tetano
Editor, Senior Moderator
Nat Commun
. 2022 Mar 23;13(1):1557.
doi: 10.1038/s41467-022-29310-8.
Reconstructing antibody dynamics to estimate the risk of influenza virus infection
Tim K Tsang[SUP] 1 2 [/SUP], Ranawaka A P M Perera[SUP] 1 3 [/SUP], Vicky J Fang[SUP] 1 [/SUP], Jessica Y Wong[SUP] 1 [/SUP], Eunice Y Shiu[SUP] 1 [/SUP], Hau Chi So[SUP] 1 [/SUP], Dennis K M Ip[SUP] 1 [/SUP], J S Malik Peiris[SUP] 1 3 [/SUP], Gabriel M Leung[SUP] 1 2 [/SUP], Benjamin J Cowling[SUP] 4 5 [/SUP], Simon Cauchemez[SUP] 6 [/SUP]
Affiliations
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.
. 2022 Mar 23;13(1):1557.
doi: 10.1038/s41467-022-29310-8.
Reconstructing antibody dynamics to estimate the risk of influenza virus infection
Tim K Tsang[SUP] 1 2 [/SUP], Ranawaka A P M Perera[SUP] 1 3 [/SUP], Vicky J Fang[SUP] 1 [/SUP], Jessica Y Wong[SUP] 1 [/SUP], Eunice Y Shiu[SUP] 1 [/SUP], Hau Chi So[SUP] 1 [/SUP], Dennis K M Ip[SUP] 1 [/SUP], J S Malik Peiris[SUP] 1 3 [/SUP], Gabriel M Leung[SUP] 1 2 [/SUP], Benjamin J Cowling[SUP] 4 5 [/SUP], Simon Cauchemez[SUP] 6 [/SUP]
Affiliations
- PMID: 35322048
- DOI: 10.1038/s41467-022-29310-8
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.