The global health crisis caused by the novel virus SARS-CoV-2 had an unparalleled impact on the world. In response to this crisis, a significant mobilisation of scientific and financial resources has enabled the rapid development of several vaccines. These include those from Moderna, Pfizer, Johnson & Johnson, Sinopharm, Sputnik V, Sputnik Light, and AstraZeneca
[1]
-
[4]
. Despite their proven effectiveness in reducing the severity of clinical forms, hospitalisations, and mortality
[5]
[6]
, vaccines have sparked controversy, fuelled in particular by misinformation and concerns about the speed of their development
[7]
[8]
. These tensions had a significant impact on vaccination coverage, particularly in African countries
[9]
. Most studies have focused on the assessment of short-term vaccine immunity, typically within six months of vaccination. However, recent studies have emphasised the importance of long-term data collection. In Bangladesh, a longitudinal study demonstrated a 50% decrease in anti-S antibodies one year after vaccination
[10]
. In China, a significant decrease in neutralising antibodies was observed within 180 days of the booster vaccination, particularly against the Delta and Omicron variants
[11]
. In the United States, a study noted persistent anti-S antibodies but increased seronegativity in elderly people or those with comorbidities
[12]
. In sub-Saharan Africa, seroprevalence rates vary widely (20% - 70%), as observed in Kenya and Nigeria, but these studies have often been short-term
[13]
[14]
. The lack of longitudinal data is a significant challenge in the development of suitable vaccination policies, especially for high-risk groups such as healthcare workers.
In the Republic of Congo, data are even more limited. A national study conducted in February 2022 by Ndziessi et al. reported a seroprevalence of 48.2% among adults, mainly among those who had been vaccinated, 24 months after the first confirmed case
[15]
. However, long-term data remain insufficient. The present study aims to evaluate the persistence of anti-SARS-CoV-2 antibodies (anti-S, anti-RBD, and anti-N) up to 49 months after vaccination in healthcare workers in Brazzaville, in an African context. Its purpose is to provide empirical data to better understand the durability of the humoral response and guide booster vaccination strategies in at-risk populations.
2. Patients and Methods2.1. Study Design and Population
A cross-sectional analytical study was conducted. Data were collected from 13 February to 28 June 2025, allowing for the collection of post-vaccination information. The target population included healthcare workers, both those directly involved in patient care (doctors, nurses, nursing assistants) and non-clinical staff (administrative, technical, and logistical personnel). The exact earliest and latest vaccination dates were respectively March 27, 2021, and October 1, 2022. These professionals were selected due to their increased risk of infection through regular contact with patients suffering from various pathologies, including SARS-CoV-2. The study was conducted in the main public referral hospitals in Brazzaville, namely Makélékélé Basic Hospital, Bacongo Basic Hospital, Talangaï Referral Hospital, the Sino-Congolese Friendship Hospital in Mfilou, as well as several peripheral health centres, ensuring a diverse representation of healthcare settings. The inclusion criteria focused on complete vaccination against COVID-19 administered in the Republic of Congo, as well as membership of the healthcare staff of the selected establishments. Those who did not meet the necessary criteria or declined to participate were excluded from the study. Participants were recruited using non-probability convenience sampling.
2.2. Sample Collection and Antibody Assay
Blood samples were collected in dry tubes. Following centrifugation, 1 µL of plasma was diluted in 400 µL of an internally prepared buffer composed of 1% phosphate-buffered saline (PBS), 5% heat-inactivated bovine serum albumin (BSA), and 0.2% Tween 20, all diluted in 1 liter of sterile distilled water. The remaining serum was then aliquoted and stored in a −80˚C freezer for future analysis. The measurement of anti-SARS-CoV-2 antibodies was conducted using a multiplex immunoassay on MagPlex microspheres, employing Luminex xMAPTM technology. This method offers good sensitivity and specificity, allowing for the simultaneous detection of antibodies directed against three distinct antigens of the virus: the spike protein (S), the Receptor Binding Domain (RBD), and the nucleocapsid (N2)
[16]
-
[20]
. MagPlex microspheres were coupled to viral antigens using an amine coupling kit (Bio-Rad Laboratories) in accordance with the manufacturer’s protocol. Readings were performed on a Magpix system (Luminex), with a minimum of 100 events acquired per sample. The results were expressed as Median Fluorescence Intensity (MFI), which reflects the number of specific antibodies bound. Biological analyses were carried out at the National Public Health Laboratory.
2.3. Serological Positivity Threshold for SARS-CoV-2 Antibodies
Samples were considered seropositive for SARS-CoV-2 antibodies if the Median Fluorescence Intensity (MFI) values for both the Spike (S) and Receptor Binding Domain (RBD) exceeded the mean MFI of pre-pandemic (negative control) samples by more than three standard deviations
[17]
. This statistical approach minimizes false positives by ensuring precise discrimination between negative and positive serum samples. An S+ classification was established when both S and RBD were positive
[19]
. We estimated seroprevalence, the proportion of individuals who were S+.
2.4. Study Variables
The variables analyzed included:
2.5. Statistical Analysis
Qualitative data were presented in frequency and proportion tables, while quantitative variables were expressed as means, standard deviations, medians, and quartiles. Comparisons of proportions were performed using Pearson’s Chi2 test or Fisher’s exact test, depending on the conditions. The significance threshold was set at p < 0.05. All analyses were performed using R software, version 4.5.1.
2.6. Ethical Considerations
The protocol of this study was approved by the Health Sciences Research Ethics Committee (CERSSA) under number 137-24/MERSIT/DGRST/CERSSA/-24. The study complied with international ethical principles, including the informed consent of participants, as well as the confidentiality and anonymity of data. Each participant was informed of the objectives, the terms and conditions of participation, and their right to withdraw without any consequence. The data were anonymised and secured to ensure the protection of personal information.
3. Results3.1. Main Characteristics of the Study Population
Of the 141 participants, the majority were female (74.5%) and aged between 18 and 44 years (57.4%). The median age was 42.3 years (Q1 = 32 years, Q3 = 50 years). Furthermore, it is notable that more than half of the respondents were in a relationship (59.6%) and had a higher level of education (66.7%). In terms of professional rank as in the
Table 1
, the largest group was nurses (49.6%), followed by administrative staff, midwives, doctors, and biologists (
Table 1
).
As shown in
Table 2
, 17.7% of participants had high blood pressure and 5.7% had diabetes. The prevalence of viral hepatitis, HIV infection, and a history of stroke was low, each accounting for 0.7%. Finally, 3.5% of healthcare workers reported a history of SARS-CoV-2 infection.
<xref ref-type="bibr" rid="scirp.145945-"></xref>Table 1. Sociodemographic characteristics of the healthcare workers surveyed.
Variables
Frequency
Percentage
Gender
Male
36
25.5
Female
105
74.5
Age group (years)
18 - 44
81
57.4
45 - 65
60
42.6
Marital status
Single
57
40.4
In a relationship
84
59.6
Level of education
Secondary
47
33.3
Higher
94
66.7
Professional rank
Nurse
70
49.6
Administrative staff
26
18.4
Midwife
22
15.6
Doctor
10
7.1
Biologist
6
4.3
Other
7
5.0
<xref ref-type="bibr" rid="scirp.145945-"></xref>Table 2. Main clinical characteristics of the healthcare workers tested.
Variables
Frequency
Percentage
High blood pressure
25
17.7
Diabetes
8
5.7
Hepatitis
1
0.7
HIV
1
0.7
Stroke
1
0.7
History of COVID-19 infection
5
3.5
As shown in
Table 3
, 39.0% of vaccinated healthcare workers had received the Johnson & Johnson vaccine, 27.0% the Sputnik Light vaccine, 25.5% the Sinopharm vaccine, 7.8% the Sputnik V vaccine, and 0.7% the Pfizer vaccine. Regarding booster doses, only 13.5% of participants had received one, while 86.5% had received only the primary vaccination regimen. The time elapsed since the last vaccination was mainly between three and five years for 78.7% of participants, and between two and three years for 21.3%.
<xref ref-type="bibr" rid="scirp.145945-"></xref>Table 3. Vaccine characteristics of health workers.
Variables
Frequency
Percentage
Vaccine name
Johnson & Johnson
55
39.0
Sputnik light
38
27.0
Sinopharm
36
25.5
Sputnik V
11
7.8
Pfizer
1
0.7
Booster doses
Yes
19
13.5
No
122
86.5
Vaccination delay (years)
2 - 3
30
21.3
3 - 5
111
78.7
3.2. Assaying Anti-Spike, Anti-RBD, and Anti-Nucleocapsid Antibodies
As illustrated in
Figure 1
, the mean concentrations of anti-SARS-CoV-2 antibodies in vaccinated healthcare workers are shown. The mean concentration of anti-spike (anti-S) antibodies was 13,353 IU/mL, with a minimum of 14 IU/mL and a maximum of 38,660 IU/mL. The mean concentration of anti-RBD antibodies was 33,224 IU/mL, with a range from 1,026 IU/mL to 44,471 IU/mL. The mean concentration of anti-N2 antibodies was 21,706 IU/mL, with a range of 402 to 42,839 IU/mL.
As illustrated in
Figure 2
, the means of antibody concentrations are indicative of the type of vaccine administered to healthcare professionals. For all vaccines analysed, the highest concentrations of antibodies were observed for anti-RBD, followed by anti-N2.
<xref ref-type="bibr" rid="scirp.145945-"></xref>Figure 2. Antibody concentration in vaccinated healthcare workers according to the vaccine.
3.3. Seroprevalence of Antibodies According to Vaccine Characteristics
Applying the defined positivity thresholds, the overall seroprevalence of antibodies (S+) was 83.0% (117/141).
Table 4
examines the distribution of seropositivity according to vaccine type, booster dose administration, and time elapsed since the last injection.
The seroprevalence rates by vaccine received range from 78.9% to 100%, with no statistically significant difference (p = 0.900). This suggests that the different vaccines are equally effective in inducing a detectable long-term humoral response.
The administration of a booster dose did not significantly influence the seroprevalence of SARS-CoV-2 antibodies (78.9% in those who did not receive a booster vs. 83.6% in those who did; p = 0.700). Similarly, no significant difference was observed based on the post-vaccination interval, whether it was between 2 - 3 years or between 3 - 5 as shown in
Table 4
(p = 1.000), indicating the relative stability of S⁺ antibodies over this period.
<xref ref-type="bibr" rid="scirp.145945-"></xref>Table 4. Seroprevalence of antibodies (S<sup>+</sup>) according to vaccine characteristics.
Variables
Frequency
Positive test (S+)
Seroprevalence (S+)
Total
141
117
83.0
Vaccine name
Johnson & Johnson
55
47
85.5
Sputnik light
38
30
78.9
Sinopharm
36
30
83.3
Sputnik V
11
9
81.8
Pfizer
1
1
100.0
P-value
0.900
Booster doses
Yes
19
15
78.9
No
122
102
83.6
P-value
0.700
Vaccination delay (years)
2 - 3
30
25
83.3
3 - 5
111
92
82.9
P-value
1.000
4. Discussion
The objective of this study was to evaluate the duration of vaccine protection through the persistence of anti-SARS-CoV-2 antibodies for up to 49 months in vaccinated healthcare workers. The seroprevalence of antibodies was estimated between 28 and 49 months after the administration of the last vaccine dose.
The results of this study demonstrate that antibodies remain present in vaccinated healthcare workers up to 49 months after the administration of the last vaccine dose. The highest mean concentration was observed for anti-RBD antibodies, followed by anti-N2 antibodies. The seroprevalence of antibodies against the S protein (S+ profile) was high among vaccinated healthcare workers, reaching 83.0%. This indicates a notable persistence of the immune response even several years after the last dose of vaccine. Our findings align with those reported by Nurjaya et al. in Indonesia, who observed antibody persistence in vaccinated healthcare workers beyond 24 months
[21]
. A study conducted in the Central African Republic (CAR) revealed a high seroprevalence rate among healthcare workers who received their vaccination 12 months after the initial programme . The prolonged persistence of antibodies in vaccinated healthcare workers may be explained by repeated exposure to the virus due to their frequent contact with infected patients, which could promote asymptomatic reinfection. A study conducted by Massala et al. in Congo reported an increase in the incidence of COVID-19 over time among vaccinated people, which was more pronounced among healthcare workers
[23]
. The detection in our study of anti-N2 antibodies in healthcare workers who received non-viral vector vaccines (Johnson & Johnson, Pfizer, Sputnik V, and Sputnik Light), which do not contain the viral nucleoprotein, supports the hypothesis of prior exposure to SARS-CoV-2. This suggests a mixed humoral response, combining vaccine-induced immunity and potentially post-infectious immunity. In fact, the main antigenic target for vaccination is the spike protein. However, vaccines such as Sinopharm are inactivated virus vaccines that trigger the production of anti-N2 antibodies
[1]
[24]
-
[26]
. Furthermore, anti-N2 antibodies are more effective than anti-spike antibodies at detecting infection
[27]
. Several studies have shown that the immune response is enhanced in vaccinated individuals who were infected before or after vaccination
[22]
[28]
-
[34]
. In Austria, the study conducted by Nunhofer et al. showed that antibodies resulting from natural infection in vaccinated individuals declined over time but could persist for more than a year
[35]
.
This distinction between vaccine-induced and infection-induced antibody responses is particularly relevant in contexts where diagnostic capacity is limited. The Republic of Congo, like many other developing countries, has limited capacity to diagnose the disease. This limitation may be responsible for underestimating the true extent of the disease, as reported by Ndziessi et al. in Congo
[15]
. Consequently, many infections remain undocumented, particularly in cases where patients are asymptomatic or only display mild symptoms. Consequently, there are several infections that have not been documented. This absence of documentation can introduce a confounding bias in the analysis of antibody persistence, as an undiagnosed person may have high antibody levels, not only because of vaccination, but also because of a recent infection. The use of anti-N2 antibody data can partially control for this effect, as the presence of these antibodies indicates natural infection, except in the case of inactivated virus vaccines such as Sinopharm.
However, this study has several strengths. Firstly, it assesses the persistence of antibodies over a long period (49 months). Secondly, the differentiated analysis of antibodies (anti-S, anti-RBD, and anti-N2) allows for a better interpretation of the source of immunity. Thirdly, the local context in Congo is poorly documented, which lends this work additional scientific and contextual value. Fourthly, the Luminex assay is a reliable method for measuring anti-SARS-CoV-2 antibodies. This has been demonstrated by studies conducted in Congo
[17]
[18]
and elsewhere
[19]
[20]
[36]
.
While the study also has some methodological limitations related to its cross-sectional design, which measures antibodies at a single point in time, thereby making it impossible to track their evolution over time; the small sample sizes for each vaccine type limit the statistical power of the analysis to detect potential differences in seroprevalence between vaccinated groups; the vaccine-induced immunity and infection-induced immunity were not differentiated due to the use of tests that cannot distinguish between antibodies from vaccination and those from natural infection; there is selection bias because the healthcare professionals included are not representative of all vaccinated individuals in Brazzaville; and finally, there is no assessment of the neutralising capacity of the antibodies. A longitudinal follow-up of the same workers would better capture antibody kinetics.
These findings have several implications for public health. It is important to periodically measure post-vaccination immunity among healthcare workers to adapt vaccination strategies, integrate serological surveillance into the surveillance system for detecting asymptomatic infections among healthcare workers, and raise awareness among healthcare workers about the need to maintain preventive measures despite vaccination.
5. Conclusion
This study highlights a high persistence of anti-SARS-CoV-2 antibodies up to 49 months after vaccination among healthcare workers in Brazzaville. None of the factors studied, vaccine type, booster dose, or time since vaccination, significantly influenced this seroprevalence. These findings suggest a potentially long-lasting post-vaccination immunity and underscore the importance of longitudinal monitoring to better understand the durability of immune protection over time.
Authors’ Contributions
J. M. P. drafted the protocol, collected and analysed the data, and wrote the draft of the manuscript. L. H. L. drafted the biological methodology and analysed the samples. G. N. and L. H. L. revised the manuscript. G. N., G. E. E. M., F. K. K., and R. F. N. supervised and validated the various stages. J. R. N. A., A. C. N., and J. A. N. proofread the manuscript. All authors contributed to this study.
Acknowledgements
The authors would like to thank the National Public Health Laboratory for providing the equipment and personnel necessary for antibody testing. They would also like to thank the investigators who contributed to the collection of data and samples, as well as the healthcare professionals who agreed to participate in the study.
References
Organisation mondiale de la Santé (2023) COVID-19 Vaccine Tracker and Landscape. >https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines
Feraoun, Y., Maisonnasse, P., Le Grand, R. and Beignon, A. (2021) COVID-19, des vaccins à la vitesse de l’éclair. Médecine/Sciences, 37, 759-772. >https://doi.org/10.1051/medsci/2021094
U.S. Food and Drug Administration (2021) Coronavirus Disease 2019 (COVID-19). >https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/covid-19-vaccines
European Medicines Agency (2023) COVID-19 Medicines. >https://www.ema.europa.eu/en/human-regulatory-overview/public-health-threats/coronavirus-disease-covid-19/covid-19-medicines
Polack, F.P., Thomas, S.J., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., et al. (2020) Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. New England Journal of Medicine, 383, 2603-2615. >https://doi.org/10.1056/nejmoa2034577
Baden, L.R., El Sahly, H.M., Essink, B., Kotloff, K., Frey, S., Novak, R., et al. (2021) Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine. New England Journal of Medicine, 384, 403-416. >https://doi.org/10.1056/nejmoa2035389
Roozenbeek, J., Schneider, C.R., Dryhurst, S., Kerr, J., Freeman, A.L.J., Recchia, G., et al. (2020) Susceptibility to Misinformation about COVID-19 around the World. Royal Society Open Science, 7, Article ID: 201199. >https://doi.org/10.1098/rsos.201199
Sallam, M. (2021) COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates. Vaccines, 9, Article 160. >https://doi.org/10.3390/vaccines9020160
Cooper, S., van Rooyen, H. and Wiysonge, C.S. (2021) COVID-19 Vaccine Hesitancy in South Africa: How Can We Maximize Uptake of COVID-19 Vaccines? Expert Review of Vaccines, 20, 921-933. >https://doi.org/10.1080/14760584.2021.1949291
Haq, M.A., Roy, A.K., Ahmed, R., Kuddusi, R.U., Sinha, M., Hossain, M.S., et al. (2024) Antibody Longevity and Waning Following COVID-19 Vaccination in a 1-Year Longitudinal Cohort in Bangladesh. Scientific Reports, 14, Article No. 11467. >https://doi.org/10.1038/s41598-024-61922-6
Chen, Z., Xie, F., Zhang, H., Li, D., Zhang, S., Zhang, M., et al. (2025) Waning Neutralizing Antibodies through 180 Days after Homologous and Heterologous Boosters of Inactivated COVID-19 Vaccine. Frontiers in Public Health, 13, Article 1478627. >https://doi.org/10.3389/fpubh.2025.1478627
Berry, A.A., Tjaden, A.H., Renteria, J., Friedman-Klabanoff, D., Hinkelman, A.N., Gibbs, M.A., et al. (2023) Persistence of Antibody Responses to COVID-19 Vaccines among Participants in the COVID-19 Community Research Partnership. Vaccine: X, 15, Article ID: 100371. >https://doi.org/10.1016/j.jvacx.2023.100371
Uyoga, S., Adetifa, I.M.O., Karanja, H.K., Nyagwange, J., Tuju, J., Wanjiku, P., et al. (2021) Seroprevalence of Anti-SARS-CoV-2 IgG Antibodies in Kenyan Blood Donors. Science, 371, 79-82. >https://doi.org/10.1126/science.abe1916
Olayanju, O., Bamidele, O., Edem, F., Eseile, B., Amoo, A., Nwaokenye, J., et al. (2021) SARS-CoV-2 Seropositivity in Asymptomatic Frontline Health Workers in Ibadan, Nigeria. The American Journal of Tropical Medicine and Hygiene, 104, 91-94. >https://doi.org/10.4269/ajtmh.20-1235
Ndziessi, G., Niama, R.F., Aloumba, A.G., Peya, J.M., Ngatse, J.A., Ngoyomi, R.A., et al. (2023) Seroprevalence of SARS-CoV-2 Antibodies in Republic of Congo, February 2022. Epidemiology and Infection, 151, e162. >https://doi.org/10.1017/s0950268823001425
Luminex Corporation (2023) Luminex xMAP® SARS-CoV-2 Antibody Testing. >https://www.luminexcorp.com/xmap-sars-cov-2-antibody-testing/
Bonguili, N.C.B., Fritz, M., Lenguiya, H., Mayengue, P.I., Koukouikila-Koussounda, F., Dossou-Yovo, L.R., et al. (2022) Early Circulation of SARS-CoV-2, Congo, 2020. Emerging Infectious Diseases, 28, 878-880. >https://doi.org/10.3201/eid2804.212476
Lenguiya, L.H., Fritz, M., De Fonclare, D.d.R., Corbet, S., Becquart, P., Mbou, C., et al. (2023) Whole-Genome Characterization of SARS-CoV-2 Reveals Simultaneous Circulation of Three Variants and a Putative Recombination (20B/20H) in Pets, Brazzaville, Republic of the Congo. Viruses, 15, Article 933. >https://doi.org/10.3390/v15040933
Chun, H.M., Osawe, S., Adams-Dabban, S., Favaloro, J., Iriemenam, N.C., Dirlikov, E., et al. (2025) SARS-CoV-2 Serologic Surveillance among People Living with HIV in Nigeria, April 2022 to January 2023. International Journal of Infectious Diseases, 151, Article ID: 107309. >https://doi.org/10.1016/j.ijid.2024.107309
Gwyn, S., Abubakar, A., Akinmulero, O., Bergeron, E., Blessing, U.N., Chaitram, J., et al. (2022) Performance of SARS-CoV-2 Antigens in a Multiplex Bead Assay for Integrated Serological Surveillance of Neglected Tropical and Other Diseases. The American Journal of Tropical Medicine and Hygiene, 107, 260-267. >https://doi.org/10.4269/ajtmh.22-0078
Nurjaya, I., Arief, E., Tabri, N.A., Djaharuddin, I., Natsir, B., Nurisyah, S., et al. (2024) Durability of SARS-CoV-2 IgG Response: A Cross-Sectional Study in Vaccinated Healthcare Workers Using Dried Blood Spot and Multi-Antigen Profiling. Exploration of Immunology, 4, 679-690. >https://doi.org/10.37349/ei.2024.00166
Manirakiza, A., Malaka, C., Mossoro-Kpinde, H.D., Yambiyo, B.M., Mossoro-Kpinde, C.D., Fandema, E., et al. (2023) Séroprévalence des anticorps Anti-SARS-CoV-2 avant et après la mise en place de la vaccination anti-COVID-19 chez le personnel hospitalier à Bangui, République Centrafricaine. Revue d’Épidémiologie et de Santé Publique, 71, Article ID: 101868. >https://doi.org/10.1016/j.respe.2023.101868
Massala Peya, J., Ndziessi, G., Matangelo, G.E.E., Niama, R.F., Babintu, L.M.B., Niama, A.C., et al. (2025) Incidence of COVID-19 in Vaccinated Persons in Congo. Health Sciences and Diseases, 26, 87-92.
Bonny, V., Maillard, A., Mousseaux, C., Plaçais, L. and Richier, Q. (2020) COVID-19: Physiopathologie d’une maladie à plusieurs visages. La Revue de Médecine Interne, 41, 375-389. >https://doi.org/10.1016/j.revmed.2020.05.003
Logunov, D.Y., Dolzhikova, I.V., Zubkova, O.V., Tukhvatulin, A.I., Shcheblyakov, D.V., Dzharullaeva, A.S., et al. (2020) Safety and Immunogenicity of an rAd26 and rAd5 Vector-Based Heterologous Prime-Boost COVID-19 Vaccine in Two Formulations: Two Open, Non-Randomised Phase 1/2 Studies from Russia. The Lancet, 396, 887-897. >https://doi.org/10.1016/s0140-6736(20)31866-3
Tukhvatulin, A.I., Dolzhikova, I.V., Shcheblyakov, D.V., Zubkova, O.V., Dzharullaeva, A.S., Kovyrshina, A.V., et al. (2021) An Open, Non-Randomised, Phase 1/2 Trial on the Safety, Tolerability, and Immunogenicity of Single-Dose Vaccine “Sputnik Light” for Prevention of Coronavirus Infection in Healthy Adults. The Lancet Regional Health—Europe, 11, Article ID: 100241. >https://doi.org/10.1016/j.lanepe.2021.100241
Burbelo, P.D., Riedo, F.X., Morishima, C., Rawlings, S., Smith, D., Das, S., et al. (2020) Sensitivity in Detection of Antibodies to Nucleocapsid and Spike Proteins of Severe Acute Respiratory Syndrome Coronavirus 2 in Patients with Coronavirus Disease 2019. The Journal of Infectious Diseases, 222, 206-213. >https://doi.org/10.1093/infdis/jiaa273
Kontopoulou, K., Nakas, C., Ainatzoglou, A., Goudi, G., Katsioulis, C. and Papazisis, G. (2021) Evolution of Antibody Titers up to 6 Months Post-Immunization with the BNT162b2 Pfizer/BioNTech Vaccine in Greece. SSRN Electronic Journal, 1-12. >https://doi.org/10.2139/ssrn.3922311
Lorent, D., Nowak, R., Figlerowicz, M., Handschuh, L. and Zmora, P. (2024) Anti-SARS-CoV-2 Antibodies Level and COVID-19 Vaccine Boosters among Healthcare Workers with the Highest SARS-CoV-2 Infection Risk—Follow Up Study. Vaccines, 12, Article 475. >https://doi.org/10.3390/vaccines12050475
Mvula, M., Mtonga, F., Mandolo, J., Jowati, C., Kalirani, A., Chigamba, P., et al. (2024) Longevity of Hybrid Immunity against SARS-CoV-2 in Adults Vaccinated with an Adenovirus-Based COVID-19 Vaccine. BMC Infectious Diseases, 24, Article No. 959. >https://doi.org/10.1186/s12879-024-09891-z
Remy, L., Tomomori-Sato, C., Conkright-Fincham, J., Wiedemann, L.M., Conaway, J.W. and Unruh, J.R. (2021) Comparison of Antibody Levels in Response to SARS-CoV-2 Infection and Vaccination Type in a Midwestern Cohort. medRxiv. >https://doi.org/10.1101/2021.08.16.21262036
Walmsley, S.L., Szadkowski, L., Wouters, B., Clarke, R., Colwill, K., Rochon, P., et al. (2023) COVID-19 Vaccine Antibody Responses in Community-Dwelling Adults to 48 Weeks Post Primary Vaccine Series. iScience, 26, Article ID: 106506. >https://doi.org/10.1016/j.isci.2023.106506
Wolszczak-Biedrzycka, B., Bieńkowska, A., Zaborowska, J.E., Smolińska-Fijołek, E., Biedrzycki, G. and Dorf, J. (2022) Anti-SARS-CoV-2s Antibody Levels in Healthcare Workers 10 Months after the Administration of Two BNT162b2 Vaccine Doses in View of Demographic Characteristic and Previous COVID-19 Infection. Vaccines, 10, Article 741. >https://doi.org/10.3390/vaccines10050741
Salvagno, G., Henry, B., Pighi, L., de, N. and Lippi, G. (2022) Total Anti-SARS-CoV-2 Antibodies Measured 6 Months after Pfizer-Biontech COVID-19 Vaccination in Healthcare Workers. Journal of Medical Biochemistry, 41, 199-203. >https://doi.org/10.5937/jomb0-33999
Nunhofer, V., Weidner, L., Hoeggerl, A.D., Zimmermann, G., Badstuber, N., Grabmer, C., et al. (2022) Persistence of Naturally Acquired and Functional SARS-CoV-2 Antibodies in Blood Donors One Year after Infection. Viruses, 14, Article 637. >https://doi.org/10.3390/v14030637
Cameron, A., Porterfield, C.A., Byron, L.D., Wang, J., Pearson, Z., Bohrhunter, J.L., et al. (2021) A Multiplex Microsphere IgG Assay for SARS-CoV-2 Using ACE2-Mediated Inhibition as a Surrogate for Neutralization. Journal of Clinical Microbiology, 59, e02489-20. >https://doi.org/10.1128/jcm.02489-20