MS09 - IMMU-01

New approaches to infectious disease immunity for model-informed vaccine development (Part 2)

Friday, July 18 at 4:00pm

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Note: this minisymposia has multiple sessions. The other session is: and Part-1.

Terry Easlick (Univeristé de Montréal/Centre de recherche Azrieli du CHU Sainte-Justine), Morgan Craig, Univeristé de Montréal/Centre de recherche Azrieli du CHU Sainte-Justine

Description:

This session will bring together leading researchers to discuss innovative mathematical modelling approaches for studying immune responses to infectious diseases for the establishment of robust vaccination strategies. The objective is to foster interdisciplinary dialogue, showcasing novel methods and their application to key areas such as antigen-specific responses, humoral and cell-mediated immunity, vaccine dose optimization, and addressing challenges posed by waning immunity and pathogen diversity. This minisymposium will present complementary approaches to studying within-host immune responses to infections and vaccines. Topics will include capturing population-level dynamics, accounting for biological variability using stochastic models, simulating cell-to-cell interactions using agent-based models (ABMs), and extracting complex patterns from large immunological datasets using machine learning techniques. In particular, we will highlight how individual-level diversity (i.e., sex, age, comorbidities, genetics, etc.) affect immune and vaccine responses. By bridging diverse perspectives and methodologies, this minisymposium will contribute to innovation in model-informed vaccine development by promoting cutting-edge approaches to mathematical immunology that advance our fundamental understanding of individual immunity to bring necessary improvements to the vaccine development pipeline.

Mélanie Prague

Université de Bordeaux/INRIA

"Mechanistic Model of initial and persisting antibody response following Ebola vaccination: application to the PREVAC trial."

Antibody response dynamics following Ebola vaccination remain incompletely understood, particularly regarding the continuum of initial induction to long-term persistence. This study developed a mechanistic model of B-cell stimulation post-vaccination able to infer antigen presentation kinetics and propose an identifiable model based solely on anti-ZEBOV IgG levels. This study was based on the PREVAC randomized placebo-controlled trial (NCT02876328), which enrolled healthy adults and children, from Sierra Leone, Mali, guinea and Liberia, to evaluate the safety and immune responses of three vaccine strategies: Ad26.ZEBOV followed by MVA-BN-Filo 56 days later (the Ad26-MVA group, 799 participants), rVSV∆G-ZEBOV-GP followed by placebo 56 days later (the rVSV group, 802 participants), and rVSV∆G-ZEBOV-GP followed by rVSV∆G-ZEBOV- GP 56 days later (the rVSV–booster group, 399 participants). Our model was modified from Clairon et al. (2022, PLOS Comp. Biol.), which assumes that antigen stimulates the differentiation of naive B cells into long-lived and short-lived antibody-secreting cells. The two groups were modeled following similar pipelines. For both vaccine groups, a bell-shaped curve best described the dynamics of antigen presentation according to model information criterion indicating a long antigen presentation regardless of replication competence of the vaccine. Longer presentation times were found for rVSV (half-life t1/2=45 days at first dose and t1/2=1 days at second dose) than for Ad26–MVA (t1/2=35 days for Ad26.ZEBOV and t1/2=7 days for MVA-BN-Filo). We as well quantified effect of sex, age and geography on humoral dynamics. This work provides a foundation for in silico simulations of vaccination clinical trials, with the objective of optimizing booster strategies to ensure the long-term maintenance of immunogenicity in target populations according to patients characteristics, revaccination timing and vaccination strategy.

Elizabeth Amona

Virginia Commonwealth University

"Studying Disease Reinfection Rates, Vaccine Efficacy and the Timing of Vaccine Rollout in the context of Infectious Diseases"

The COVID-19 pandemic has highlighted the intricate nature of disease dynamics, extending beyond transmission patterns to the complex interplay of intervention strategies. In the post-COVID-19 era, reinfection has emerged as a critical factor, shaping how we model disease progression, evaluate immunity, and assess the effectiveness of public health interventions. This research uniquely explores the varied efficacy of existing vaccines and the pivotal role of vaccination timing in the context of COVID-19. Departing from conventional modeling, we introduce two models that account for the impact of vaccines on infections, reinfections, and deaths. We estimate model parameters under the Bayesian framework, specifically utilizing the Metropolis-Hastings Sampler. The study conducts data-driven scenario analyses for the State of Qatar, quantifying the potential duration during which the healthcare system could have been overwhelmed by an influx of new COVID-19 cases surpassing available hospital beds. Additionally, the research explores similarities in predictive probability distributions of cumulative infections, reinfections, and deaths, employing the Hellinger distance metric. Comparative analysis, utilizing the Bayes factor, underscores the plausibility of a model assuming a different susceptibility rate to reinfection, as opposed to assuming the same susceptibility rate for both infections and reinfections. Results highlight the adverse outcomes associated with delayed vaccination, emphasizing the efficacy of early vaccination in reducing infections, reinfections, and deaths. Our research advocates prioritizing early vaccination as a key strategy in effectively combating future pandemics, thereby providing vital insights for evidence-based public health interventions.

Cailan Jeynes-Smith

University of Tennessee Health Science Centre

"Dissecting Cytokine Production: Integrating Subset-Specific Data into Immunological Models"

Understanding cytokine regulation and its impact on the cellular response to infection is challenging. Immunological models often rely on implicit assumptions about cytokine production, as direct quantification of cytokine-producing cell subsets is uncommon. However, only a fraction of cells may be actively producing cytokines, and their dynamics frequently diverge from those of the broader population. To address this, we developed a mechanistic model of IFN-γ production during influenza A virus infection, integrating cell abundance data with integrated median fluorescence intensity (iMFI) measurements. This framework allowed us to quantify the relative contributions and nonlinear regulation in addition to demonstrating the necessity of using the iMFI to define the balance between production and uptake to explain observed IFN-γ levels. Our findings highlight the importance of incorporating both cell subset data and functional intensity (iMFI) into cytokine modeling, enabling more accurate inference of production mechanisms and improved model predictions.

Jonah Hall

University of British Columbia/BC Children's Hospital Research Institute

"Optimization of Pertussis Immunization Using Mathematical Modeling"

Pertussis disease (whooping cough), caused by the bacteria Bordetella pertussis, is most severe in young infants, with the majority of deaths occurring among unvaccinated children aged <3 months. Pertussis vaccination is a safe and effective approach for prevention of pertussis, with the DTaP (priming) and TdaP (booster) vaccine series. TdaP is given during pregnancy and DTaP is given in the first year of childhood. The phenomenon of immunomodulation, however, is known to dampen the IgG response of infants born following pertussis vaccination during pregnancy. We hypothesize that the timing of the vaccination series, while not the immunological cause, could be modified so as to decrease the effect of immunomodulation and thus increase the efficacy of childhood pertussis vaccination. While attempting to empirically test many different schedules would be ineffective, using mathematical modeling to evaluate several schedules simultaneously could be extremely useful in determining a more effective schedule. We will use a combination of a mathematical model of pertussis vaccination and an experimental mouse model of pertussis vaccination to identify the optimal immunization schedule for pregnancy and infancy. We will immunize pregnant and infant mice, according to a murine analog of the conventional vaccination schedule, with TdaP (pregnant) and DTaP (infant) pertussis vaccine doses. These data will form an input to our working mathematical model. Using the data, we will estimate experimentally inaccessible parameters that govern the mechanisms of the immune system. Once parametrized, we will use the mathematical model to propose immunization schedules that maximize the infant antibody response. We will test our proposed schedules via a second mouse experiment, comparing the immune responses between the two experiments to evaluate the efficacy of the mathematically-optimized schedule. Possible mechanisms of immunomodulation can be evaluated in the mathematical model, using the data collected in both experiments to ensure model accuracy.

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Annual Meeting for the Society for Mathematical Biology, 2025.