INFLUENZA immunity levels in populations can help predict which virus strains will dominate and whether upcoming flu seasons will be larger or smaller, new research suggests.
Influenza Immunity Measures Help Predict Epidemic Patterns
Understanding population immunity is a key challenge in epidemic preparedness, but translating individual immune responses into reliable population-level indicators has remained difficult.
To address this, researchers analysed more than 41,000 serum samples collected across China, Hong Kong, Viet Nam, and the USA between 2009 and 2024. The data covered 27 influenza epidemics and were used to develop four different population immunity estimators based on antibody levels in individuals.
These measures included average antibody titres, the proportion of people without prior immunity, the estimated proportion of the population considered immune, and modelled reductions in virus transmission potential. Together, they aimed to provide a more complete picture of influenza immunity at the population level.
Predicting Dominant Strains and Epidemic Size
The immunity-based measures showed moderate to strong ability to predict which influenza subtype would dominate each season. They correctly identified the dominant strain in most cases, particularly for H3N2 seasons, where prediction accuracy reached 100%.
They also performed well in estimating whether upcoming epidemics would be larger or smaller than the previous year. In many cases, the models correctly anticipated reduced epidemic size when population immunity was higher, and larger outbreaks when immunity was lower.
In a long-term Hong Kong cohort, higher immunity estimates were generally associated with lower subsequent infection rates, particularly for H1N1 influenza. However, the relationship was weaker and less consistent for H3N2.
Implications for Public Health Planning
Antibody-based measures of influenza immunity could become useful tools for seasonal surveillance and preparedness. By identifying periods of lower population immunity, health systems may be able to better anticipate higher transmission seasons and allocate resources more effectively.
While the models show promising predictive ability, the authors note variability across influenza subtypes and uncertainty in some estimates, meaning further refinement is needed before routine use in public health decision-making.
Overall, the study demonstrates that population immunity can be quantified in meaningful ways that may help improve influenza forecasting and epidemic response planning.
Reference
Xiong W et al. Measuring population immunity against influenza using individual antibody titres: a multicountry, retrospective observational study. Lancet Infect Dis. 2026; DOI:10.1016/S1473-3099(26)00061-7.
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