Longitudinal determination of mRNA-vaccination induced strongly binding SARS-CoV-2 IgG antibodies in a cohort of Romanian healthcare workers

Abstract

Mass vaccination against the disease caused by the novel coronavirus (COVID-19) is a crucial step in slowing the spread of SARS-CoV-2. The BioNTech/Pfizer (BNT162b2) vaccine has been shown to induce strong immune responses among the vaccinated population. Measuring SARS-CoV-2 anti-spike protein IgG levels is a clinically convenient way to estimate post-vaccination humoral immune responses, but only limited data exists about its short- and long-term dynamics. We present a longitudinal analysis of post-vaccination IgG levels in a cohort of 122 healthcare workers vaccinated with BNT162b2 with weekly follow-up until 35 days past the first dose and results of the first monthly follow-up after that for a subset of these. This prospective, multicenter cohort study consists of two periods for short-term and long-term evaluation of post-vaccination IgG levels. Tests were carried out on 666 samples from 122 participants, using in-house anti-spike 1 and anti-nucleocapsid IgG ELISA assays and a commercial, combined version of these. Participants with previous SARS-CoV-2 infection mount a quick immune response, reaching peak IgG levels two weeks after vaccination. In contrast, the corresponding IgG levels for previously uninfected participants increase gradually, changing abruptly after the booster dose. Overall higher IgG levels are maintained for the previously infected group 35-70 days after vaccination, and we observe age-dependence of immune response as well. Our results show a robust humoral immune response mounting gradually after the first vaccine dose for the uninfected group, and a much stronger immune response within 7-14 days after the first dose for the previously infected group.

Impacts of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 variants Alpha and Delta on Coronavirus Disease 2019 transmission dynamics in four metropolitan areas of the United States

“…According to the model, the time that a person spends in is distributed according to the probability density function is Erlang distributed with shape parameter n V = 6 and rate parameter k V = 0.3 d -1) . As can be seen in Appendix Figure 8, the cumulative distribution function of this Erlang distribution reasonably captures the empirical cumulative distribution of waiting times observed in the longitudinal study of Korodi et al (4) . Thus, in the model, passage through V 1 , …, V 6 with rate constant k V = 0.3 d -1) accounts for the variable and significantly non-zero amount of time required for development of a protective antibody response after vaccination. The parameters f 0 > f 1 , f 1 > f 2 , and f 2 are fractions that characterize the average effectiveness of vaccines used in the US and that determine the sizes of (mutually exclusive) subpopulations of vaccinated persons having different susceptibilities to productive infection (i.e., an infection that can be transmitted to others): S V ,1 , S V ,2 , S V ,3 , and S V ,4 (Figure 1, Appendix Figure 1).…”

“…According to the model, the time that a person spends in is distributed according to the probability density function is Erlang distributed with shape parameter n V = 6 and rate parameter k V = 0.3 d -1) . As can be seen in Appendix Figure 8, the cumulative distribution function of this Erlang distribution reasonably captures the empirical cumulative distribution of waiting times observed in the longitudinal study of Korodi et al (4) . Thus, in the model, passage through V 1 , …, V 6 with rate constant k V = 0.3 d -1) accounts for the variable and significantly non-zero amount of time required for development of a protective antibody response after vaccination.…”

“…Recall that, in the model, susceptible persons enter V 1 upon vaccination (Figure 1, Appendix Figure 1). The values of n V (= 6) and k V (= 0.3 d -1) ) were selected so that the time a person spends in , which we will denote as t V , is distributed approximately the same as , the waiting time between vaccination of a previously uninfected person and detection of vaccine-induced SARS-CoV-2-specific IgG antibodies (4) (Appendix Figure 8). According to the model, the time that a person spends in is distributed according to the probability density function is Erlang distributed with shape parameter n V = 6 and rate parameter k V = 0.3 d -1) .…”

“…After susceptible persons enter V 1 , they can move through a series of additional compartments ( V 2 through V 6 ), which are included to capture the time needed for immunity to develop after completion of vaccination. We estimate that the time needed to acquire immunity after vaccination is approximately three weeks based on longitudinal studies of anti-spike protein IgG levels (4) . Persons who exit the V 6 compartment without becoming infected enter one of the following compartments: S V ,1 , S V ,2 , S V ,3 , or S V ,4 .…”

“…Comparison of an Erlang cumulative distribution function with shape parameter n V = 6 and rate parameter k V ( = 0.3 d -1) and the empirical cumulative distribution of waiting times ( values) observed in the longitudinal study of Korodi et al (4) . The waiting time is the time between vaccination of a previously uninfected person and detection of vaccine-induced SARS-CoV-2-specific IgG antibodies.…”