A hybrid discrete-continuum model of immune responses to SARS-CoV-2 infection in the lung alveolar region, with a focus on interferon induced innate response

Aristotelous, Andreas C.; Chen, Alex; Forest, M. Gregory

doi:10.1016/j.jtbi.2022.111293

Cited by 6 publications

(6 citation statements)

References 52 publications

(63 reference statements)

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“…(iv) The model in ( Moses et al, 2021 ) resolves spatially distributed respiratory tract infection in the presence of immune protection. While the results presented in the present paper are absent of immune protection, recent publications explore antibody protection ( Chen et al, 2022 ) using the agent-based model in ( Chen et al, 2022 ) and this paper, and innate immune protection ( Aristotelous et al, 2022 ) in the lung parenchyma using a hybrid continuum-stochastic model.…”

Section: Methods and Prior Resultsmentioning

confidence: 91%

“…Our model predictions should therefore be viewed as estimates of upper bounds on absolute shed viral load and infected cells over the 0-48 hours post infection, most faithful to individuals without antibody and innate immune protection on this timescale. In separate studies, we have coupled antibody protection in the respiratory tract ( Chen et al, 2022 ) and innate immune protection from interferon signaling in the alveolar ducts and sacs ( Aristotelous et al, 2022 ), which in future studies will be integrated with the parameter sensitivity analysis presented here.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling identifies variability in SARS-CoV-2 uptake and eclipse phase by infected cells as principal drivers of extreme variability in nasal viral load in the 48 h post infection

Pearson

Wessler

Chen

et al. 2023

Journal of Theoretical Biology

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Section: Methods and Prior Resultsmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Modeling identifies variability in SARS-CoV-2 uptake and eclipse phase by infected cells as principal drivers of extreme variability in nasal viral load in the 48 h post infection

Pearson

Wessler

Chen

et al. 2023

Journal of Theoretical Biology

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“…This experimental data, coupled with the physiology of the individual, are predictive of pre-immune response in the immediate 48 h post initial nasal cell infection. Should features not incorporated into our modeling platform be shown to have a leading order effect, we are poised to incorporate those features, similar to how we have extended our pre-immune modeling platform to both innate [ 11 ] and adaptive [ 12 ] immunity.…”

Section: Discussionmentioning

confidence: 99%

“…Below, we summarize the mathematical structure of this model. Additional extensions of the model to include innate [ 11 ] and adaptive [ 12 ] immunity have been developed, but they are not included in this study motivated by the overwhelming evidence of immune escape over the 48 h or longer post-infection period [ 9 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], independent of vaccination status and prior infection.…”

Section: Introductionmentioning

confidence: 99%

Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data

Zhang,

Cao,

Medlin

et al. 2023

Viruses

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Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each “virtual individual”, which we define as each fixed set of model parameters (1) and (2) above. The “virtual population” and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated “virtual population database” of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus–cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h.

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“…The parameters of the model implemented in [10] used mean population properties of human RT physiology (e.g., for each branch generation in the upper and lower RT, branch length, mucus thickness, and mucociliary advection velocity of the mucus layer) together with the best-known clinical and experimental parameter estimates of the kinetic processes for SARS-CoV-2 virions. The alveolar ducts and sacs of the deep lung were modeled as well in [11], but those details are not relevant for the present focus on nasal infections.…”

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis of the Onset of Nasal Passage Infection by SARS-CoV-2 With Respect to Heterogeneity in Host Physiology and Host Cell-Virus Kinetic Interactions

Zhang,

Cao,

Medlin

et al. 2023

Preprint

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Throughout the COVID-19 pandemic, positive nasal swab tests have revealed dramatic population heterogeneity in viral titers spanning 6 orders-of-magnitude. Our goal here is to probe potential drivers of infection outcome sensitivity arising from (i) physiological heterogeneity between hosts and (ii) host-variant heterogeneity in the detailed kinetics of cell infection and viral replication. Toward this goal, we apply global sensitivity methods (Partial Rank Correlation Coefficient analysis and Latin Hypercube Sampling) to a physiologically faithful, stochastic, spatial model of inhaled SARS-CoV-2 exposure and infection in the human respiratory tract. We focus on the nasal passage as the primary origin of respiratory infection and site of clinical testing, and we simulate the spatial and dynamic progression of shed viral load and infected cells in the immediate 48 hours post infection. We impose immune evasion, i.e., suppressed immune protection, based on the preponderance of clinical evidence that nasal infections occur rapidly post exposure, largely independent of immune status. Global sensitivity methods provide the de-correlated outcome sensitivities to each source of within-host heterogeneity, including the dynamic progression of sensitivities at 12, 24, 36, and 48 hours post infection. The results reveal a dynamic rank-ordering of the drivers of outcome sensitivity in early infection, providing insights into the dramatic population-scale outcome diversity during the COVID-19 pandemic. While we focus on SARS-CoV-2, the model and methods are applicable to any inhaled virus in the immediate 48 hours post infection.

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A hybrid discrete-continuum model of immune responses to SARS-CoV-2 infection in the lung alveolar region, with a focus on interferon induced innate response

Cited by 6 publications

References 52 publications

Modeling identifies variability in SARS-CoV-2 uptake and eclipse phase by infected cells as principal drivers of extreme variability in nasal viral load in the 48 h post infection

Modeling identifies variability in SARS-CoV-2 uptake and eclipse phase by infected cells as principal drivers of extreme variability in nasal viral load in the 48 h post infection

Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data

Global Sensitivity Analysis of the Onset of Nasal Passage Infection by SARS-CoV-2 With Respect to Heterogeneity in Host Physiology and Host Cell-Virus Kinetic Interactions

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