Serological Correlates of Protection Induced by COVID-19 Vaccination in the Working Age Population: A Systematic Review and Meta-Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Analysis
2.2. Registration and Protocol
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Search strategy
Search Strategy | Details |
Search query | (COVID-19 OR SARS-CoV-2) AND (vaccin* OR immunizat* OR vaccinat*) AND (“correlate* of protection” OR CoP OR “surrogate* of protection” OR SoP OR “protective correlate*” OR “protective surrogate*” OR “protective titer” OR “protective titre” OR “vaccine protection” OR “immune protection”) |
Databases | PubMed/MEDLINE, Scopus, Web of Science |
Time filter | 1 January 2020 to 1 December 2023 |
Language filter | English and Italian |
Inclusion criteria | P (population): working age population (15–64 years) who underwent COVID-19 vaccination I (intervention): anti-Spike, -S1, -S2, -RBD antibody serologic testing C (comparator): different types of vaccination; different doses of vaccination; immunocompetent vs. immunocompromised O (outcome): mean of anti-Spike and anti-RBD Antibody Titer corresponding to a protective effect, definition of a Correlate of Protection induced by COVID-19 vaccination Study type and design: primary research, studies reporting cross-sectional or longitudinal data |
Exclusion criteria | Studies not matching the defined PICO criteria; studies on pediatric population; studies on geriatric population; animal studies; reviews; editorials; comments; case-reports; case series |
References
- World Health Organization. Statement on the Ninth Meeting of the International Health Regulations (2005) Emergency Committee Regarding the Coronavirus Disease (COVID-19) Pandemic. 2021. Available online: https://www.who.int/news/item/26-10-2021-statement-on-the-ninth-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-coronavirus-disease-(covid-19)-pandemic (accessed on 28 March 2024).
- World Health Organization. Statement on the Fifteenth Meeting of the IHR (2005) Emergency Committee on the COVID-19 Pandemic. Available online: https://www.who.int/news/item/05-05-2023-statement-on-the-fifteenth-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-coronavirus-disease-(covid-19)-pandemic (accessed on 28 March 2024).
- Teijaro, J.R.; Farber, D.L. COVID-19 vaccines: Modes of immune activation and future challenges. Nat. Rev. Immunol. 2021, 21, 195–197. [Google Scholar] [CrossRef]
- Plotkin, S.A.; Orenstein, W.; Offit, P.A.; Edwards, K.M. Plotkin’s Vaccines, 7th ed.; Elsevier: Amsterdam, The Netherlands, 2018. [Google Scholar]
- Plotkin, S.A. Vaccines: Correlates of vaccine-induced immunity. Clin. Infect. Dis. 2008, 47, 401–409. [Google Scholar] [CrossRef] [PubMed]
- Plotkin, S.A. Correlates of protection induced by vaccination. Clin. Vaccine Immunol. 2010, 17, 1055–1065. [Google Scholar] [CrossRef] [PubMed]
- da Silva Antunes, R.; Grifoni, A.; Frazier, A.; Weiskopf, D.; Sette, A. An update on studies characterizing adaptive immune responses in SARS-CoV-2 infection and COVID-19 vaccination. Int. Immunol. 2023, 35, 353–359. [Google Scholar] [CrossRef]
- Marking, U.; Havervall, S.; Norin, N.G.; Bladh, O.; Christ, W.; Gordon, M.; Ng, H.; Blom, K.; Phillipson, M.; Mangsbo, S.; et al. Correlates of protection and viral load trajectories in omicron breakthrough infections in triple vaccinated healthcare workers. Nat. Commun. 2023, 14, 1577. [Google Scholar] [CrossRef] [PubMed]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, 71. [Google Scholar] [CrossRef]
- Organisation for Economic Co-Operation and Development. Working Age Population (Indicator). Available online: https://data.oecd.org/pop/working-age-population.htm (accessed on 28 March 2024).
- Shi, J.; Luo, D.; Wan, X.; Liu, Y.; Liu, J.; Bian, Z.; Tong, T. Detecting the skewness of data from the five-number summary and its application in meta-analysis. Stat. Methods Med. Res. 2023, 32, 1338–1360. [Google Scholar] [CrossRef]
- Shi, J.; Luo, D.; Weng, H.; Zeng, X.T.; Lin, L.; Chu, H.; Tong, T. Optimally estimating the sample standard deviation from the five-number summary. Res. Synth. Methods 2020, 11, 641–654. [Google Scholar] [CrossRef]
- Luo, D.; Wan, X.; Liu, J.; Tong, T. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat. Methods Med. Res. 2018, 27, 1785–1805. [Google Scholar] [CrossRef]
- Wan, X.; Wang, W.; Liu, J.; Tong, T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med. Res. Methodol. 2014, 14, 135. [Google Scholar] [CrossRef]
- Deeks, J.J.; Higgins, J.P.T.; Altman, D.G. Chapter 10: Analysing data and undertaking meta-analyses. In Cochrane Handbook for Systematic Reviews of Interventions Version 6.4 (Updated August 2023); Higgins, J.P.T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M.J., Welch, V.A., Eds.; Cochrane: London, UK, 2023; Available online: www.training.cochrane.org/handbook (accessed on 28 March 2024).
- Duval, S.; Tweedie, R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000, 56, 455–463. [Google Scholar] [CrossRef] [PubMed]
- Egger, M.; Davey Smith, G.; Schneider, M.; Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997, 315, 629–634. [Google Scholar] [CrossRef] [PubMed]
- Atef, S.; Al Hosani, F.; AbdelWareth, L.; Al-Rifai, R.H.; Abuyadek, R.; Jabari, A.; Ali, R.; Altrabulsi, B.; Dunachie, S.; Alatoom, A.; et al. Susceptibility to reinfection with SARS-CoV-2 virus relative to existing antibody concentrations and T cell response. Int. J. Infect. Dis. 2023, 131, 100–110. [Google Scholar] [CrossRef] [PubMed]
- Dimeglio, C.; Migueres, M.; Bouzid, N.; Chapuy-Regaud, S.; Gernigon, C.; Da-Silva, I.; Porcheron, M.; Martin-Blondel, G.; Herin, F.; Izopet, J. Antibody Titers and Protection against Omicron (BA.1 and BA.2) SARS-CoV-2 Infection. Vaccines 2022, 10, 1548. [Google Scholar] [CrossRef] [PubMed]
- Fernández-Rivas, G.; Barallat, J.; Quirant-Sánchez, B.; González, V.; Doladé, M.; Martinez-Caceres, E.; Piña, M.; Matllo, J.; Blanco, I.; Cardona, P.J. Follow up of the Humoral Response in Healthcare Workers after the Administration of Two Dose of the Anti-SARS-CoV-2 Vaccines-Effectiveness in Delta Variant Breakthrough Infections. Viruses 2022, 14, 1385. [Google Scholar] [CrossRef] [PubMed]
- Fong, Y.; McDermott, A.B.; Benkeser, D.; Roels, S.; Stieh, D.J.; Vandebosch, A.; Le Gars, M.; Van Roey, G.A.; Houchens, C.R.; Martins, K.; et al. Immune correlates analysis of the ENSEMBLE single Ad26.COV2.S dose vaccine efficacy clinical trial. Nat. Microbiol. 2022, 7, 1996–2010. [Google Scholar] [CrossRef]
- Fong, Y.; Huang, Y.; Benkeser, D.; Carpp, L.N.; Áñez, G.; Woo, W.; McGarry, A.; Dunkle, L.M.; Cho, I.; Houchens, C.R.; et al. Immune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial. Nat. Commun. 2023, 14, 331. [Google Scholar] [CrossRef] [PubMed]
- Gilbert, P.B.; Montefiori, D.C.; McDermott, A.B.; Fong, Y.; Benkeser, D.; Deng, W.; Zhou, H.; Houchens, C.R.; Martins, K.; Jayashankar, L.; et al. Immune correlates analysis of the mRNA-1273 COVID-19 vaccine efficacy clinical trial. Science 2022, 375, 43–50. [Google Scholar] [CrossRef]
- Gilboa, M.; Gonen, T.; Barda, N.; Cohn, S.; Indenbaum, V.; Weiss-Ottolenghi, Y.; Amit, S.; Asraf, K.; Joseph, G.; Levin, T.; et al. Factors Associated with Protection From SARS-CoV-2 Omicron Variant Infection and Disease Among Vaccinated Health Care Workers in Israel. JAMA Netw. Open 2023, 6, e2314757. [Google Scholar] [CrossRef]
- Goldblatt, D.; Fiore-Gartland, A.; Johnson, M.; Hunt, A.; Bengt, C.; Zavadska, D.; Snipe, H.D.; Brown, J.S.; Workman, L.; Zar, H.J.; et al. Towards a population-based threshold of protection for COVID-19 vaccines. Vaccine 2022, 40, 306–315. [Google Scholar] [CrossRef]
- Hertz, T.; Levy, S.; Ostrovsky, D.; Oppenheimer, H.; Zismanov, S.; Kuzmina, A.; Friedman, L.M.; Trifkovic, S.; Brice, D.; Chun-Yang, L.; et al. Correlates of protection for booster doses of the SARS-CoV-2 vaccine BNT162b2. Nat. Commun. 2023, 14, 4575. [Google Scholar] [CrossRef]
- Macrae, K.; Gong, C.Y.; Sheth, P.; Martinez-Cajas, J.; Gong, Y. Quantitative Analysis of SARS-CoV-2 Serological Responses Post Three Doses of Immunization and Prior to Breakthrough COVID-19 Infections. Vaccines 2022, 10, 1590. [Google Scholar] [CrossRef]
- Möhlendick, B.; Čiučiulkaitė, I.; Elsner, C.; Anastasiou, O.E.; Trilling, M.; Wagner, B.; Zwanziger, D.; Jöckel, K.H.; Dittmer, U.; Siffert, W. Individuals with Weaker Antibody Responses After Booster Immunization Are Prone to Omicron Breakthrough Infections. Front. Immunol. 2022, 13, 907343. [Google Scholar] [CrossRef] [PubMed]
- Perez-Saez, J.; Zaballa, M.E.; Lamour, J.; Yerly, S.; Dubos, R.; Courvoisier, D.S.; Villers, J.; Balavoine, J.F.; Pittet, D.; Kherad, O.; et al. Long term anti-SARS-CoV-2 antibody kinetics and correlate of protection against Omicron BA.1/BA.2 infection. Nat. Commun. 2023, 14, 3032. [Google Scholar] [CrossRef]
- Regenhardt, E.; Kirsten, H.; Weiss, M.; Lübbert, C.; Stehr, S.N.; Remane, Y.; Pietsch, C.; Hönemann, M.; von Braun, A. SARS-CoV-2 Vaccine Breakthrough Infections of Omicron and Delta Variants in Healthcare Workers. Vaccines 2023, 11, 958. [Google Scholar] [CrossRef] [PubMed]
- Regev-Yochay, G.; Lustig, Y.; Joseph, G.; Gilboa, M.; Barda, N.; Gens, I.; Indenbaum, V.; Halpern, O.; Katz-Likvornik, S.; Levin, T.; 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, e309–e318. [Google Scholar] [CrossRef] [PubMed]
- Roy, A.; Saade, C.; Josset, L.; Clément, B.; Morfin, F.; Destras, G.; Valette, M.; Icard, V.; Billaud, G.; Oblette, A.; et al. Determinants of protection against SARS-CoV-2 Omicron BA.1 and Delta infections in fully vaccinated outpatients. J. Med. Virol. 2023, 95, e28984. [Google Scholar] [CrossRef] [PubMed]
- Sendi, P.; Widmer, N.; Branca, M.; Thierstein, M.; Büchi, A.E.; Güntensperger, D.; Blum, M.R.; Baldan, R.; Tinguely, C.; Heg, D.; et al. Do quantitative levels of antispike-IgG antibodies aid in predicting protection from SARS-CoV-2 infection? Results from a longitudinal study in a police cohort. J. Med. Virol. 2023, 95, e28904. [Google Scholar] [CrossRef]
- Wei, J.; Pouwels, K.B.; Stoesser, N.; Matthews, P.C.; Diamond, I.; Studley, R.; Rourke, E.; Cook, D.; Bell, J.I.; Newton, J.N.; et al. Antibody responses and correlates of protection in the general population after two doses of the ChAdOx1 or BNT162b2 vaccines. Nat. Med. 2022, 28, 1072–1082. [Google Scholar] [CrossRef]
- Perry, J.; Osman, S.; Wright, J.; Richard-Greenblatt, M.; Buchan, S.A.; Sadarangani, M.; Bolotin, S. Does a humoral correlate of protection exist for SARS-CoV-2? A systematic review. PLoS ONE 2022, 17, e0266852. [Google Scholar] [CrossRef]
- Havervall, S.; Marking, U.; Svensson, J.; Greilert-Norin, N.; Bacchus, P.; Nilsson, P.; Hober, S.; Gordon, M.; Blom, K.; Klingström, J.; et al. Anti-Spike Mucosal IgA Protection against SARS-CoV-2 Omicron Infection. N. Engl. J. Med. 2022, 387, 1333–1336. [Google Scholar] [CrossRef] [PubMed]
- Gilbert, P.B.; Donis, R.O.; Koup, R.A.; Fong, Y.; Plotkin, S.A.; Follmann, D. A Covid-19 Milestone Attained—A Correlate of Protection for Vaccines. N. Engl. J. Med. 2022, 387, 2203–2206. [Google Scholar] [CrossRef]
- Wang, L.; Nicols, A.; Turtle, L.; Richter, A.; Duncan, C.J.; Dunachie, S.J.; Klenerman, P.; Payne, R.P. T cell immune memory after covid-19 and vaccination. BMJ Med. 2023, 2, e000468. [Google Scholar] [CrossRef] [PubMed]
- Spiteri, G.; D’Agostini, M.; Abedini, M.; Ditano, G.; Collatuzzo, G.; Boffetta, P.; Vimercati, L.; Sansone, E.; De Palma, G.; Modenese, A.; et al. Protective role of SARS-CoV-2 anti-S IgG against breakthrough infections among European healthcare workers during pre and post-Omicron surge-ORCHESTRA project. Infection, 2024; Epub ahead of print. [Google Scholar] [CrossRef]
- Lang, P.O.; Govind, S.; Bokum, A.T.; Kenny, N.; Matas, E.; Pitts, D.; Aspinall, R. Immune senescence and vaccination in the elderly. Curr. Top. Med. Chem. 2013, 13, 2541–2550. [Google Scholar] [CrossRef] [PubMed]
- Dietz, L.L.; Juhl, A.K.; Søgaard, O.S.; Reekie, J.; Nielsen, H.; Johansen, I.S.; Benfield, T.; Wiese, L.; Stærke, N.B.; Jensen, T.Ø.; et al. Impact of age and comorbidities on SARS-CoV-2 vaccine-induced T cell immunity. Commun. Med. 2023, 3, 58. [Google Scholar] [CrossRef]
- Bobrovitz, N.; Ware, H.; Ma, X.; Li, Z.; Hosseini, R.; Cao, C.; Selemon, A.; Whelan, M.; Premji, Z.; Issa, H.; et al. Protective effectiveness of previous SARS-CoV-2 infection and hybrid immunity against the omicron variant and severe disease: A systematic review and meta-regression. Lancet Infect. Dis. 2023, 23, 556–567. [Google Scholar] [CrossRef] [PubMed]
- Wu, N.; Joyal-Desmarais, K.; Ribeiro, P.A.B.; Vieira, A.M.; Stojanovic, J.; Sanuade, C.; Yip, D.; Bacon, S.L. Long-term effectiveness of COVID-19 vaccines against infections, hospitalisations, and mortality in adults: Findings from a rapid living systematic evidence synthesis and meta-analysis up to December, 2022. Lancet Respir. Med. 2023, 11, 439–452. [Google Scholar] [CrossRef] [PubMed]
- Hu, J.; Peng, P.; Cao, X.; Wu, K.; Chen, J.; Wang, K.; Tang, N.; Huang, A.L. Increased immune escape of the new SARS-CoV-2 variant of concern Omicron. Cell Mol. Immunol. 2022, 19, 293–295. [Google Scholar] [CrossRef] [PubMed]
- Menegale, F.; Manica, M.; Zardini, A.; Guzzetta, G.; Marziano, V.; d’Andrea, V.; Trentini, F.; Ajelli, M.; Poletti, P.; Merler, S. Evaluation of Waning of SARS-CoV-2 Vaccine-Induced Immunity: A Systematic Review and Meta-analysis. JAMA Netw. Open 2023, 6, e2310650. [Google Scholar] [CrossRef]
- Bedekar, P.; Kearsley, A.J.; Patrone, P.N. Prevalence estimation and optimal classification methods to account for time dependence in antibody levels. J. Theor. Biol. 2023, 559, 111375. [Google Scholar] [CrossRef]
- Wang, L.; Patrone, P.N.; Kearsley, A.J.; Izac, J.R.; Gaigalas, A.K.; Prostko, J.C.; Kwon, H.J.; Tang, W.; Kosikova, M.; Xie, H.; et al. Monoclonal Antibodies as SARS-CoV-2 Serology Standards: Experimental Validation and Broader Implications for Correlates of Protection. Int. J. Mol. Sci. 2023, 24, 15705. [Google Scholar] [CrossRef] [PubMed]
First Name | Year | Country | Study Design | Sample Size (n) | Mean Age (y) | Female (%) | Type of Vaccine | Proportion Primary Course of Vaccination (%) | Proportion First Booster Dose (%) | Proportion Second Booster Dose (%) | Time Since Last Dose (d) | Proportion of Prior Infections (%) | Mean Protective Antibody Titer | Prevalent VOC | Type of Serologic Testing |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Atef S. et al. [18] | 2023 | UAE | Longitudinal | 940 | 35.5 | 0.0 | Inactivated-mRNA | 97.7 | 75.5 | 2.1 | 89.2 | 12.2 | Anti-RBD 941.7 (652.4) | Delta | CMIA |
Dimeglio C. et al. [19] | 2022 | France | Longitudinal | 259 | 40.1 | 74.5 | mRNA-VV | 100.0 | 36.7 | 0.0 | 208.4 | 64.9 | <6000 BAU/mL provided no protection against Omicron BA.1 infection; 6000–20,000 BAU/mL provided 55.6% protection;20,000 or more provided 87.7% protection | Omicron | ECLIA |
Fernández-Rivas G. et al. [20] | 2022 | Spain | Cross-Sectional | 5000 | 35–54 (44.9) | 80.4 | mRNA | 87.4 | NA | NA | 180.0 | 16.6 | Anti-Spike 1268.8 (1197.6) | Delta | ECLIA |
Fong Y. et al. [21] | 2022 | Argentina, Brazil, Chile, Colombia, Mexico, Peru, South Africa and USA | Case-cohort | 826 | 49.4 | 45.2 | VV | 100 | 0.0 | 0.0 | 29.0 | 0.0 | Breakthrough case 27.54 BAU/mL vs. non-case 32.49 BAU/mL | pre-Delta | ECLIA |
Fong Y. et al. [22] | 2023 | USA | Case-cohort | 639 | 55 | 46.2 | Recombinant protein subunit | 100.0 | NA | NA | 35.0 | NA | Anti-RBD 2123.0 (2998.6)- Anti-Spike 1552.0 (1973.3) | pre-Delta | ECLIA |
Gilbert P.B. et al. [23] | 2022 | USA | Case-cohort | 1147 | 54.4 | 47.0 | mRNA | 100.0 | 0.0 | 0.0 | 30.0 | 0.0 | spike IgG of 33, 300, and 4000 BAU/mL, vaccine efficacy was 85% (31 to 92%), 90% (77 to 94%), and 94% (91 to 96%) | pre-Delta | ECLIA |
Gilboa M. et al. [24] | 2023 | Israel | Longitudinal | 2310 | 50.0 | 76.6 | mRNA | 100.0 | 100.0 | 0.0 | NA | 0.0 | IgG > 2000 BAU were less likely to be infected compared to IgG ≤ 500 BAU (OR, 0.52; 95% CI, 0.39–0.67) | Omicron | CMIA |
Goldblatt D. et al. [25] | 2021 | UK–Latvia–South Africa | Cross-Sectional | 122 | 46.5 | 60.7 | mRNA-VV | 100.0 | NA | NA | 15.4 | 0.0 | Overall protective threshold was estimated to be 154 BAU/mL (95% CI 42–559) | pre-Delta. Delta | ECLIA |
Hertz T. et al. [26] | 2023 | Israel | Longitudinal | 607 | 47.3 | 72.0 | mRNA | 100.0 | 100.0 | 39.9 | 147.4 | 0.0 | IgG responses against the RBD were not significantly associated with infection status (four doses: p = 0.083; three doses p = 0.281) | Omicron | ELISA |
Macrae K. et al. [27] | 2022 | Canada | Longitudinal | 140 | 54.6 | 67.1 | mRNA-VV | 90.0 | 56.4 | NA | 112.3 | NA | Average antibody concentration prior to infection was 1911.3 BAU/mL | Delta–Omicron | ELISA |
Marking U. et al. [8] | 2023 | Sweden | Longitudinal | 347 | 52.6 | 89.0 | mRNA-VV | 100.0 | 100.0 | 0.0 | 34.4 | 42.0 | Adjusted relative risk of infection for participants above vs. below 75th percentile of serum-IgG was 0.35 (95% CI 0.14–0.71) | Omicron | ECLIA |
Möhlendick B. et al. [28] | 2022 | Germany | Longitudinal | 1391 | 40.7 | 77.3 | mRNA-VV | 100.0 | 100.0 | 0.0 | NA | NA | After 1 month following booster dose administration subjects with 3477.0 BAU/mL became infected, while with 4733.0 BAU/mL did not | Delta–Omicron | CMIA |
Perez-Saez J. et al. [29] | 2023 | Switzerland | Longitudinal | 1083 | 18–64 (91.0) | 54.5 | mRNA | NA | NA | NA | NA | 31.4 | Overall three-fold reduction in the hazard of reporting a positive test for antibody levels above 800 IU/mL | Omicron | ECLIA |
Regenhardt E. et al. [30] | 2023 | Germany | Longitudinal | 81 | 34.9 | 69.1 | mRNA-VV | 100.0 | 40.7 | 0.0 | NA | NA | Median anti-RBD-IgG before Omicron breakthrough infection = 1235, 95% CI [771–2404] vs. Delta breakthrough infection = 138, 95% CI [106–220] | Delta–Omicron | CMIA |
Regev-Yochay, G. et al. [31] | 2023 | Israel | Longitudinal | 1461 | 41.7 | 54.1 | mRNA | 96.4 | 0.0 | 0.0 | 177.8 | 22.8 | Uninfected 168.2 BAU per mL [95% CI 158.3–178.7] vs. infected 130.5 BAU/mL [118.3–143.8] | Delta | CMIA |
Roy A. et al. [32] | 2023 | France | Longitudinal | 636 | 37.0 | 74.2 | mRNA-VV | 100.0 | 38.1 | 0.7 | 120.2 | 17.1 | 1040.8 (1188.3) | Delta–Omicron | CMIA |
Sendi P. et al. [33] | 2023 | Switzerland | Longitudinal | 949 | 41.0 | 27.0 | mRNA | 89.0 | 69.5 | 0.0 | NA | 54.9 | association of anti-S1 IgG levels and protection from infection was higher during the Omicron period | Delta–Omicron | ELISA |
Wei J. et al. [34] | 2022 | UK | Longitudinal | 222,493 | 56.0 | 53.8 | mRNA-VV | 100.0 | 0.0 | 0.0 | 71–76 | 9.7 | ChAdOx1 or BNT162b2 required estimated levels of 107 BAU/mL and 94 BAU/mL, respectively | Delta | ELISA |
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Rahmani, A.; Montecucco, A.; Priano, L.; Mandolini, L.; Dini, G.; Durando, P. Serological Correlates of Protection Induced by COVID-19 Vaccination in the Working Age Population: A Systematic Review and Meta-Analysis. Vaccines 2024, 12, 494. https://doi.org/10.3390/vaccines12050494
Rahmani A, Montecucco A, Priano L, Mandolini L, Dini G, Durando P. Serological Correlates of Protection Induced by COVID-19 Vaccination in the Working Age Population: A Systematic Review and Meta-Analysis. Vaccines. 2024; 12(5):494. https://doi.org/10.3390/vaccines12050494
Chicago/Turabian StyleRahmani, Alborz, Alfredo Montecucco, Luca Priano, Lucia Mandolini, Guglielmo Dini, and Paolo Durando. 2024. "Serological Correlates of Protection Induced by COVID-19 Vaccination in the Working Age Population: A Systematic Review and Meta-Analysis" Vaccines 12, no. 5: 494. https://doi.org/10.3390/vaccines12050494