Reconstructing antibody dynamics to estimate the risk of influenza virus infection

Tim K Tsang (1) , Ranawaka a P M Perera (1) , Vicky J Fang (1) , Jessica Y Wong (1) , Eunice Y Shiu (1) , Hau Chi So (1) , Dennis K M Ip (1) , J S Malik Peiris (1) , Gabriel M Leung (1) , Benjamin J Cowling (1) , Simon Cauchemez (2)

1 HKU - The University of Hong Kong
2 Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases

Abstract For >70 years, a 4-fold or greater rise in antibody titer has been used to confirm influenza virus infections in paired sera, despite recognition that this heuristic can lack sensitivity. Here we analyze with a novel Bayesian model a large cohort of 2353 individuals followed for up to 5 years in Hong Kong to characterize influenza antibody dynamics and develop an algorithm to improve the identification of influenza virus infections. After infection, we estimate that hemagglutination-inhibiting (HAI) titers were boosted by 16-fold on average and subsequently decrease by 14% per year. In six epidemics, the infection risks for adults were 3%–19% while the infection risks for children were 1.6–4.4 times higher than that of younger adults. Every two-fold increase in pre-epidemic HAI titer was associated with 19%–58% protection against infection. Our inferential framework clarifies the contributions of age and pre-epidemic HAI titers to characterize individual infection risk.

Domains: Life Sciences [q-bio]

Tim K Tsang, Ranawaka a P M Perera, Vicky J Fang, Jessica Y Wong, Eunice Y Shiu, et al.. Reconstructing antibody dynamics to estimate the risk of influenza virus infection. Nature Communications, 2022, 13 (1), pp.1557. ⟨10.1038/s41467-022-29310-8⟩. ⟨pasteur-04095291⟩