Immunogenicity, efficacy, and safety of SARS-CoV-2 vaccine dose fractionation: a systematic review and meta-analysis

doi:10.21203/rs.3.rs-1571821/v1

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Immunogenicity, efficacy, and safety of SARS-CoV-2 vaccine dose fractionation: a systematic review and meta-analysis

https://doi.org/10.21203/rs.3.rs-1571821/v1

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Dose fractionation of Coronavirus Disease 2019 (COVID-19) vaccine could effectively accelerate global vaccine coverage, while supporting evidence of efficacy, immunogenicity, and safety are needed, especially with emerging variants. We reviewed clinical trials reported dose-finding results to estimate the dose-response relationship of neutralizing antibodies (nAbs) of COVID-19 vaccines. We further predict the vaccine efficacy against both ancestral and variants, using previously reported correlates of protection and cross-reactivity. We also reviewed and compared T-cell responses and safety profiles between fractional and standard dose groups. We found that dose fractionation of mRNA and protein subunit vaccines could induce SARS-CoV-2 specific nAbs and T-cells that likely confer a reasonable level of protection (i.e., vaccine efficacy > 50%) against ancestral strains and variants. Safety profiles of fractional doses were non-inferior to the standard dose. Dose fractionation of mRNA and protein subunit vaccines may be safe and effective.

Three years into the pandemic, COVID-19 continues to threaten the global health with emerging variants. While vaccinations have averted tens of thousands of hospitalizations and deaths in high-income countries ^1,2, only 14.5% of people in lower income countries received at least one dose as of 1 April 2022 ³. Dose fractionation of vaccines has been previously recommended to ease global supply shortage and accelerate vaccine coverage in low-income countries, where a larger proportion of the population could have access to vaccination despite for each individual would receive a lower vaccine dose ^4,5. In 2016, fractional doses of yellow fever vaccines were non-inferior to the standard dose in immunogenicity and safety ⁶ and recommended for emergency use by the World Health Organization (WHO) ⁵. However, uncertainties and concerns about the vaccine efficacy using fractional doses against SARS-CoV-2 ancestral strains and emerging variants of concern (VoCs) ^7–9, and the potential differences between vaccine platforms, hindered the endorsement for dose fractionation of COVID-19 vaccines ^10,11.

Modelling studies suggested that dose fractionation of COVID-19 vaccines may be cost-effective in low-income countries, while such findings were largely determined by vaccine efficacy. However, as few clinical trials of dose fractionation of COVID-19 vaccines were available (possibly lack of commercial incentives ¹²), those modelling studies either assumed a range of hypothetic vaccine efficacy or used data against the ancestral strains ^12,13. Additionally, VoCs evaded vaccine-induced immunity and resulted in lower vaccine efficacy against symptomatic infections ^7–9, adding more complexity to assessing the efficacy of fractioning CVODI-19 vaccine doses. Therefore, reliable estimations of vaccine efficacy against both ancestral and VoCs of fractional doses is critical to determine the cost-effectiveness.

Studies so far suggest vaccine-induced neutralizing antibodies (nAbs) may be a good correlate of protection (CoP) against infection outcomes for ancestral strain and VoCs ^14,15. Therefore, characterizing the dose-response relationship between vaccine dosage and vaccine-induced nAbs may allow prediction of efficacy of fractional doses. In addition, evidence suggested that T-cell mediated immunity, especially CD4 + cells, may contribute to better disease outcomes ^16,17, indicating that comparing the T-cells responses elicited by fractional doses to that from standard doses may further inform the potential vaccine efficacy against severe infections conferred by fractional doses.

Here, we conducted a systematic review and meta-analysis of phase I/II trials that reported dose-finding results of immunogenicity and safety profiles for COVID-19 vaccines (detailed search terms in Table S1). We estimated the pooled dose-response relationship of nAbs against the ancestral strains, which were then used to predict the potential VE of fractional doses against infections of both ancestral strains and VoCs. We also reviewed the differences in seroconversion of nAbs, T-cell mediated immune responses and safety profile between fractional and standard dose (i.e., doses used for final products or phase III trials) groups, to further assess the differences in immunogenicity and safety after receiving fractional and standard doses.

We systematically reviewed 38 studies (out of 1,733 searched; Supplementary Fig. 1) ^18–55, among which inactivated vaccines (29%, n = 11) were studied the most, followed by protein subunit (“subunit” hereafter; 26%, n = 10), mRNA (24%, n = 9), and non-replicating viral vector (“vector” hereafter; 13%, n = 5) (Supplementary Fig. 1 and Supplementary Table 2). We found overall low risks of bias of the included studies, expect that seven adopted the non-randomized, and non-double-blinded design (Supplementary Fig. 2) ^{18,28,29,34,39,41,55}.

We estimated the pooled risk ratio (RR) of the seroconversion (pre-defined by each study; details in Supplementary Table 3) against ancestral strains among individuals who completed fractional and standard dose from 14 studies of 9 vaccines (Fig. 1). The probability of seroconversion to ancestral strains was 2.1% (95% confidence interval (CI) 0.4–3.6%; \({I}^{2}\)= 52.0%, P-value < 0.01) lower among individuals with fractional doses compared to standard doses. However, we found no association between dose fractionation (1.4%, 95% CI, -20.4–29.3% per fold increase in dose) and seroconversion proportions between lower and standard dose groups after accounting for vaccine platform, age group and assay methods (i.e., live or pseudo virus) (Supplementary Table 4).

To quantify the dose-response relationship of nAbs and dose fractionation, we fitted a generalized additive model (deviance explained 79%; Supplementary Fig. 3 and Table 5) to vaccine-induced nAbs levels against ancestral strains and accounted for the vaccine platform, vaccination schedule (i.e., total dosages and time since complete vaccinations), age group and assay methods. Post-vaccine nAbs levels were standardized to the ratio of nAbs titers induced by vaccinations and natural infections within each study ¹⁴. We also applied the previously established CoP of nAbs ¹⁴ to predict the vaccine efficacy of fractional doses against symptomatic and severe infection of ancestral strains.

Twenty-four studies reported nAbs against live (n = 20) and/or pseudo (n = 7) ancestral viruses from both post-vaccination and convalescent sera (Supplementary Figs. 4–6 and Supplementary Table 3). We estimated that two half-dose mRNA vaccines would elicit 2.6 (95% CI, 2.1 to 3.3, measured on day 14) fold of the nAbs against the ancestral strain in convalescent sera (Fig. 2A), which is expected to prevent 97% (95% CI, 95–97%) of symptomatic and 100% (95% CI, 100–100%) of severe infections of the ancestral strains, respectively (Fig. 2B). Whereas two half-dose inactivated vaccines would elicit 0.28 (95% IC 0.20 to 0.37) -fold of nAbs against the ancestral strains in convalescent sera, corresponding to 61% (95% CI, 51–70%) and 95% (95% CI, 92–96%) efficacy against symptomatic and severe infections of the ancestral strains, respectively. Overall, our predictions suggested that the reduction in vaccine efficacy was smaller than dose fractionation across all vaccine platforms (Fig. 2C); half-doses may provide more than half of protection efficacy of standard doses. We also estimated that fully vaccinated with fractional doses elicited higher nAbs than partially vaccinated with standard dose across all platforms (Fig. 2A and Supplementary Figs. 3–7).

Further incorporating the reported fold reduction in of vaccine-induced nAbs against VoCs (Supplementary Table 6)^7,15, we projected that two half-dose mRNA vaccines would confer the highest efficacy against symptomatic infections of VoCs (94%, 95% CI, 92–95% against Alpha, 63%, 54–70% against Beta, 85%, 79–89% against Gamma, 83%, 78–87% against Delta and 32%, 26–40% against Omicron), followed by subunit, vector and inactivated vaccines (Fig. 3 and Supplementary Fig. 8). Half-dose vaccine efficacy against severe infections of Alpha, Beta, Gamma, and Delta was predicted to be over 75% for across vaccine platforms, except for inactivated (52%, 95% CI, 41–63%) and vector (62%, 47–76%) vaccines against Beta (Fig. 3 and Supplementary Fig. 9). The efficacy against severe infections by Omicron was only estimated to above 50% for two half-dose mRNA (85%, 80–88%) and subunit (69%, 62–75%) vaccines. Our predicted efficacy against symptomatic infections of VoCs for standard dose highly correlated (Pearson correlation 0.705, p-value < 0.01; Supplementary Fig. 10) with empirical data (Supplementary Table 7) ^56–66, while we were not able to validate predictions for fractional doses due to lack of data.

Since assays and measurements used for cellular responses vary across studies, we reviewed whether T-cell mediated immune responses elicited by dose fractioning vaccines 1) would be higher than pre-vaccination level and 2) would be lower than that elicited by the standard dose vaccine. We assessed the statistical significance of difference in means of measurements of T-cells and related cytokines between groups within the same study (Supplementary Table 8; see Methods). All 7 studies of 5 vaccines reported significant increase in SARS-CoV-2 specific CD4+/CD8 + or CD4 + T helper type 1 (Th1) responses after vaccinated with fractional doses compared to pre-vaccination (Fig. 4A), which were all biased to Th1 cells. Three vaccines (BNT162b1^32,39, MVC-COV1901²⁸ and Sf9 cells³³) reported that dose fractionation elicited similar level of CD4 + and/or CD8 + T-cells compared to standard dose (Fig. 4B). Quarter-dose of mRNA-1273 ^18,29 was reported to induce significantly lower CD4 + Th1 cells compared to standard dose, while half-dose of BBV152 were reported to induce significantly higher Th1 cytokines in one of two trials. We also compared the cellular responses between standard and higher dose groups and found no evidence for dose-dependent relationship for 7 out of 9 vaccines (Supplementary Fig. 11).

We reviewed the safety profile for 34 studies and found that, compared to standard dose group, people in the fractional dose groups tended to experience adverse events at similar or lower frequency (Supplementary Figs. 12–17). Particularly, the risk of experiencing solicited, and unsolicited adverse events were 9.5% (95% CI, 3.9–14.8%) and 24.4% (3.9–41.1%) lower in individuals who received factional dose of mRNA vaccines compared to standard doses (Supplementary Figs. 15–16). One inactivated (BBIBP-CorV in children ⁴⁸) and two subunit (NVX- CoV2373 and Livzon in adults ^24,40) vaccines reported higher risk of solicited systemic reactions in groups that received lower dose than the standard dose.

Our findings suggested that vaccine-induced nAbs varied substantially across dose fractions and vaccine platforms. For vaccine platforms (e.g., mRNA and subunit) which standard doses could elicit higher nAbs levels than convalescent sera, dose fractionation could induce robust nAbs against the ancestral strains and similar seroconversion proportion with standard doses. nAbs induced by fractional vaccines of mRNA and subunit were predicted to confer \(\ge\) 65% efficacy against symptomatic and severe infections of SARS-CoV-2 variants, except for symptomatic infections of Beta and Omicron. Fractionation of vaccine doses seemed to be safe and induce robust Th1 biased T-cell responses that were similar to standard doses except for mRNA-1273.

We found that dose fractionation of COVID-19 vaccines would induce, though lower than standard doses, detective nAbs against ancestral strains. Based on previously established CoP ^14,15,67, these nAbs may confer reasonable protection (i.e., > 50%) against symptomatic infections of ancestral strains, but not the subsequent VoCs, especially Omicron. Previous modelling study suggested that dose fractionation could be a cost-effective strategy in low-income countries, if vaccination could confer at least 50% of protection against symptomatic infections of variants with low or moderate transmissibility (i.e., basic reproduction number \({R}_{0}\) < 5) ¹³. Given these findings, our results of nAbs and vaccine efficacy predictions suggested that dose fractionation could have been cost-effective strategy to control the emergence of some early VoCs (e.g., Alpha and Delta), but not for the currently circulating Omicron given the significant immunity breakthrough ^7,8 and higher transmissibility ⁶⁸.

Despite relatively lower levels, nAbs elicited by fractional doses were predicted with high efficacy against severe infections of VoCs across vaccine platforms, even against Omicron (e.g., RNA, and subunit). Furthermore, fractional doses of most studied vaccines could induce detective and likely robust T-cell responses, which may also facilitate the vaccines’ protections against severe outcomes, given that SARS-CoV-2 specific T-cells could broadly cross-react to a range of VoCs (including Omicron) and were associated with better outcomes ^16,17. Therefore, dose fractionation of COVID-19 vaccines may still avert a considerable number of hospitalizations and deaths, even with the emergence of new variants with vaccine breakthrough.

Our results indicated that nAbs and thus the vaccine efficacy of two half-doses were higher than that by one standard dose. Given the likely robust protection of fractional doses against severe infections of both ancestral and variant strains, it therefore may avert more hospitalizations and deaths by splitting one dose into two. Shipping the standard doses and splitting at the injection sites may make dose fractionation more cost-effective by reducing logistical cost, especially cost arising from manufacture and delivery.

We were not able to assess the durability of the immune responses elicited by fractional doses of COVID-19 vaccines, as most trials reported limited follow up that was typically just one month after vaccination. Therefore, our efficacy estimates may only be indicative for short-term protection. Waning SARS-CoV-2 specific aAbs, T-cells and vaccine efficacy (against both ancestral and VoCs) may be expected, as suggested by evidence from individuals receiving standard doses after 6 months ^8,69−71. For standard doses, both homogeneous and heterogenous boosters could substantially increase nAbs and vaccine efficacy against VoCs ⁹, while such data were lacking for fractional doses.

We did not look at the nAbs induced by individual vaccine manufacturers due to limited data, while we found consistent seroconversion proportion and dose-relationship within platform (Supplementary Figs. 3–7). Nevertheless, disparities in nAbs levels and durability were reported for individual vaccines from the same platform (e.g., mRNA-1273 vs. BNT162b1 vaccines ¹⁰).

To summarise, fractionation of vaccine doses, especially mRNA and protein subunit vaccines, are safe and would induce antibody and T-cell responses that likely confer a reasonable level of protection against severe infections of SARS-CoV-2 ancestral and VoCs. Using fractional doses could provide a more efficient use of limited antigen stockpiles.

Search strategy and study selection

We searched peer-reviewed publications on clinical trials of SARS-CoV-2 vaccines in PubMed on 9 December 2021. We searched with the following terms: (SARS-CoV-2 OR COVID-19) AND (vaccine AND dose) AND (antibod* OR immun*) (Supplementary Table 1). We included dose-escalation studies that reported safety, neutralizing antibodies (nAbs, which were measured by PRNT₅₀ and/or sVNT), and/or T-cell mediated immunity among healthy individuals received SRAS-CoV-2 vaccines (Supplementary Tables 2,3 and 8). We excluded 1) studies did not report immunological response or only reported binding antibody; 2) studies without dose-escalation; 3) studies on non-human hosts; 4) studies on participants with specific health conditions (e.g., cancer, organ transplantation) or pregnancy; 5) studies specifically designed for hybrid immunity (i.e., natural infection or heterogenous vaccinations); and 6) reviews or commentaries (Supplementary Fig. 1). We assessed the quality of included studies using the Cochrane Risk of Bias tool 2.0 for randomized trials ⁷² (Supplementary Fig. 2).

Data extraction and processing

Two reviewers (BY and XH) independently screened the titles and full texts of articles according to the inclusion and exclusion criteria. For each included study, we extracted relevant information of the vaccines and participants onto a standardized form, which includes vaccine name, manufacture, platform, dose fraction, vaccination and sampling schedule, age group, and sizes of vaccinated subjects. Dose fraction (\({F}_{i,j}\)) was defined as the ratio of each examined dose group (\({d}_{i,j}\)) and the standard dose (\({d}_{ref,j}\), defined as the dose selected for the approved vaccine product or phase III trials) for each study (\(j\)):

\({F}_{i,j}=\frac{{d}_{i,j}}{{d}_{ref,j}}\)

(1)

Differences in seroconversion of neutralizing antibodies after fractional and standard dose

We compared the seroconversion proportion to nAbs against ancestral strains after receiving fractional doses compared with the standard dose group, where seroconversion was predefined by each study as at least four-fold increase in nAbs and/or changing from negative to positive after vaccinations (details about definitions for positive threshold and seroconversion were shown in Supplementary Table 3). Sample size and the number of seroconverted participants were extracted for each dose group, which were then used to estimate the pooled log risk ratio of seroconversion between fractional and standard dose group using random effects (RE) model, stratified by vaccine type (Fig. 1). We fitted mixed effects meta-regressions to assess the effects of vaccine platform and dose fractions on seroconversion, after accounting for age group and assay methods (Supplementary Table 4). We also repeated the above analysis for higher dose group, which results can be found in our data repository.

Dose-response relationship of neutralizing antibodies

For each study \(j\), we extracted the mean (\({\mu }_{i,j}\)) and standard deviations (\({\sigma }_{i,j}\)) of nAbs titers in different dose groups \(i\); if not reported, we estimated \({\mu }_{i,j}\) and \({\sigma }_{i,j}\)from 1) individual data points, or 2) median, interquartile (IQR) and sample sizes⁷³. We then standardized the vaccine-induced nAbs level (\({z}_{i,j}\)) using the nAbs measured in convalescent sera (\({\mu }_{c,j}\)) for each study:

\({z}_{i,j}=\frac{{u}_{i,j}}{{\mu }_{c,j}}\)

(2)

We summarized standardized nAbs among different dose groups (i.e., fractional, standard, and higher dose groups) at different time points (Supplementary Fig. 2). To quantify the dose-response relationship of vaccination and the standardized nAbs (\({z}_{i,j}\)), we fitted a generalized additive model (GAM; Supplementary Table 5) that accounted for the vaccine platform (\(V\)), vaccine schedule (i.e., total dosages \(D\) and days after full vaccination \({T}_{i,j}\)), age group (\(A\)= children, adult, or elderly) and antigen used for neutralizing assay (\(M\) = live or pseudo virus):

\({\text{log}}_{2}{z}_{i,j}={\beta }_{0}+ {\beta }_{T}s\left({T}_{i,j}\right)+ {\beta }_{F}s\left({\text{log}}_{2}{F}_{i,j}\right)+{\beta }_{V}{V}_{j}+{\beta }_{D}{D}_{i,j}+ {\beta }_{A}{A}_{i,j}+{\beta }_{M}{M}_{i,j}\)

(3)

With estimates from Eq. 3, we predicted the standardized nAbs (assuming measured by live virus and in adults; same for the following) against SARS-CoV-2 ancestral strain after fully vaccinated (i.e., 1 dose for vector and 2 for the rest) with fractional doses (\({F}_{i,j}\)) for different vaccine platforms (Fig. 2A by dose fraction and Supplementary Fig. 7 by vaccine schedule). Validation of our predictions and raw data was shown in Supplementary Fig. 3.

Vaccine efficacy predicted from neutralizing antibodies

We applied the established CoP protection of standardized nAbs ^14,15 to predict the dose-fractioning vaccine efficacy (\({{\Phi }}_{i}\)) against infection outcomes (\(E\), i.e., symptomatic, or severe) of SARS-CoV-2 ancestral strain for different vaccine platform (\(V\)):

\({{\Phi }}_{i,V,E}=\frac{1}{1+{e}^{-{k}_{E}{\text{log}}_{10}\frac{{z}_{i,V}}{{z}_{50,E}} }}\)

(4)

We obtained the log-transformed 50% protective efficacy (\({\text{log}}_{10}{z}_{50,E}\)) and steepness parameter \({k}_{E}\) for both symptomatic and severe infection from the previous study ¹⁴. \({z}_{i,V}\) is the standardized nAbs at 14 days after fully vaccinated of fractional doses (\({F}_{i,j}\)) for each vaccine platform (\(V\)), which was estimated from Eq. 3 with coefficients shown in Supplementary Table 5.

We used previously reported ^7,15 fold of reduction (\({\delta }_{S}\); Supplementary Table 6) in nAbs to estimate the level of standardized nAbs (\({{\delta }_{S}z}_{i,V}\)) against the variant \(S\), which was then applied to Eq. 4 to predict the vaccine efficacy of dose fractioning against infections of SARS-CoV-2 variant of concerns (VoCs) (Fig. 3 and Supplementary Figs. 8–9). To validate our predicted vaccine efficacy against VoCs (Supplementary Fig. 10), we compared the predicted vaccine efficacy against symptomatic infections after standard dose and observations (Supplementary Table 7) reported previously by Pearson correlation. Standard doses were used for comparison since there were no empirical data regarding half-dose.

T-cell responses

Since assays and measurements used for T-cell mediated responses vary across studies, we reviewed if T-cell responses elicited by dose-fractioning vaccines 1) would be higher than that at pre-vaccination level and 2) would be lower than that elicited by the standard dose vaccine within the same study. Briefly, we extracted mean (\({\stackrel{-}{x}}_{i,j,k}\)), standard error (SE, \({\widehat{\sigma }}_{{\stackrel{-}{x}}_{i,j,k}}\)) and sample size (\({n}_{i}\)) of specific measurement \(k\) for T-cell responses for each dose group or reference group (i.e., pre-vaccination or post standard dose vaccination) \(i\) in study \(j\). Specific measurement (\(k\)) includes T-cell types (i.e., CD4 + or CD8+) and/or cytokines for T helper type 1 (Th1, including interferon-\(\gamma\) (IFN- \(\gamma\)), tumor necrosis factor (TNF- \(\alpha\)) and Interleukin-2 (IL-2)) and T helper type 2 (Th2, including IL-4, IL-5, IL-13). If mean and SE were not reported, we estimated these metrics from individual original data points or median, IQR and sample sizes ⁷³. We determined the statistical significance of difference in means (\({{\Delta }}_{i,j,k}\)) assuming it follows a normal distribution (Fig. 4 and Supplementary Fig. 11).

\({\stackrel{-}{{\Delta }}}_{i,j,k}= {\stackrel{-}{x}}_{i,j,k}- {\stackrel{-}{x}}_{ref,j,k}\)	(5)
\({\widehat{\sigma }}_{{\stackrel{-}{{\Delta }}}_{i,j,k}}= \sqrt{\frac{{{\widehat{\sigma }}_{{\stackrel{-}{x}}_{i,j,k}}}^{2}}{{n}_{i}}+ \frac{{{\widehat{\sigma }}_{{\stackrel{-}{x}}_{ref,j,k}}}^{2}}{{n}_{ref}}}\)	(6)

Safety

We compared the safety profiles after receiving fractional dose compared with the standard dose group. We extracted the sample size and the number of adverse events (AEs, i.e., solicited local and/or systemic events, unsolicited events, and any AEs) for each dose group. Individual manifestations within each AE category were extracted and assessed. We estimated the pooled log risk ratio of experiencing AEs between fractional and standard dose group using RE model and stratifying by specific AE and vaccine platform (Supplementary Fig. 12–17). We calculated the I² to measure the heterogeneity of the included estimates. We also repeated the above analysis for higher dose group, which results can be found in our data repository.

Data availability

All data were collected from publicly available literatures, with detailed description in the Methods and Supplementary Tables. Data used for the analysis can be assessed at: https://github.com/byyangyby/fractional_dose_review.

Code availability

The authors declare that all codes for analyzing the data are made available at https://github.com/byyangyby/fractional_dose_review.

Acknowledgements

This project was supported by the Health and Medical Research Fund, Food and Health Bureau, Government of the Hong Kong Special Administrative Region (grant no. COVID190118) and the Theme-based Research Scheme (Project No. T11-712/19-N) of the Research Grants Council of the Hong Kong SAR Government. BJC also acknowledges the support AIR@InnoHK administered by Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region.

Author contributions

All authors meet the ICMJE criteria for authorship. BY and BJC conceived the study. BY and XH performed the literature review and screening. BY, XH and HG extracted data. BY analyzed the data and wrote the first draft of the manuscript. All authors provided critical review and revision of the text and approved the final version.

Completing interest statement

B.J.C. consults for AstraZeneca, GSK, Moderna, Roche, Sanofi Pasteur, and Pfizer. The remaining authors declare no competing interests.

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Yes there is potential Competing Interest. B.J.C. consults for AstraZeneca, GSK, Moderna, Roche, Sanofi Pasteur, and Pfizer. The remaining authors declare no competing interests.

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Immunogenicity, efficacy, and safety of SARS-CoV-2 vaccine dose fractionation: a systematic review and meta-analysis

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Discussion

Methods

Search strategy and study selection

Data extraction and processing

Differences in seroconversion of neutralizing antibodies after fractional and standard dose

Dose-response relationship of neutralizing antibodies

Vaccine efficacy predicted from neutralizing antibodies

T-cell responses

Safety

Data availability

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