MS02 - IMMU-2
Griffin West Ballroom (#2133) in The Ohio Union

Within-host SARS-CoV-2 viral and immune dynamics

Monday, July 17 at 04:00pm

SMB2023 SMB2023 Follow Monday during the "MS02" time block.
Room assignment: Griffin West Ballroom (#2133) in The Ohio Union.
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Esteban A. Hernandez-Vargas, Hana Dobrovolny


The COVID-19 pandemic has received significant attention from the mathematical biology community to understand the transmission of the virus at the populational level. However, too little has been dedicated to the virus's host interactions and the host's immune system. Mathematical immunology offers qualitative and quantitative analyses of various immunological processes across many scales and in multiple settings. Mathematical models at the within-host level are central to understanding the dynamics, organization, and control of the immune system in patients with COVID-19. Modeling the interactions between SARS-CoV-2 and the immune system and the differences in disease severity will be the focus of discussion at this mini-symposium on computational models. The ground-breaking goal will be to bring experts to develop and maturate a within-host modeling approach as a new paradigm for a better preparedness against COVID-19. The mini-symposium is composed of 11 speakers. We suggest dividing PART I and II. We give the flexibility to the organizing committee to decide if they split the sessions or keep a long-running session.

Esteban Abelardo Hernandez-Vargas

University of Idaho (Department of Mathematics and Statistical Science)
"The shape of antibody dynamics of severe and non-severe patients with COVID-19: A mathematical modeling approach"
The COVID-19 pandemic is a significant public health threat with unanswered questions regarding the immune system's role in the disease's severity level. In this paper, based on antibody kinetic data of patients with different disease severity, topological data analysis by the mapper algorithm highlights apparent differences in the shape of antibody dynamics between three groups of patients, which were non-severe, severe, and one intermediate case of severity. Subsequently, different mathematical models were developed to quantify the dynamics between the different severity groups. The best model was the one with the lowest median value of the Akaike Information Criterion for all groups of patients. Although high IgG level has been reported in severe patients, our findings suggest that IgG antibodies in severe patients may be less effective (affinity) than in non-severe patients due to early B cell production and early activation of the seroconversion process from IgM to IgG antibody. A bifurcation associated with a stable virus-positive steady state suggests that a sufficiently rapid viral replication can overcome the T cell response to cause the infection. Our work contributes to the in-host modeling of COVID-19 (and future related diseases), which can lead to effective treatments and an understanding of the disease from a systems perspective.
Additional authors: Alexis E.A. Almocera; Rodolfo Blanco; Fernanda Ordonez-Jimenez; Gustavo Chinney Herrera

Veronika I. Zarnitsyna

Emory University School of Medicine (Microbiology and Immunology)
"Competing Heterogeneities in Vaccine Effectiveness Estimation"
According to epidemiological data, protection from the flu and COVID-19 vaccines could wane within a year. Accurately measuring this fast waning of vaccine effectiveness (VE) is crucial for protecting public health, guiding vaccine development, and informing individual health decisions. Population heterogeneities in underlying susceptibility to infection and vaccine response pose an additional challenge in VE estimation, as they can cause measured VE to change over time, even without pathogen evolution or actual waning of immune responses. VE studies often rely on time-to-infection data and the Cox proportional hazards model. An extension of the Cox proportional hazards model, which utilized scaled Schoenfeld residuals, is commonly used to capture VE waning. We found that this approach is unreliable in capturing both the degree of fast waning and its functional form, especially when vaccination is spread over months, and identified the mathematical factors contributing to this unreliability (Nikas et al., Clinical Infectious Diseases, 2022). We showed that a relatively simple method based on including time-vaccine interaction in the model, with further proposed optimization, performs significantly better. Using this method, we explored the effect of the competing heterogeneities on the estimation of VE waning by analyzing the synthetic data from a multi-scale agent-based model parameterized with epidemiological and immunological data.
Additional authors: Ariel Nikas, Department of Microbiology and Immunology, Emory University School of Medicine; Hasan Ahmed, Department of Biology, Emory University

Hana Maria Dobrovolny

Texas Christian University (Department of Physics & Astronomy)
"Virus-mediated cell fusion of SARS-CoV-2 variants of concern"
Many viruses, including SARS-CoV-2, have the ability to cause neighboring cells to fuse into multi-nucleated cells called syncytia. Much is still unknown about how syncytia affect the course of viral infection. Using data from a recent study of virus-mediated cell fusion for different SARS-CoV-2 variants of concern, we use mathematical modeling to estimate the syncytia formation rate and the fusing time of SARS-CoV-2 variants. We find that the alpha variant has a syncytia formation rate higher than other variants. We are also able to estimate the time it takes for fusion to occur, finding that the beta variant takes the longest, followed by the alpha variant, with the delta and original Wuhan strains fusing fastest. This study exemplifies the role that mathematical models can play in helping to quantify the biological characteristics of different viruses.
Additional authors: Ava Amidei, Department of Chemistry & Biochemistry, Texas Christian University

Suzan Farhang-Sardroodi

University of Manitoba (Department of Mathematics)
"Mathematical modelling of the humoral and B cell response to SARS-CoV-2"
Mechanistic modelling approaches have become integral to systems biology to describe known physiology and fill in the gaps in our understanding of which complex interactions drive host-pathogen responses. They, therefore, provide valuable insights for public health planning and infectious disease control. In this mini-symposium, I will present our work on developing a mathematical model to study humoral (antibody-mediated) immunity. B cells and their antibodies are critical to protecting against COVID-19 over time. However, it is increasingly evident that waning antibodies after natural infection or vaccination translate to reduced defence against repeated SARS-CoV-2 infections. To understand the dynamics of antibody production from B cells, we constructed a computational biology model describing B cells and IgG-neutralizing antibodies coupled with host-pathogen interactions. This model provides better insight into the kinetic processes and mechanisms driving the humoral response against SARS-CoV-2. Our model delineates the initiation of B cell responses through their differentiation to germinal center cells, long-lived plasma cells, and memory cells. It sheds light on how antibodies are produced in primary and secondary reactions.
Additional authors: Prof. Morgan Craig (Sainte-Justine University Hospital Research Centre and Department of Mathematics and Statistics, Université de Montréal), Prof. Stéphanie Portet (Department of Mathematics, University of Manitoba), Prof. Kang-Ling Liao (Department of Mathematics, University of Manitoba), Prof. Julien Arino (Department of Mathematics, University of Manitoba).

Ruian Ke

Los Alamos National Laboratory
"The relationship between SARS-CoV-2 viral load and infectiousness and quantifying the infectiousness heterogeneity"
The within-host viral kinetics of SARS-CoV-2 infection and how they relate to a person’s infectiousness are not well understood. This limits our ability to quantify the impact of interventions on viral transmission. Here, we develop viral dynamic models of SARS-CoV-2 infection and fit them to data to estimate key within-host parameters such as the infected cell half-life and the within-host reproductive number. We then develop a model linking viral load (VL) to infectiousness and show a person’s infectiousness increases sublinearly with VL and that the logarithm of the VL in the upper respiratory tract is a better surrogate of infectiousness than the VL itself. By fitting mechanistic models to a wide variety of datasets, we directly quantified heterogeneity in individual infectiousness. Significant person-to-person variation in infectious virus shedding suggests that individual-level heterogeneity in viral dynamics contributes to ‘superspreading’.

Jane Heffernan

York University (Mathematics & Statistics)
"Modelling COVID-19 infection and vaccination"
Immunity is gained after infection and vaccination, but can also wane over time. We have developed mathematical models of COVID-19 infection and vaccination to track the accumulation and decay of effective COVID-19 immunity in individuals. The results from our in-host models are then embedded into epidemiological models of COVID-19 immunity distributions. In this talk I will review our in-host models and discuss our modelling results associated with mild, moderate, and severe COVID-19 infection, and vaccination using Astrazeneca, Moderna, or Pfizer vaccines. An example of immunity distribution modelling for Ontario Canada will also be discussed.

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