A prospective, comparative cohort analysis of influenza antibody waning in Michigan and Hong Kong during periods of low influenza circulation
Yang, Y.; Smith, M.; Ho, F.; Truscon, R.; Leung, N. H. L.; Touyon, L.; Fitzsimmons, W. J.; Callear, A.; Godonou, E.-T.; Blair, C. N.; Monto, A.; Lauring, A. S.; Cowling, B. J.; Wong, S.-S.; Martin, E. T.
Show abstract
BackgroundReduced influenza transmission during the COVID-19 pandemic prompted concern about waning of population immunity that could lead to subsequent surges in circulation. We evaluated this by comparing longitudinal influenza antibody titers in Michigan and Hong Kong, two regions with reduced influenza transmission during the COVID-19 pandemic. MethodsIn two prospective cohort studies (HIVE, Michigan; EPI-HK, Hong Kong), we analyzed longitudinal serum samples collected from 2020 through 2023 from participants without documented influenza virus infection or vaccination. Sera were tested using hemagglutination inhibition assays (HAI) against relevant vaccine strains. Geometric mean titers (GMTs) and fold changes were estimated by region and time. Linear mixed-effects models were used to assess temporal trends. ResultsWe analyzed 173 sera from 57 HIVE participants and 259 sera from 60 EPI-HK participants. Initial GMTs in 2020-21 ranged from 12.3-123.4 in HIVE and 6.3-40.9 in EPI-HK (B/Yamagata-H1N1). Fold changes in GMTs ranged from 1.2-2.6 in HIVE and 0.7-1.0 in EPI-HK. In HIVE models, no significant change in HAI titers over time was detected. In EPI-HK, small but statistically significant monthly declines were observed for select H1N1 (A/Michigan) and H3N2 (A/Hong Kong) strains (e.g., A/Hong Kong: -0.98%, 95% CI: -1.82% to -0.11%). ConclusionMinimal HAI titer waning was observed in both regions. In some cases, antibody levels increased in Michigan, possibly indicating cryptic circulation of strains prior to the 2022/23 influenza season. These findings do not support an "immunity debt" during pandemic restrictions and could help explain the lack of a substantial surge in influenza impact after the COVID-19 pandemic.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Increased Influenza Severity in Children in the Wake of SARS-CoV-2 95%
- A rapid and flexible microneutralization assay for serological assessment of influenza viruses 94%
- The REinfection in COVID-19 Estimation of Risk (RECOVER) study: Reinfection and serology dynamics in a cohort of Canadian healthcare workers 93%
Similar papers in this journal
- Vaccine-induced antibody level predicts the clinical course of breakthrough infection of COVID-19 caused by delta and omicron variants: a prospective observational cohort study 94%
- High co-circulation of influenza and SARS-CoV-2 94%
- SARS-CoV-2 natural antibody response persists up to 12 months in a nationwide study from the Faroe Islands 93%
Similar papers in this journal
- The Nicaraguan Pediatric Influenza Cohort Study, 2011-2019: influenza incidence, seasonality, and transmission 95%
- Effectiveness of Influenza Vaccines in the HIVE household cohort over 8 years: is there evidence of indirect protection? 95%
- Immunogenicity of a third dose of BNT162b2 to ancestral SARS-CoV-2 & Omicron variant in adults who received two doses of inactivated vaccine 94%
Similar papers in this journal
- Risk assessment of 2024 cattle H5N1 using age-stratified serosurveillance data 94%
- Pigs are highly susceptible to but do not transmit mink-derived highly pathogenic avian influenza virus H5N1 clade 2.3.4.4b 93%
- Rapid Surge of Reassortant A(H1N1) Influenza Viruses in Danish Swine and their Zoonotic Potential 92%
Similar papers in this journal
- Strength and durability of antibody responses to BNT162b2 and CoronaVac 94%
- Impact of serum versus anticoagulant-containing plasma on influenza virusneuraminidase-based serological assays 94%
- Receipt of COVID-19 and seasonal influenza vaccines in California (USA) during the 2021-2022 influenza season 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.