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© Research
Publication : Nature communications

Reconstructing antibody dynamics to estimate the risk of influenza virus infection.

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Nature communications - 23 Mar 2022

Tsang TK, Perera RAPM, Fang VJ, Wong JY, Shiu EY, So HC, Ip DKM, Malik Peiris JS, Leung GM, Cowling BJ, Cauchemez S,

Link to Pubmed [PMID] – 35322048

Link to DOI – 10.1038/s41467-022-29310-8

Nat Commun 2022 Mar; 13(1): 1557

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.

https://pubmed.ncbi.nlm.nih.gov/35322048