tetano
Editor, Senior Moderator
Sci Rep
. 2025 Oct 7;15(1):34899.
doi: 10.1038/s41598-025-18706-3. Clustering and time series analyses of hybrid immunity to SARS-COV-2 using data from the BQC19 biobank
Jean-Frédéric Boulianne[SUP] 1 2 [/SUP], Denis Larocque[SUP] 1 [/SUP], Simon Rousseau[SUP] 3 4 [/SUP], Delphine Bosson-Rieutort[SUP] 5 6 [/SUP]
Affiliations
The SARS-CoV-2 pandemic revealed that immunity after infection was temporary, with reinfections occurring. As the pandemic progressed, individuals encountered infection and vaccination in varying sequences and at different time intervals, resulting in heterogeneous patterns of infection, reinfection and vaccination, so-called hybrid immunity. This study analyzed these patterns by grouping individuals based on their infection, reinfection, and vaccination sequences using data from the Biobanque québécoise de la COVID-19 (BQC19). We applied agglomerative and divisive hierarchical clustering on time series representing patients' COVID-19 episodes, using Dynamic Time Warping to compute distances. Their characterization revealed that clusters followed a temporal progression depending on the timing of infection and its positioning across the pandemic waves. On the other hand, reinfections occurred from the fifth wave onward. The most highly vaccinated groups appear to have been infected and consequently reinfected later in the pandemic. Some groups featured a higher proportion of healthcare workers, while for others, the trajectory and their timeframes were decisive. This study highlights the role of vaccination, which is in line with current knowledge. It also shows that, beyond the sequence of events, it is rather their temporality and the delays between them that are of greatest importance. In terms of hybrid immunity, the results of this study suggest that an infection between two vaccines could offer greater immunity.
Keywords: BQC19; COVID-19; Cluster analysis; Immunity; SARS-CoV-2; Time series.
. 2025 Oct 7;15(1):34899.
doi: 10.1038/s41598-025-18706-3. Clustering and time series analyses of hybrid immunity to SARS-COV-2 using data from the BQC19 biobank
Jean-Frédéric Boulianne[SUP] 1 2 [/SUP], Denis Larocque[SUP] 1 [/SUP], Simon Rousseau[SUP] 3 4 [/SUP], Delphine Bosson-Rieutort[SUP] 5 6 [/SUP]
Affiliations
- PMID: 41057392
- PMCID: PMC12504541
- DOI: 10.1038/s41598-025-18706-3
The SARS-CoV-2 pandemic revealed that immunity after infection was temporary, with reinfections occurring. As the pandemic progressed, individuals encountered infection and vaccination in varying sequences and at different time intervals, resulting in heterogeneous patterns of infection, reinfection and vaccination, so-called hybrid immunity. This study analyzed these patterns by grouping individuals based on their infection, reinfection, and vaccination sequences using data from the Biobanque québécoise de la COVID-19 (BQC19). We applied agglomerative and divisive hierarchical clustering on time series representing patients' COVID-19 episodes, using Dynamic Time Warping to compute distances. Their characterization revealed that clusters followed a temporal progression depending on the timing of infection and its positioning across the pandemic waves. On the other hand, reinfections occurred from the fifth wave onward. The most highly vaccinated groups appear to have been infected and consequently reinfected later in the pandemic. Some groups featured a higher proportion of healthcare workers, while for others, the trajectory and their timeframes were decisive. This study highlights the role of vaccination, which is in line with current knowledge. It also shows that, beyond the sequence of events, it is rather their temporality and the delays between them that are of greatest importance. In terms of hybrid immunity, the results of this study suggest that an infection between two vaccines could offer greater immunity.
Keywords: BQC19; COVID-19; Cluster analysis; Immunity; SARS-CoV-2; Time series.