Reconstructing antibody dynamics to estimate the risk of influenza virus infection

serojump: A Bayesian tool for inferring infection timing and antibody kinetics from longitudinal serological data

Understanding acute infectious disease dynamics at individual and population levels is critical for informing public health preparedness and response. Serological assays, which measure a range of biomarkers relating to humoral immunity, can provide a valuable window into immune responses generated by past infections and vaccinations. However, traditional methods for interpreting serological data, such as binary seropositivity and seroconversion thresholds, often rely on heuristics that fail to account for individual variability in antibody kinetics and timing of infection, potentially leading to biased estimates of infection rates and post-exposure immune responses. To address these limitations, we developed serojump , a novel probabilistic framework and software package that uses individual-level serological data to infer infection status, timing, and subsequent antibody kinetics. We validated serojump using simulated serological data and real-world SARS-CoV-2 datasets from The Gambia. In simulation studies, the model accurately recovered individual infection status, population-level antibody kinetics, and the relationship between biomarkers and immunity against infection, demonstrating robustness under observational noise. Benchmarking against standard serological heuristics in real-world data revealed that serojump achieves higher sensitivity in identifying infections, outperforming static threshold-based methods and precision in inferred infection timing. Application of serojump to longitudinal SARS-CoV-2 serological data taken during the Delta wave provided additional insights into i) missed infections based on sub-threshold rises in antibody level and ii) antibody responses to multiple biomarkers post-vaccination and infection. Our findings highlight the utility of serojump as a pathogen-agnostic, flexible tool for serological inference, enabling deeper insights into infection dynamics, immune responses, and correlates of protection. The open-source framework offers researchers a platform for extracting information from serological datasets, with potential applications across various infectious diseases and study designs.

Reconciling heterogeneous dengue virus infection risk estimates from different study designs

Uncovering rates at which susceptible individuals become infected with a pathogen, i.e., the force of infection (FOI), is essential for assessing transmission risk and reconstructing distribution of immunity in a population. For dengue, reconstructing exposure and susceptibility statuses from the measured FOI is of particular significance as prior exposure is a strong risk factor for severe disease. FOI can be measured via many study designs. Longitudinal serology is considered gold standard measurements, as they directly track the transition of seronegative individuals to seropositive due to incident infections (seroincidence). Cross-sectional serology can provide estimates of FOI by contrasting seroprevalence across ages. Age of reported cases can also be used to infer FOI. Agreement of these measurements, however, has not been assessed. Using 26 y of data from cohort studies and hospital-attended cases from Kamphaeng Phet province, Thailand, we found FOI estimates from the three sources to be highly inconsistent. Annual FOI estimates from seroincidence were 1.75 to 4.05 times higher than case-derived FOI. Seroprevalence-derived was moderately correlated with case-derived FOI (correlation coefficient = 0.47) with slightly lower estimates. Through extensive simulations and theoretical analysis, we show that incongruences between methods can result from failing to account for dengue antibody kinetics, assay noise, and heterogeneity in FOI across ages. Extending standard inference models to include these processes reconciled the FOI and susceptibility estimates. Our results highlight the importance of comparing inferences across multiple data types to uncover additional insights not attainable through a single data type/analysis.

Serodynamics: A primer and synthetic review of methods for epidemiological inference using serological data

We present a review and primer of methods to understand epidemiological dynamics and identify past exposures from serological data, referred to as serodynamics. We discuss processing and interpreting serological data prior to fitting serodynamical models, and review approaches for estimating epidemiological trends and past exposures, ranging from serocatalytic models applied to binary serostatus data, to more complex models incorporating quantitative antibody measurements and immunological understanding. Although these methods are seemingly disparate, we demonstrate how they are derived within a common mathematical framework. Finally, we discuss key areas for methodological development to improve scientific discovery and public health insights in seroepidemiology.

Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course

Humans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual's antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology in humans remains unclear. Here, we used a multilevel mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns in humans and to investigate how influenza incidence varies over time, space, and age in this population. We estimated median annual influenza infection rates to be approximately 19% from 1968 to 2015, but with substantial variation between years; 88% of individuals were estimated to have been infected at least once during the study period (2009 to 2015), and 20% were estimated to have 3 or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual's antibody profile. Finally, we reconstructed each individual's expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long-term epidemiological trends, within-host processes, and immunity when analysed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.

Reconciling heterogeneous dengue virus infection risk estimates from different study designs

Uncovering rates at which susceptible individuals become infected with a pathogen, i.e. the force of infection (FOI), is essential for assessing transmission risk and reconstructing distribution of immunity in a population. For dengue, reconstructing exposure and susceptibility statuses from the measured FOI is of particular significance as prior exposure is a strong risk factor for severe disease. FOI can be measured via many study designs. Longitudinal serology are considered gold standard measurements, as they directly track the transition of seronegative individuals to seropositive due to incident infections (seroincidence). Cross-sectional serology can provide estimates of FOI by contrasting seroprevalence across ages. Age of reported cases can also be used to infer FOI. Agreement of these measurements, however, have not been assessed. Using 26 years of data from cohort studies and hospital-attended cases from Kamphaeng Phet province, Thailand, we found FOI estimates from the three sources to be highly inconsistent. Annual FOI estimates from seroincidence was 2.46 to 4.33-times higher than case-derived FOI. Correlation between seroprevalence-derived and case-derived FOI was moderate (correlation coefficient=0.46) and no systematic bias. Through extensive simulations and theoretical analysis, we show that incongruences between methods can result from failing to account for dengue antibody kinetics, assay noise, and heterogeneity in FOI across ages. Extending standard inference models to include these processes reconciled the FOI and susceptibility estimates. Our results highlight the importance of comparing inferences across multiple data types to uncover additional insights not attainable through a single data type/analysis.

Incidence of laboratory-confirmed influenza and RSV and associated presenteeism and absenteeism among healthcare personnel, Israel, influenza seasons 2016 to 2019

BackgroundHealthcare personnel (HCP) are at high risk for respiratory infections through occupational exposure to respiratory viruses.AimWe used data from a prospective influenza vaccine effectiveness study in HCP to quantify the incidence of acute respiratory infections (ARI) and their associated presenteeism and absenteeism.MethodsAt the start and end of each season, HCP at two Israeli hospitals provided serum to screen for antibodies to influenza virus using the haemagglutination inhibition assay. During the season, active monitoring for the development of ARI symptoms was conducted twice a week by RT-PCR testing of nasal swabs for influenza and respiratory syncytial virus (RSV). Workplace presenteeism and absenteeism were documented. We calculated incidences of influenza- and RSV-associated ARI and applied sampling weights to make estimates representative of the source population.ResultsThe median age of 2,505 participating HCP was 41 years, and 70% were female. Incidence was 9.1 per 100 person-seasons (95% CI: 5.8-14.2) for RT-PCR-confirmed influenza and 2.5 per 100 person-seasons (95% CI: 0.9-7.1) for RSV illness. Each season, 18-23% of unvaccinated and influenza-negative HCP seroconverted. The incidence of seroconversion or RT-PCR-confirmed influenza was 27.5 per 100 person-seasons (95% CI: 17.8-42.5). Work during illness occurred in 92% (95% CI: 91-93) of ARI episodes, absence from work in 38% (95% CI: 36-40).ConclusionInfluenza virus and RSV infections and associated presenteeism and absenteeism were common among HCP. Improving vaccination uptake among HCP, infection control, and encouraging sick HCP to stay home are important strategies to reduce ARI incidence and decrease the risk of in-hospital transmission.

Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course

Humans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual's antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology remains unclear. Here, we used a multi-level mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns and to investigate how influenza incidence varies over time, space and age in this population. We estimated median annual influenza infection rates to be approximately 18% from 1968 to 2015, but with substantial variation between years. 88% of individuals were estimated to have been infected at least once during the study period (2009-2015), and 20% were estimated to have three or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual's antibody profile. Finally, we reconstructed each individual's expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long term, epidemiological trends, within-host processes and immunity when analyzed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.

A Dynamic Model for Estimating the Retention Duration of Neutralizing Antibody Titers After Vaccination in a COVID-19 Convalescent Population

We measured neutralizing antibodies (nAbs) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a cohort of 235 convalescent patients (representing 384 analytic samples). They were followed for up to 588 days after the first report of onset in Taiwan. A proposed Bayesian approach was used to estimate nAb dynamics in patients postvaccination. This model revealed that the titer reached its peak (1819.70 IU/mL) by 161 days postvaccination and decreased to 154.18 IU/mL by day 360. Thus, the nAb titers declined in 6 months after vaccination. Protection, against variant B.1.1.529 (ie, Omicron) may only occur during the peak period.

Determinants of epidemic size and the impacts of lulls in seasonal influenza virus circulation

During the COVID-19 pandemic, levels of seasonal influenza virus circulation were unprecedentedly low, leading to concerns that a lack of exposure to influenza viruses, combined with waning antibody titres, could result in larger and/or more severe post-pandemic seasonal influenza epidemics. However, in most countries the first post-pandemic influenza season was not unusually large and/or severe. Here, based on an analysis of historical influenza virus epidemic patterns from 2002 to 2019, we show that historic lulls in influenza virus circulation had relatively minor impacts on subsequent epidemic size and that epidemic size was more substantially impacted by season-specific effects unrelated to the magnitude of circulation in prior seasons. From measurements of antibody levels from serum samples collected each year from 2017 to 2021, we show that the rate of waning of antibody titres against influenza virus during the pandemic was smaller than assumed in predictive models. Taken together, these results partially explain why the re-emergence of seasonal influenza virus epidemics was less dramatic than anticipated and suggest that influenza virus epidemic dynamics are not currently amenable to multi-season prediction.

Co-evolution of immunity and seasonal influenza viruses

Seasonal influenza viruses cause recurring global epidemics by continually evolving to escape host immunity. The viral constraints and host immune responses that limit and drive the evolution of these viruses are increasingly well understood. However, it remains unclear how most of these advances improve the capacity to reduce the impact of seasonal influenza viruses on human health. In this Review, we synthesize recent progress made in understanding the interplay between the evolution of immunity induced by previous infections or vaccination and the evolution of seasonal influenza viruses driven by the heterogeneous accumulation of antibody-mediated immunity in humans. We discuss the functional constraints that limit the evolution of the viruses, the within-host evolutionary processes that drive the emergence of new virus variants, as well as current and prospective options for influenza virus control, including the viral and immunological barriers that must be overcome to improve the effectiveness of vaccines and antiviral drugs.

serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics processes

serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals' antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.

Household Transmission of Influenza A Viruses in 2021-2022

IMPORTANCE

Influenza virus infections declined globally during the COVID-19 pandemic. Loss of natural immunity from lower rates of influenza infection and documented antigenic changes in circulating viruses may have resulted in increased susceptibility to influenza virus infection during the 2021-2022 influenza season.

OBJECTIVE

To compare the risk of influenza virus infection among household contacts of patients with influenza during the 2021-2022 influenza season with risk of influenza virus infection among household contacts during influenza seasons before the COVID-19 pandemic in the US.

DESIGN, SETTING, AND PARTICIPANTS

This prospective study of influenza transmission enrolled households in 2 states before the COVID-19 pandemic (2017-2020) and in 4 US states during the 2021-2022 influenza season. Primary cases were individuals with the earliest laboratory-confirmed influenza A(H3N2) virus infection in a household. Household contacts were people living with the primary cases who self-collected nasal swabs daily for influenza molecular testing and completed symptom diaries daily for 5 to 10 days after enrollment.

EXPOSURES

Household contacts living with a primary case.

MAIN OUTCOMES AND MEASURES

Relative risk of laboratory-confirmed influenza A(H3N2) virus infection in household contacts during the 2021-2022 season compared with prepandemic seasons. Risk estimates were adjusted for age, vaccination status, frequency of interaction with the primary case, and household density. Subgroup analyses by age, vaccination status, and frequency of interaction with the primary case were also conducted.

RESULTS

During the prepandemic seasons, 152 primary cases (median age, 13 years; 3.9% Black; 52.0% female) and 353 household contacts (median age, 33 years; 2.8% Black; 54.1% female) were included and during the 2021-2022 influenza season, 84 primary cases (median age, 10 years; 13.1% Black; 52.4% female) and 186 household contacts (median age, 28.5 years; 14.0% Black; 63.4% female) were included in the analysis. During the prepandemic influenza seasons, 20.1% (71/353) of household contacts were infected with influenza A(H3N2) viruses compared with 50.0% (93/186) of household contacts in 2021-2022. The adjusted relative risk of A(H3N2) virus infection in 2021-2022 was 2.31 (95% CI, 1.86-2.86) compared with prepandemic seasons.

CONCLUSIONS AND RELEVANCE

Among cohorts in 5 US states, there was a significantly increased risk of household transmission of influenza A(H3N2) in 2021-2022 compared with prepandemic seasons. Additional research is needed to understand reasons for this association.

Potential impacts of prolonged absence of influenza virus circulation on subsequent epidemics

Background

During the first two years of the COVID-19 pandemic, the circulation of seasonal influenza viruses was unprecedentedly low. This led to concerns that the lack of immune stimulation to influenza viruses combined with waning antibody titres could lead to increased susceptibility to influenza in subsequent seasons, resulting in larger and more severe epidemics.

Methods

We analyzed historical influenza virus epidemiological data from 2003-2019 to assess the historical frequency of near-absence of seasonal influenza virus circulation and its impact on the size and severity of subsequent epidemics. Additionally, we measured haemagglutination inhibition-based antibody titres against seasonal influenza viruses using longitudinal serum samples from 165 healthy adults, collected before and during the COVID-19 pandemic, and estimated how antibody titres against seasonal influenza waned during the first two years of the pandemic.

Findings

Low country-level prevalence of influenza virus (sub)types over one or more years occurred frequently before the COVID-19 pandemic and had relatively small impacts on subsequent epidemic size and severity. Additionally, antibody titres against seasonal influenza viruses waned negligibly during the first two years of the pandemic.

Interpretation

The commonly held notion that lulls in influenza virus circulation, as observed during the COVID-19 pandemic, will lead to larger and/or more severe subsequent epidemics might not be fully warranted, and it is likely that post-lull seasons will be similar in size and severity to pre-lull seasons.

Funding

European Research Council, Netherlands Organization for Scientific Research, Royal Dutch Academy of Sciences, Public Health Service of Amsterdam.

Research in context

Evidence before this study: During the first years of the COVID-19 pandemic, the incidence of seasonal influenza was unusually low, leading to widespread concerns of exceptionally large and/or severe influenza epidemics in the coming years. We searched PubMed and Google Scholar using a combination of search terms (i.e., "seasonal influenza", "SARS-CoV-2", "COVID-19", "low incidence", "waning rates", "immune protection") and critically considered published articles and preprints that studied or reviewed the low incidence of seasonal influenza viruses since the start of the COVID-19 pandemic and its potential impact on future seasonal influenza epidemics. We found a substantial body of work describing how influenza virus circulation was reduced during the COVID-19 pandemic, and a number of studies projecting the size of future epidemics, each positing that post-pandemic epidemics are likely to be larger than those observed pre-pandemic. However, it remains unclear to what extent the assumed relationship between accumulated susceptibility and subsequent epidemic size holds, and it remains unknown to what extent antibody levels have waned during the COVID-19 pandemic. Both are potentially crucial for accurate prediction of post-pandemic epidemic sizes.Added value of this study: We find that the relationship between epidemic size and severity and the magnitude of circulation in the preceding season(s) is decidedly more complex than assumed, with the magnitude of influenza circulation in preceding seasons having only limited effects on subsequent epidemic size and severity. Rather, epidemic size and severity are dominated by season-specific effects unrelated to the magnitude of circulation in the preceding season(s). Similarly, we find that antibody levels waned only modestly during the COVID-19 pandemic.Implications of all the available evidence: The lack of changes observed in the patterns of measured antibody titres against seasonal influenza viruses in adults and nearly two decades of epidemiological data suggest that post-pandemic epidemic sizes will likely be similar to those observed pre-pandemic, and challenge the commonly held notion that the widespread concern that the near-absence of seasonal influenza virus circulation during the COVID-19 pandemic, or potential future lulls, are likely to result in larger influenza epidemics in subsequent years.