Statistical analysis
∗∗∗
Statistical analysis was performed using Python (version 3.10.1) and the SciPy Stats library (version 1.9.2). Non-parametric tests were used for all comparative analyses since most comparisons were made within heterogeneous study groups with a relatively small sample size. To control for type I errors, we used Bonferroni post-hoc test. Comparisons between timepoints were considered dependent comparisons, while comparisons based on the number of vaccine doses and COVID-19 status were considered independent comparisons because subgroup compositions varied relative to the number of vaccine doses, in line with national vaccine recommendations at the time. Therefore, we used Wilcoxon signed-rank test for dependent comparisons and Mann–Whitney U-test for independent comparisons, both with Bonferroni correction for multiple tests. For dependent tests, missing data-pairs were excluded. In total, there were 95 missing sampling timepoints (out of 1424) in the reconsented study which corresponds 6.7%. The Spearman correlation test was used to assess correlation between antibody titres and COVID-19 severity. For Pearson correlation tests, we used Shapiro–Wilks test for normality. Geometric mean was used to display mean values of antibody titres. The statistical tests used are indicated in the figure legends. The star annotation (∗) indicates statistical significance at a p-value threshold of 0.05 (or ∗∗ for p < 0.01, ∗∗∗ for p < 0.001, ∗∗∗∗ for p < 0.0001). Additionally, non-dichotomized p-values are listed in 