Reconstructing antibody dynamics to estimate the risk of influenza virus infection

• Published in: Nature communications Vol. 13; no. 1; p. 1557
• Main Authors: Tsang, Tim K., Perera, Ranawaka A. P. M., Fang, Vicky J., Wong, Jessica Y., Shiu, Eunice Y., So, Hau Chi, Ip, Dennis K. M., Malik Peiris, J. S., Leung, Gabriel M., Cowling, Benjamin J., Cauchemez, Simon
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23-03-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 631/250/255; 692/699/255/1578; 692/700/478/174; Adult; Adults; Algorithms; Antibodies; Antibodies, Viral; Bayes Theorem; Bayesian analysis; Child; Communicable Diseases; Disease Susceptibility; Epidemics; Health risks; Hemagglutination; Hemagglutination Inhibition Tests; Humanities and Social Sciences; Humans; Infections; Influenza; Influenza Vaccines; Influenza, Human; Life Sciences; multidisciplinary; Orthomyxoviridae; Science; Science (multidisciplinary); Serology; Viruses
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-29310-8

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.
Serological classification of influenza infection has classically been based on a four-fold or higher increase in antibody levels, but this approach may not be optimal. Here, the authors develop a Bayesian model to improve identification of infections in serological samples by accounting for individual antibody dynamics.