaccinated participants (n = 31), the majority of the participants received the Pfizer BioNTech BNT162b2 (74.2%) as compared to the Johnson & Johnson Ad26.COV2 (25.8%) vaccine after their baseline visit. A significant portion remained unvaccinated throughout the study. All 67 participants had serum and saliva samples, of which 53 participants were paired at baseline, 45 participants at 6 months, 57 participants at 9 months, and 50 participants at 12 months (Table 1a and Table 1b).

TABLE 1a: Age median and interquartile range of participants in the study.
TABLE 1b: Characteristics of participants included in the study.
Baseline and longitudinal seroprevalence

Serum anti-S IgG was detected in 87.6% (95% confidence interval [CI], 77.1–94.5) at baseline, and in 96.6% (95% CI, 88.2–99.5), 98.4% (95% CI, 91.4–99.9) and 93.5% (95% CI, 84.2–98.2) at 6-months, 9-months, and 12-months time points. Salivary anti-S IgG was detected in 61.1% (95% CI, 46.8–74.1) of individuals at baseline, which showed a slight increase at 6-months (88.8%; 95% CI, 75.9–96.2), 9-months (86.8%; 95% CI, 75.7–94.1), and 12-months (90%; 95% CI, 78.2–96.6) time points.

Serum anti-N IgG was detected in 75.3% (95% CI, 63.1–85.2) at baseline, and in 81.3% (95% CI, 69.1–90.3), 79.3% (95% CI, 67.3–88.5) and 72.5% (95% CI, 59.7–83.1) at 6-month, 9-month, and 12-month time points. Salivary anti-N IgG was more variable and lower, detected in 31.4% (95% CI, [19.5–45.5]) of individuals at baseline, and gradually decreasing to 22.9% (95% CI, 12.0–37.3) by 12 months.

Relationship of anti-S and anti-N responses in serum and saliva

Pooled data showed that there was concordance at any of the time points between the matched serum and saliva IgG antibody ratios in 165/205 (80.5%) of anti-S IgG and 85/204 (41.7%) of anti-N IgG results. There was a moderate positive correlation (ρ = 0.42, p < 0.001 for anti-S) and a weak (ρ = 0.33, p < 0.001 for anti-N), but significant correlation between the matched serum and saliva samples (despite the fact that saturation (anti-S ratio of 120 and anti-N ratio of 25) was reached for many of the serum samples, for both anti-S and anti-N (Figure 1).

FIGURE 1: Correlation analysis of anti-S and anti-N IgG between (a) 205 serum and (b) 204 saliva samples across a 12-month follow-up period.

The POC study enrolled symptomatic PUI with possible acute COVID-19, and asymptomatic contact suspected of having acute COVID-19 presenting to health facilities in two urban sites in Gauteng and the Western Cape, and one peri-urban site in Limpopo in South Africa between January 2021 and September 2022.

Longitudinal changes in serum and saliva ratios

The change in antibody ratios over time was quantified, by comparing IgG antibody ratios (relative to a negative control pool of pre-COVID-19 pandemic human serum) at four time points (baseline, 6, 9, and 12 months). The median ratio of anti-S IgG was higher compared with baseline at each of the subsequent time points in both the serum and saliva samples (Figure 2).

FIGURE 2: Longitudinal analysis of Anti-S and Anti-N ratios in serum and saliva across the participants at indicated time points.

Serum anti-S ratios increased from a baseline median of 76.3 (IQR: 22.4–118.1) to 113.5 (IQR: 75.9–121.4) at 6 months (p = 0.003), remained stable at 9 months [111.0 (81.0–120.2); p = 0.934], and then declined to 101.9 (65.1–118.7) at 12 months (p = 0.049). In contrast, serum anti-N levels remained unchanged from baseline [17.6 (6.4–23.9)] to 6 months [17.6 (9.0–27.4)] (p = 0.424), but significantly decreased at 9 months [15.1 (8.4–23.9)] (p = 0.011) (see Figure 2).

Correlation of antibody titres by comorbidities or symptom severity

The participants with moderate COVID-19 had higher anti-N serum ratios compared to asymptomatic individuals at 6 months (23.7 [12.9–28.3] vs 11.5 [7.8–15.8]; p = 0.0095), 9 months (17.1 [10.2–25.6] vs 8.6 [3.9–13.1]; p = 0.013) and 12 months (15.8 [10.2–24.2] vs 10.8 [5.5–12.3]; p = 0.004). The serum anti-N IgG ratios were higher for participants with comorbidities than for those without comorbidities at 6 months (22.7 [16.9–28.1] vs. 13.9 [8.8–24.0]; p = 0.042), 9 months (24.6 [14.7–26.8] vs. 10.7 [4.9–17.9]; p = 0.001), and 12 months (16.9 [12.1–25.9] vs. 10.7 [4.9–17.9]; p = 0.028).

Antibody responses by vaccine

Longitudinal analysis for serum anti-S IgG ratios in participants vaccinated with BNT162b2 increased significantly from baseline (94.7 [27.4–118.4]), to 9 months (102.8 [88.6–120.0]), p = 0.038, while in the Ad26.COV2 participants, anti-S IgG ratios increased from baseline (71.8 [10.2–103.9]) to 6 months (119.7 [114.3–121.8]), p = 0.023. There was no statistical difference in the serum anti-S IgG ratios for the unvaccinated participants. The serum anti-S IgG ratios remained stable at 12 months for both Ad26.CoV2 and BNT162b2, while unvaccinated decreased slightly, although not significantly.

The saliva anti-S IgG ratios for participants vaccinated with BNT162b2 increased significantly, from baseline (14.1 [4.0–104.5]) to 6 months (233.6 [77.0–433.7]), p < 0.001, and decreased from 6 months (233.6 [77.0–433.7]) to 9 months (44.0 [22.5–241.7]), p = 0.008, and from 6 months (233.6 [77.0–433.7]) to 12 months (58.1 [15.1–169.2]), p = 0.008. Ad26.COV2 increased at different time points, albeit not significantly. However, the saliva anti-S IgG ratios of unvaccinated participants also increased significantly from baseline (7.0 [2.0–24.0]) to 9 months (44.7 [12.3–161.7]), p = 0.003. The saliva anti-S ratio for all three groups (Ad26.CoV2, BNT162b2, and unvaccinated) were at a similar level at 12 months. The saliva anti-N IgG ratios decreased significantly from baseline to 12 months in both Ad26.CoV2 (4.6 [2.5–32.1] vs. 2.0 [1.4–2.6], p = 0.031) and BNT162b2 (5.3 [0.9–332.0] vs. 2.8 [1.4–5.5], p = 0.027). There was no difference with the serum anti-N IgG ratios (Figure 3).

FIGURE 3: Longitudinal analysis of Anti-S and Anti-N ratios in serum and saliva according to vaccination status at different time points (Baseline (0), 6, 9, and 12 months). (a) Saliva Anti-IgG, (b) Serum Anti-S IgG, (c) Saliva Anti-N IgG and (d) Serum Anti-N IgG.

Breakthrough infections or reinfections

Breakthrough infections or reinfections were identified in 56.5% (35/62) of participants, based on a significant increase (> 2-fold, average increase of 3.84-fold) in serum anti-N IgG between time points, with 58% (18/31) of vaccinated participants having a breakthrough infection, and 54.8% (17/31) of unvaccinated participants having a reinfection. Breakthrough infections were identified in both the BNT162b2 (56.5%, 13/23) and Ad26.COV2 (75%, 6/8) groups between one to 12 months post vaccination. Participants with breakthrough infections (27.7 [10.2–118.4] and 108.1 [82.4–118.2]; p = 0.016) or reinfections (34.3 [15.9–79.9] and 104.5 [76.3–118.5]; p = 0.036) had lower serum anti-S IgG at baseline compared to those without (Figure 4). There was no statistical difference in the saliva anti-S IgG when comparing breakthrough infections or reinfections to those without (data not shown).

FIGURE 4: Baseline analysis of serum anti-S IgG comparing participants with re-infection and breakthrough in unvaccinated and vaccinated participants.

Discussion

Assessing the robustness and persistence of antibody responses to infection and vaccination at the mucosal surface provides important data for understanding long-term immunity and vaccine efficacy.31 This paired saliva and serum study showed similar anti-S IgG responses in the mucosal and systemic compartments but significant differences in the anti-N IgG, following SARS-CoV-2 infection and vaccination. The overall prevalence of serum anti-S IgG and anti-N IgG was higher than that reported in a large South African seroepidemiological survey conducted from 22 October 2021 to 09 December 2021, consisting mainly of unvaccinated participants.32 Our study reported that saliva anti-S IgG ratios were detected for up to 12 months in the majority of participants, co-existing with serum ratios (90% vs. 93.5%). Persistent salivary IgG has been reported previously in natural SARS-CoV-2 infection17 and COVID-19 vaccination,25 supporting that saliva can be used as a matrix to assess mucosal immunity.

Despite the assay detecting anti-S and anti-N IgG in both saliva and serum, the data showed a weak, yet significant correlation between the serum and saliva anti-S and anti-N IgG across all time points. Although several studies have reported a significant correlation between serum and saliva,18,33,34 this study is in concordance with a previous study in the United Kingdom that reported a weak correlation among healthcare workers.23 The weak correlation in this study may be a result of lower levels of IgG in the saliva samples, especially in anti-N, as reported previously.35 There was a 1-log difference between the anti-S IgG in the saliva and the serum. Another study has reported a correlation despite saliva levels being approximately 3 log lower as compared to plasma levels.36 Similar to previous studies, anti-S IgG levels in this study were higher and persisted for a longer period than anti-N IgG.35,37,38 The detection of antibodies in the saliva, although with weak correlation, suggests that saliva can be used as an alternative specimen to assess immunity.39 Higher levels of anti-N IgG levels were reported in the mild and moderate COVID-19 and comorbid participants, and this is in agreement with previous studies.40,41

The study revealed a significant rise in the serum anti-S IgG levels following vaccination, supporting the ability to generate robust spike-specific antibodies in both Ad26.COV2 and BNT162b2 vaccines demonstrated in previous studies.42,43 Vaccine administration in previously SARS-CoV-2-infected participants has been shown to induce high levels of durable binding and neutralising antibody responses compared to uninfected participants, which might lead to enhanced systemic and mucosal antibody responses, protecting against progression to severe disease.44,45,46 However, care should be taken in interpreting these differences, since anti-S IgG levels were high and often at the upper detection limit of the Luminex assay under the experimental conditions. In addition, a study performed in South Africa reported a boost in anti-S IgG titres in Ad26.COV2.S-vaccinated South African patients with prior infection.47

This study is in agreement with previous studies, which have shown that saliva IgG antibodies in increase after BNT162b2 vaccination in participants with previous infection45,46,48,49,50 and natural infection,45 although at a low level, indicating an intense immune response in the mucosa which might lead to protection against SARS-COV-2 infection. Although Ad26.COV2 may be effective systemically, the modest increase in saliva IgG antibodies may reflect limited mucosal boosting capacity owing to a single dose.

This study also reported, in a cohort with prior SARS-CoV-2 infection, breakthrough infection in vaccinated and reinfection in unvaccinated participants, as measured by the significant increase in the anti-N serum IgG levels. Both vaccines target the spike protein,51 and the nucleocapsid is not included in the vaccines.52 The reported breakthrough infection rate (58%) in both vaccines is similar to a study in Pakistan that reported a 67% breakthrough infection rate in participants without prior infection,53 but higher than previously reported studies showing 13%54 and 3.9%.55 Similar to other studies, the low antibody levels in both serum and saliva samples prior to breakthrough infection have been reported previously.56,57 Low anti-S titres may serve as an indicator of poor prognosis in breakthrough cases,58 thus exposing the participants to a risk of reinfection. It is worth noting that the majority of participants developed breakthrough infections more than 3 months post vaccination. In addition, the majority of the breakthrough infections in this study occurred between November 2021 and May 2022, when the more transmissible omicron variants were circulating in South Africa.59 It has been reported previously that individuals infected with Omicron variants have a high rate of breakthrough and reinfections.60,61 A study by Moreira et al. highlighted the importance of the third dose of BNT162b2 vaccine in reducing infections.62 Breakthrough infections post-vaccination have been reported to boost neutralising antibodies and to contribute to high levels of immunity.63 The persistent duration of SARS-CoV-2 IgG in saliva and the increase in the number of participants positive for anti-S IgG levels over time suggest sustained mucosal immunity and potentially enhanced protection at mucosal surfaces.

Limitations

Limitations of this study include the measurement of IgG responses only. It is possible that other types of antibodies, including anti-S mucosal IgA antibodies, may assess mucosal immunity better. Moreover, not all participants were vaccinated, and the severity of breakthrough or re-infections could not be assessed, thus reducing the ability to assess the effect of vaccination on subsequent infections authentically. The difference in the number of men and women is most likely because of volunteer participation, as women are generally more willing to participate in health-related research than men.

Conclusion

In conclusion, this longitudinal study reports the correlation of the anti-S and anti-N IgG responses in serum and saliva following SARS-CoV-2 infection and subsequent vaccination. In addition, the level of protection from breakthrough and re-infections with SARS-CoV-2 appears to be related to some extent to antibody levels, and to the waning of IgG antibodies. This warrants vaccination and/or booster vaccination as a long-term strategy to control SARS-CoV-2, although the results of this study must be interpreted with caution because of the small sample size.

Acknowledgements

The authors acknowledge all the participants in this study, the South African Medical Research Council for funding, the National Health Laboratory Services for providing the rapid test kits, and the study teams within each province.

Competing interests

The authors, Maemu P. Gededzha and Penny L. Moore reported that they have received funding from institutions that may be affected by the research reported in the enclosed publication. Maemu P. Gededzha has received funding from the National Research Foundation Thuthuka, and Penny L. Moore received funding from the South African Medical Research Council Strategic Health Innovations Department, the South African Research Chairs Initiative of the Department of Science and Innovation, and the National Research Foundation. They have disclosed those interests fully and have in place an approved plan for managing any potential conflicts arising from that involvement. The authors, Maemu P. Gededzha, Mixo Sibiya, Celine Pellaton, Kubashni Woeber, Duduzile Nsibande, Nobuhle Mchunu, Brodie Daniels, Terusha Chetty, Reshmi Dassaye, Khanya Mohlabi, Shameem Jaumdally, Keertan Dheda, Ruth Lekalakala, Shabir A. Madhi, Elizabeth Mayne, Glenda Gray, Yves Levy, Craig Fenwick, Song Ding, Penny L. Moore, and Ameena Goga declare that this project is part of the European and Developing Countries Clinical Trials Partnership (EDCTP) programme supported by the European Union. All authors confirmed that they have no other interests to declare that may have inappropriately influenced them in writing this article.

CRediT authorship contribution

Maemu P. Gededzha: Methodology, Formal analysis, Funding acquisition, Writing - original draft, Writing - review & editing. Mixo Sibiya: Methodology, Software, Writing - review & editing. Celine Pellaton: Methodology, Formal analysis, Software, Writing - review & editing. Kubashni Woeber: Data curation, Project administration, Writing - review & editing. Duduzile Nsibande: Data curation, Project administration, Writing - review & editing. Nobuhle Mchunu: Formal analysis, Writing - review & editing. Brodie Daniels: Data curation, Project administration, Writing - review & editing. Terusha Chetty: Data curation, Project administration, Writing - review & editing. Reshmi Dassaye: Data curation, Project administration, Writing - review & editing. Khanya Mohlabi: Data curation, Project administration, Writing - review & editing. Shameem Jaumdally: Conceptualisation, Data curation, Writing - review & editing. Keertan Dheda: Conceptualisation, Data curation, Writing - review & editing. Ruth Lekalakala: Conceptualisation, Data curation, Writing - review & editing. Shabir A. Madhi: Conceptualisation, Writing - review & editing. Elizabeth Mayne: Conceptualisation, Writing - review & editing. Glenda Gray: Conceptualisation, Funding acquisition, Supervision, Writing - review & editing. Yves Levy: Conceptualisation, Methodology, Formal analysis, Software, Supervision, Writing - review & editing. Craig Fenwick: Conceptualisation, Methodology, Formal analysis, Funding acquisition, Supervision, Writing - review & editing. Song Ding: Conceptualisation, Funding acquisition, Supervision, Writing - review & editing. Penny L. Moore: Conceptualisation, Writing - review & editing, Supervision, Funding acquisition. Ameena Goga: Conceptualisation, Funding acquisition, Supervision, Writing - review & editing. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission, and publication, and take responsibility for the integrity of its findings.

Sources of support

This project is part of EDCTP programme supported by the European Union (grant no. RIA2020EF-2977-ASCENT). The author, Maemu P. Gededzha, is supported by National Research Foundation Thuthuka (grant no. TTK210409593292) and author, Penny L. Moore, is supported by the South African Medical Research Council Strategic Health Innovations Department, the South African Research Chairs Initiative of the Department of Science and Innovation, and the National Research Foundation (grant no. 98341).

Data availability

The data that support the findings of this study are not openly available owing to reasons of sensitivity and are available from the corresponding author, Maemu P. Gededzha, upon reasonable request.

Disclaimer

The views and opinions expressed in this article are those of the authors and are the product of professional research. The article does not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The authors are responsible for the results, findings, and content of this article.

References

  1. Garenne M, Stiegler N. Covid-19 demography in France and South Africa: A comparative study of morbidity and mortality in 2020-2022. PLoS One. 2024;19(2):e0294870. https://doi.org/10.1371/journal.pone.0294870
  2. Hogan AB, Wu SL, Toor J, et al. Long-term vaccination strategies to mitigate the impact of SARS-CoV-2 transmission: A modelling study. PLoS Med. 2023;20(11):e1004195. https://doi.org/10.1371/journal.pmed.1004195
  3. Gudbjartsson DF, Norddahl GL, Melsted P, et al. Humoral immune response to SARS-CoV-2 in Iceland. N Engl J Med. 2020;383(18):1724–1734. https://doi.org/10.1056/NEJMoa2026116
  4. Olafsdottir TA, Bjarnadottir K, Norddahl GL, et al. HLA alleles, disease severity, and age associate with T-cell responses following infection with SARS-CoV-2. Commun Biol. 2022;5(1):914. https://doi.org/10.1038/s42003-022-03893-w
  5. Pelleau S, Woudenberg T, Rosado J, et al. Kinetics of the severe acute respiratory syndrome coronavirus 2 antibody response and serological estimation of time since infection. J Infect Dis. 2021;224(9):1489–1499. https://doi.org/10.1093/infdis/jiab375
  6. Jacobs JJL. Neutralizing antibodies mediate virus-immune pathology of COVID-19. Med Hypotheses. 2020;143:109884. https://doi.org/10.1016/j.mehy.2020.109884
  7. Jarlhelt I, Pérez-Alós L, Bayarri-Olmos R, et al. Distinguishing SARS-CoV-2 infection and vaccine responses up to 18 months post-infection using nucleocapsid protein and receptor-binding domain antibodies. Microbiol Spectr. 2023;11(5):e0179623. https://doi.org/10.1128/spectrum.01796-23
  8. Ebrahim F, Tabal S, Lamami Y, et al. Anti-SARS-CoV-2 IgG Antibodies post-COVID-19 or post-vaccination in Libyan population: Comparison of four vaccines. Vaccines (Basel). 2022;10(12):2002. https://doi.org/10.3390/vaccines10122002
  9. Iyer AS, Jones FK, Nodoushani A, et al. Persistence and decay of human antibody responses to the receptor binding domain of SARS-CoV-2 spike protein in COVID-19 patients. Sci Immunol. 2020;5(52):eabe0367. https://doi.org/10.1126/sciimmunol.abe036
  10. Scozzari G, Costa C, Migliore E, et al. Prevalence, persistence, and factors associated with SARS-CoV-2 IgG seropositivity in a large cohort of healthcare workers in a tertiary care university hospital in Northern Italy. Viruses. 2021;13(6):1064. https://doi.org/10.3390/v13061064
  11. Khoury DS, Cromer D, Reynaldi A, et al. Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-CoV-2 infection. Nat Med. 2021;27(7):1205–1211. https://doi.org/10.1038/s41591-021-01377-8
  12. Regev-Yochay G, Lustig Y, Joseph G, et al. Correlates of protection against COVID-19 infection and intensity of symptomatic disease in vaccinated individuals exposed to SARS-CoV-2 in households in Israel (ICoFS): a prospective cohort study. Lancet Microbe. 2023;4(5):e309–e318. https://doi.org/10.1016/S2666-5247(23)00012-5
  13. Sun K, Bhiman JN, Tempia S, et al. SARS-CoV-2 correlates of protection from infection against variants of concern. Nat Med. 2024;30(10):2805–2812. https://doi.org/10.1038/s41591-024-03131-2
  14. Jacobsen H, Sitaras I, Katzmarzyk M, et al. Systematic review and meta-analysis of the factors affecting waning of post-vaccination neutralizing antibody responses against SARS-CoV-2. NPJ Vaccines. 2023;8(1):159. https://doi.org/10.1038/s41541-023-00756-1
  15. Li H, Wang Y, Ji M, et al. Transmission routes analysis of SARS-CoV-2: A systematic review and case report. Front Cell Dev Biol. 2020;8:618. https://doi.org/10.3389/fcell.2020.00618
  16. Butler SE, Crowley AR, Natarajan H, et al. Distinct features and functions of systemic and mucosal humoral immunity among SARS-CoV-2 convalescent individuals. Front Immunol. 2020;11:618685. https://doi.org/10.3389/fimmu.2020.618685
  17. Alkharaan H, Bayati S, Hellstrom C, et al. Persisting salivary IgG Against SARS-CoV-2 at 9 months after mild COVID-19: A complementary approach to population surveys. J Infect Dis. 2021;224(3):407–414. https://doi.org/10.1093/infdis/jiab256
  18. Egorov AI, Griffin SM, Fuzawa M, et al. A multiplex noninvasive salivary antibody assay for SARS-CoV-2 infection and its application in a population-based survey by mail. Microbiol Spectr. 2021;9(2):e0069321. https://doi.org/10.1128/Spectrum.00693-21
  19. Wright PF, Prevost-Reilly AC, Natarajan H, et al. Longitudinal systemic and mucosal immune responses to SARS-CoV-2 infection. J Infect Dis. 2022;226(7):1204–1214. https://doi.org/10.1093/infdis/jiac065
  20. Chan RWY, Liu S, Cheung JY, et al. The mucosal and serological immune responses to the novel coronavirus (SARS-CoV-2) vaccines. Front Immunol. 2021;12:744887. https://doi.org/10.3389/fimmu.2021.744887
  21. Puhach O, Bellon M, Adea K, et al. SARS-CoV-2 convalescence and hybrid immunity elicits mucosal immune responses. EBioMedicine. 2023;98:104893. https://doi.org/10.1016/j.ebiom.2023.104893
  22. Mitsi E, Diniz MO, Reine J, et al. Respiratory mucosal immune memory to SARS-CoV-2 after infection and vaccination. Nat Commun. 2023;14(1):6815. https://doi.org/10.1038/s41467-023-42433-w
  23. Faustini SE, Cook A, Hill H, et al. Saliva antiviral antibody levels are detectable but correlate poorly with serum antibody levels following SARS-CoV-2 infection and/or vaccination. J Infect. 2023;87(4):328–335. https://doi.org/10.1016/j.jinf.2023.07.018
  24. Chellamuthu P, Angel AN, MacMullan MA, et al. SARS-CoV-2 specific IgG antibodies persist over a 12-month period in oral mucosal fluid collected from previously infected individuals. Front Immunol. 2021;12:777858. https://doi.org/10.3389/fimmu.2021.777858
  25. Pinilla YT, Heinzel C, Caminada LF, et al. SARS-CoV-2 antibodies are persisting in saliva for more than 15 months after infection and become strongly boosted after vaccination. Front Immunol. 2021;12:798859. https://doi.org/10.3389/fimmu.2021.798859
  26. Anggraeni R, Ana ID, Wihadmadyatami H. Development of mucosal vaccine delivery: An overview on the mucosal vaccines and their adjuvants. Clin Exp Vaccine Res. 2022;11(3):235–248. https://doi.org/10.7774/cevr.2022.11.3.235
  27. Goga A, Mayne ES, Woeber K, et al. Point-of-care antibody tests for COVID-19: Field-based performance, South Africa. In Special Issue: Abstracts From the 2022 Conference on Retroviruses and Opportunistic Infections_Abstract823-4 (1). Top Antiv Med [serial online]. 2022 [cited 2025 Feb 25];30(1):331. Available from: https://www.researchgate.net/publication/361164561_point_of_care_antibody_tests_during_covid-19_field-based_performance_south_africa
  28. Goga A, Mayne ES, Woeber K, et al. Utility of Covid-19 point of care antigen tests in low-middle income settings. Top Antiv Med. 2022;30(11):331. https://hdl.handle.net/11288/598210
  29. Fenwick C, Croxatto A, Coste AT, et al. Changes in SARS-CoV-2 Spike versus nucleoprotein antibody responses impact the estimates of infections in population-based seroprevalence studies. J Virol. 2021;95(3):e01828-20. https://doi.org/10.1128/JVI.01828-20
  30. WHO. Clinical management of COVID-19: Living guidance [homepage on the Internet]. World Health Organization; 2021 [cited 2025 Sep 10]. Available from: https://www.who.int/publications/i/item/B09467
  31. Zhang Z, Hong W, Zhang Y, Li X, Que H, Wei X. Mucosal immunity and vaccination strategies: Current insights and future perspectives. Mol Biomed. 2025;6(1):57. https://doi.org/10.1186/s43556-025-00301-7
  32. Madhi SA, Kwatra G, Myers JE, et al. Population immunity and Covid-19 severity with Omicron variant in South Africa. N Engl J Med. 2022;386(14):1314–1326. https://doi.org/10.1056/NEJMoa2119658
  33. Heaney CD, Pisanic N, Randad PR, et al. Comparative performance of multiplex salivary and commercially available serologic assays to detect SARS-CoV-2 IgG and neutralization titers. J Clin Virol. 2021;145:104997. https://doi.org/10.1016/j.jcv.2021.104997
  34. Li D, Calderone R, Nsouli TM, Reznikov E, Bellanti JA. Salivary and serum IgA and IgG responses to SARS-CoV-2-spike protein following SARS-CoV-2 infection and after immunization with COVID-19 vaccines. Allergy Asthma Proc. 2022;43(5):419–430. https://doi.org/10.2500/aap.2022.43.220045
  35. Cohen JI, Dropulic L, Wang K, et al. Comparison of levels of nasal, salivary, and plasma antibody to severe acute respiratory syndrome coronavirus 2 during natural infection and after vaccination. Clin Infect Dis. 2023;76(8):1391–1399. https://doi.org/10.1093/cid/ciac934
  36. Klingler J, Lambert GS, Itri V, et al. Detection of antibody responses against SARS-CoV-2 in plasma and saliva from vaccinated and infected individuals. Front Immunol. 2021;12:759688. https://doi.org/10.3389/fimmu.2021.759688
  37. Bolotin S, Tran V, Osman S, et al. SARS-CoV-2 seroprevalence survey estimates are affected by anti-nucleocapsid antibody decline. J Infect Dis. 2021;223(8):1334–1338. https://doi.org/10.1093/infdis/jiaa796
  38. Van Elslande J, Oyaert M, Lorent N, et al. Lower persistence of anti-nucleocapsid compared to anti-spike antibodies up to one year after SARS-CoV-2 infection. Diagn Microbiol Infect Dis. 2022;103(1):115659. https://doi.org/10.1016/j.diagmicrobio.2022.115659
  39. Guerra ENS, De Castro VT, Amorim Dos Santos J, Acevedo AC, Chardin H. Saliva is suitable for SARS-CoV-2 antibodies detection after vaccination: A rapid systematic review. Front Immunol. 2022;13:1006040. https://doi.org/10.3389/fimmu.2022.1006040
  40. Movsisyan M, Truzyan N, Kasparova I, et al. Tracking the evolution of anti-SARS-CoV-2 antibodies and long-term humoral immunity within 2 years after COVID-19 infection. Sci Rep. 2024;14(1):13417. https://doi.org/10.1038/s41598-024-64414-9
  41. Batra M, Tian R, Zhang C, et al. Role of IgG against N-protein of SARS-CoV2 in COVID19 clinical outcomes. Sci Rep. 2021;11(1):3455. https://doi.org/10.1038/s41598-021-83108-0
  42. Gray G, Collie S, Goga A, et al. Effectiveness of Ad26.COV2.S and BNT162b2 vaccines against omicron variant in South Africa. N Engl J Med. 2022;386:2243–2245. https://doi.org/10.1056/NEJMc2202061
  43. Sadoff J, Gray G, Vandebosch A, et al. Safety and efficacy of single-dose Ad26.COV2.S vaccine against Covid-19. N Engl J Med. 2021;384(23):2187–2201. https://doi.org/10.1056/NEJMoa2101544
  44. O’Shea KM, Schuler CF, Chen J, et al. Wild-type SARS-CoV-2 neutralizing immunity decreases across variants and over time but correlates well with diagnostic testing. Front Immunol. 2023;14:1055429. https://doi.org/10.3389/fimmu.2023.1055429
  45. Paul MJ, Hudda MT, Pallett S, et al. Mucosal immune responses to SARS-CoV-2 infection and COVID-19 vaccination. Vaccine. 2025;56:127175. https://doi.org/10.1016/j.vaccine.2025.127175
  46. Gorochov G, Ropers J, Launay O, et al. Serum and salivary IgG and IgA response after COVID-19 messenger RNA vaccination. JAMA Netw Open. 2024;7(4):e248051. https://doi.org/10.1001/jamanetworkopen.2024.8051
  47. Keeton R, Richardson SI, Moyo-Gwete T, et al. Prior infection with SARS-CoV-2 boosts and broadens Ad26.COV2.S immunogenicity in a variant-dependent manner. Cell Host Microbe. 2021;29(11):1611.e5–1619.e5. https://doi.org/10.1016/j.chom.2021.10.003
  48. Ketas TJ, Chaturbhuj D, Portillo VMC, et al. Antibody responses to SARS-CoV-2 mRNA vaccines are detectable in saliva. Pathog Immun. 2021;6(1):116–134. https://doi.org/10.20411/pai.v6i1.441
  49. Nguyen K, Relja B, Epperson M, et al. Salivary immune responses after COVID-19 vaccination. PLoS One. 2024;19(9):e0307936. https://doi.org/10.1371/journal.pone.0307936
  50. Azzi L, Dalla Gasperina D, Veronesi G, et al. Mucosal immune response in BNT162b2 COVID-19 vaccine recipients. EBioMedicine. 2022;75:103788. https://doi.org/10.1016/j.ebiom.2021.103788
  51. Islam MA. A review of SARS-CoV-2 variants and vaccines: Viral properties, mutations, vaccine efficacy, and safety. Infect Med (Beijing). 2023;2(4):247–261. https://doi.org/10.1016/j.imj.2023.08.005
  52. Gaeta A, Angeloni A, Napoli A, et al. Anti-N SARS-CoV-2 assays for evaluation of natural viral infection. J Immunol Methods. 2023;518:113486. https://doi.org/10.1016/j.jim.2023.113486
  53. Hussain A, Sarfaraz S, Anis S, et al. Breakthrough COVID-19 infections – Analyzing our experience. Pak J Med Sci. 2023;39(5):1225–1231. https://doi.org/10.12669/pjms.39.5.6724
  54. Serwanga J, Kato L, Oluka GK, et al. The single-dose Janssen Ad26.COV2.S COVID-19 vaccine elicited robust and persistent anti-spike IgG antibody responses in a 12-month Ugandan cohort. Front Immunol. 2024;15:1384668. https://doi.org/10.3389/fimmu.2024.1384668
  55. Vivaldi G, Jolliffe DA, Faustini S, et al. Correlation between postvaccination anti-spike antibody titers and protection against breakthrough severe acute respiratory syndrome coronavirus 2 infection: A population-based longitudinal study. J Infect Dis. 2022;226(11):1903–1908. https://doi.org/10.1093/infdis/jiac321
  56. Aldridge RW, Yavlinsky A, Nguyen V, et al. SARS-CoV-2 antibodies and breakthrough infections in the Virus Watch cohort. Nat Commun. 2022;13(1):4869. https://doi.org/10.1038/s41467-022-32265-5
  57. Martin Perez C, Aguilar R, Jimenez A, et al. Correlates of protection and determinants of SARS-CoV-2 breakthrough infections 1 year after third dose vaccination. BMC Med. 2024;22(1):103. https://doi.org/10.1186/s12916-024-03304-3
  58. Sanghavi DK, Bhakta S, Wadei HM, et al. Low antispike antibody levels correlate with poor outcomes in COVID-19 breakthrough hospitalizations. J Intern Med. 2022;292(1):127–135. https://doi.org/10.1111/joim.13471
  59. Al Hasan SM, Saulam J, Mikami F, Kanda K, Yokoi H, Hirao T. COVID-19 outbreak trends in South Africa: A comparison of Omicron (B.1.1.529), Delta (B.1.617.2), and Beta (B.1.351) variants outbreak periods. J Infect Public Health. 2022;15(7):726–733. https://doi.org/10.1016/j.jiph.2022.05.011
  60. Goga A, Bekker LG, Garrett N, et al. Breakthrough SARS-CoV-2 infections during periods of delta and omicron predominance, South Africa. Lancet. 2022;400(10348):269–271. https://doi.org/10.1016/S0140-6736(22)01190-4
  61. Nunes MC, Mbotwe-Sibanda S, Baillie VL, et al. SARS-CoV-2 Omicron symptomatic infections in previously infected or vaccinated South African healthcare workers. Vaccines (Basel). 2022;10(3):459. https://doi.org/10.3390/vaccines10030459
  62. Moreira ED, Jr, Kitchin N, Xu X, et al. Safety and efficacy of a third dose of BNT162b2 Covid-19 vaccine. N Engl J Med. 2022;386(20):1910–1921. https://doi.org/10.1056/NEJMoa2200674
  63. Kitchin D, Richardson SI, van der Mescht MA, et al. Ad26.COV2.S breakthrough infections induce high titers of neutralizing antibodies against Omicron and other SARS-CoV-2 variants of concern. Cell Rep Med. 2022;3:100535. https://doi.org/10.1016/j.xcrm.2022.100535


Crossref Citations

No related citations found.