Tim Tsang
Assistant Professor Hong Kong University
The CEIRR CMC holds bi-monthly research seminars that feature leading researchers who develop state-of-the-art computational methods to better understand respiratory viruses. These events are designed to facilitate in-depth discussions and provide ample opportunity for questions from the audience, with each seminar lasting 1 hour and 30 minutes.
Title:
Measuring Population Immunity Against Influenza Using Individual Antibody Titers
Abstract:
Background Measuring population immunity is crucial for epidemic preparedness, but methods to translate individual immunity into population-level immunity metrics remain underdeveloped. We aimed to develop and evaluate population immunity estimators using influenza as a model pathogen.
Methods In this multicountry, retrospective cohort study, we analysed 41 835 serum samples from six studies in China, Hong Kong, Viet Nam, and the USA across 27 influenza epidemics in 2009–24. We constructed four population-immunity estimators from individual antibody titres: geometric mean titre, proportion of non-naive individuals, proportion of population immune, and relative reduction in reproductive number calculated using a next-generation matrix framework. We evaluated the ability of these estimators to predict which subtype (H1N1 vs H3N2) would dominate, to predict whether epidemics would be larger or smaller than the previous season, and to correlate with subsequent cumulative incidence in a longitudinal Hong Kong cohort spanning eight influenza seasons with within-epidemic serum collection.
Findings Subtype-specific relative changes in these estimators from previous seasons correctly predicted 57–86% of H1N1-dominant seasons and 100% of H3N2-dominant seasons, with an area under the receiver operating characteristic curve (AUROC) of 79–93%. The estimators correctly predicted larger epidemics in 67–83% of cases and smaller epidemics in 90–100% of cases, with an AUROC of 83–92%. Performance varied by estimator and influenza subtype, with wide uncertainty intervals for some estimates indicating modest precision. In the longitudinal Hong Kong cohort, all four estimators negatively correlated with subsequent 30-day cumulative incidence for H1N1 (Pearson correlations –0·23 to –0·46), whereas H3N2 correlations were mostly non-significant.
Interpretation We developed and validated four complementary population immunity estimators derived from individual antibody titres that showed predictive utility for influenza subtype dominance and epidemic size direction across diverse settings. These estimators could inform seasonal influenza preparedness, surveillance prioritisation, and health-care resource planning.
After you register, click on the Eventbrite event page for a zoom link.
Note: CEIRR CMC events are open only to CEIRR participants. The meeting and discussion are confidential.