tetano
Editor, Senior Moderator
Lancet Infect Dis
. 2026 Apr 17:S1473-3099(26)00061-7.
doi: 10.1016/S1473-3099(26)00061-7. Online ahead of print.
Measuring population immunity against influenza using individual antibody titres: a multicountry, retrospective observational study
Weijia Xiong[SUP] 1 [/SUP], Xiaotong Huang[SUP] 1 [/SUP], Tran Thi Nhu Thao[SUP] 2 [/SUP], Nguyen Thi Le Thanh[SUP] 2 [/SUP], Liping Peng[SUP] 1 [/SUP], Ranawaka A P M Perera[SUP] 1 [/SUP], Vicky J Fang[SUP] 1 [/SUP], Bingyi Yang[SUP] 1 [/SUP], Sook-San Wong[SUP] 3 [/SUP], Nancy H L Leung[SUP] 1 [/SUP], Sheena G Sullivan[SUP] 4 [/SUP], Dennis K M Ip[SUP] 1 [/SUP], Malik Peiris[SUP] 5 [/SUP], Ted M Ross[SUP] 6 [/SUP], Maciej F Boni[SUP] 7 [/SUP], Benjamin J Cowling[SUP] 1 [/SUP], Tim K Tsang[SUP] 8 [/SUP]
Affiliations
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 observational 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.
Funding: Hong Kong Health Bureau and Research Grants Council.
. 2026 Apr 17:S1473-3099(26)00061-7.
doi: 10.1016/S1473-3099(26)00061-7. Online ahead of print.
Measuring population immunity against influenza using individual antibody titres: a multicountry, retrospective observational study
Weijia Xiong[SUP] 1 [/SUP], Xiaotong Huang[SUP] 1 [/SUP], Tran Thi Nhu Thao[SUP] 2 [/SUP], Nguyen Thi Le Thanh[SUP] 2 [/SUP], Liping Peng[SUP] 1 [/SUP], Ranawaka A P M Perera[SUP] 1 [/SUP], Vicky J Fang[SUP] 1 [/SUP], Bingyi Yang[SUP] 1 [/SUP], Sook-San Wong[SUP] 3 [/SUP], Nancy H L Leung[SUP] 1 [/SUP], Sheena G Sullivan[SUP] 4 [/SUP], Dennis K M Ip[SUP] 1 [/SUP], Malik Peiris[SUP] 5 [/SUP], Ted M Ross[SUP] 6 [/SUP], Maciej F Boni[SUP] 7 [/SUP], Benjamin J Cowling[SUP] 1 [/SUP], Tim K Tsang[SUP] 8 [/SUP]
Affiliations
- PMID: 42009019
- DOI: 10.1016/S1473-3099(26)00061-7
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 observational 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.
Funding: Hong Kong Health Bureau and Research Grants Council.