Older adults tend to exhibit weaker vaccine-elicited responses to mRNA COVID-19 immunization than younger people. This is a public health concern, as older individuals are more likely to experience severe COVID-19. To better understand the mechanisms of this age-related disparity, we developed a mathematical model of the post-vaccination humoral immune response. Through calibration to clinical data from 32 healthcare workers (HCWs) and 27 seniors who received the primary vaccine series (two priming doses and one booster), our model predicted that repeated vaccinations consistently enhanced antibody responses in both groups. While seniors were estimated to experience an accelerated decay in T follicular helper cells compared to HCWs, a larger booster dose effectively compensated for this weakened antibody response. Furthermore, we linked antibody and neutralization levels and used this relationship to predict post-vaccination neutralization, thus serving as a proxy for vaccine efficacy. By studying various combinations of mixed doses sizes in the primary vaccination series, our model predicted that administering a full-dose booster significantly enhances immunization outcomes, irrespective of the initial vaccine dose size. Further, a biannual half booster strategy was found to be more effective than one with an annual full booster, especially for seniors. Overall, our findings highlight the importance of tailoring vaccination strategies to different age groups to provide robust and long-lasting immunity against SARS-CoV-2 infections. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project was supported by funding from the Government of Canada, through the COVID-19 Immunity Task Force, the OMNI-REUNIS Emerging Infectious Disease Modelling Network supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Public Health Agency of Canada, the Fondation du CHU Sainte-Justine, NSERC Discovery Grant RGPIN-2018-04546, and the Canada Research Chair in Computational Immunology. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: In this study, we used data describing SARS-CoV-2 specific immune response from a cohort of COVID-19 vaccinees, including HCWs and seniors (aged 70 years and older) in British Columbia, Canada [13] I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data requests should be directed to the COVID-19 Immunity Taskforce Databank, available at https://www.covid19immunitytaskforce.ca/citf-databank/. All code for reproducing figures in the this study is available on Github: https://github.com/Craig-Lab/COVID19Vaccine.git.

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in millions of deaths globally. Adults with immunosuppression (e.g., solid organ transplant recipients) and those undergoing active cancer treatments experience worse infections and more severe COVID-19. It is difficult to conduct clinical studies in these populations, resulting in a restricted amount of data that can be used to relate mechanisms of immune dysfunction to COVID-19 outcomes in these vulnerable groups. To study immune dynamics after infection with SARS-CoV-2 and to investigate drivers of COVID-19 severity in individuals with cancer and immunosuppression, we adapted our mathematical model of the immune response during COVID-19 and generated virtual patient cohorts of cancer and immunosuppressed patients. The cohorts of plausible patients recapitulated available longitudinal clinical data collected from patients in Montréal, Canada area hospitals. Our model predicted that both cancer and immunosuppressed virtual patients with severe COVID-19 had decreased CD8+ T cells, elevated interleukin-6 concentrations, and delayed type I interferon peaks compared to those with mild COVID-19 outcomes. Additionally, our results suggest that cancer patients experience higher viral loads (however, with no direct relation with severity), likely because of decreased initial neutrophil counts (i.e., neutropenia), a frequent toxic side effect of anti-cancer therapy. Furthermore, severe cancer and immunosuppressed virtual patients suffered a high degree of tissue damage associated with elevated neutrophils. Lastly, parameter values associated with monocyte recruitment by infected cells were found to be elevated in severe cancer and immunosuppressed patients with respect to the COVID-19 reference group. Together, our study highlights that dysfunction in type I interferon and CD8+ T cells are key drivers of immune dysregulation in COVID-19, particularly in cancer patients and immunosuppressed individuals.

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Objectives. Identifying biomarkers causing differential SARS-CoV-2 infection kinetics associated with severe COVID-19 is fundamental for effective diagnostics and therapeutic planning. Methods. In this work, we applied mathematical modelling to investigate the relationships between patient characteristics, plasma SARS-CoV-2 RNA dynamics and COVID-19 severity. Using a straightforward mathematical model of within-host viral kinetics, we estimated key model parameters from serial plasma viral RNA (vRNA) samples from 256 hospitalised COVID-19(+) patients. Results. Our model predicted that clearance rates distinguish key differences in plasma vRNA kinetics and severe COVID-19. Moreover, our analyses revealed a strong correlation between plasma vRNA kinetics and plasma receptor for advanced glycation end products (RAGE) concentrations (a plasma biomarker of lung damage), collected in parallel to plasma vRNA from patients in our cohort, suggesting that RAGE can substitute for viral plasma shedding dynamics to prospectively classify seriously ill patients. Conclusion. Overall, our study identifies factors of COVID-19 severity, supports interventions to accelerate viral clearance and underlines the importance of mathematical modelling to better understand COVID-19.

B cells and antibodies are crucial in protecting against infections like SARS-CoV-2. However, antibody levels decline after infection or vaccination, reducing defences against future SARS-CoV-2 infections. To understand antibody production and decline, we developed a mathematical model that predicts germinal center B cell, long-lived plasma cell, memory B cell, and antibody dynamics. Our focus was on B cell activation and antibody generation following both primary and secondary SARS-CoV-2 infections. Aligning our model with clinical data, we adjusted antibody production rates for germinal center B cells and plasma B cells during primary and secondary infections. We also assessed antibody neutralization against Delta and Omicron variants post-primary and secondary exposure. Our findings showed reduced neutralization against Omicron due to its immune evasion. In primary and secondary exposures to Delta and Omicron, our predictions indicated enhanced antibody neutralization in the secondary response within a year of the primary response. We also explored waning immunity, demonstrating how B cell kinetics affect viral neutralization post-primary infection. This study enhances our understanding of humoral immunity to SARS-CoV-2 and can predict antibody dynamics post-infection or vaccination.

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.