Reconstructing antibody dynamics to estimate the risk of influenza virus infection

Tim K. Tsang, Ranawaka A. P. M. Perera, Vicky J. Fang, Jessica Y. Wong, Eunice Y. Shiu, Hau Chi So, Dennis K. M. Ip, J. S. Malik Peiris, Gabriel M. Leung, Benjamin J. Cowling () and Simon Cauchemez
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Tim K. Tsang: The University of Hong Kong
Ranawaka A. P. M. Perera: The University of Hong Kong
Vicky J. Fang: The University of Hong Kong
Jessica Y. Wong: The University of Hong Kong
Eunice Y. Shiu: The University of Hong Kong
Hau Chi So: The University of Hong Kong
Dennis K. M. Ip: The University of Hong Kong
J. S. Malik Peiris: The University of Hong Kong
Gabriel M. Leung: The University of Hong Kong
Benjamin J. Cowling: The University of Hong Kong
Simon Cauchemez: Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-29310-8

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