Multiplex serology reveals age-specific immunodynamics of respiratory pathogens in the wake of the COVID-19 pandemic

2025 Nature Communications 0 citations

Abstract

The rebound of endemic respiratory viruses following the COVID-19 pandemic was marked by atypical transmission dynamics, with children experiencing increased disease burden and out-of-season epidemics as restrictions relaxed. Here we used serology from a newly developed quantitative multiplex assay to assess the post-pandemic immunity debt. We assessed age-specific antibody dynamics across a broad range of respiratory viruses, including influenza, respiratory syncytial virus, seasonal coronaviruses, and SARS-CoV-2 using serology collected in King County, Washington, US, from 2020-2022 (n = 1508). We found that respiratory virus immunodynamics differed between individuals <5 years of age and older individuals, with young children experiencing larger boosts and quicker waning of antibodies across pathogens. We confirmed that these patterns are upheld in a non-pandemic setting by analyzing influenza serology collected in South Africa between 2016-2018 (n = 1028). We incorporated our serological insights into an influenza transmission model calibrated to epidemiological data from King County and show that consideration of age-specific immunodynamics may be important to anticipate the effects of pandemic perturbations.

MeSH Terms

HumansCOVID-19ChildPreschoolSARS-CoV-2ChildAntibodiesViralAdultInfluenzaHumanMiddle AgedAdolescentAge FactorsInfantYoung AdultMaleFemaleAgedPandemicsWashingtonSouth Africa

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Publication Info

Year
2025
Type
article
Volume
16
Issue
1
Pages
11015-11015
Citations
0
Access
Closed

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Samantha J. Bents, Emily T. Martin, Terry Stevens‐Ayers et al. (2025). Multiplex serology reveals age-specific immunodynamics of respiratory pathogens in the wake of the COVID-19 pandemic. Nature Communications , 16 (1) , 11015-11015. https://doi.org/10.1038/s41467-025-65957-9

Identifiers

DOI
10.1038/s41467-025-65957-9
PMID
41372127
PMCID
PMC12695953

Data Quality

Data completeness: 77%