Mechanistic Models of COVID-19: Insights into Disease Progression, Vaccines, and Therapeutics

Desikan, Rajat; Padmanabhan, Pranesh; Kierzek, Andrzej; Graaf, Piet H. van der

doi:10.1016/j.ijantimicag.2022.106606

Cited by 18 publications

(12 citation statements)

References 62 publications

(54 reference statements)

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“…Several mathematical models of within-host SAR-CoV-2 dynamics have been developed and have offered valuable insights [72,73]. For instance, they have helped estimate the withinhost basic reproductive ratio [60,74,75] and assess the effects of drugs and vaccines [26,43,[76][77][78][79][80].…”

Section: Discussionmentioning

confidence: 99%

Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms

2022

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SARS-CoV-2 infection results in highly heterogeneous outcomes, from cure without symptoms to acute respiratory distress and death. Empirical evidence points to the prominent roles of innate immune and CD8 T-cell responses in determining the outcomes. However, how these immune arms act in concert to elicit the outcomes remains unclear. Here, we developed a mathematical model of within-host SARS-CoV-2 infection that incorporates the essential features of the innate immune and CD8 T-cell responses. Remarkably, by varying the strengths and timings of the two immune arms, the model recapitulated the entire spectrum of outcomes realized. Furthermore, model predictions offered plausible explanations of several confounding clinical observations, including the occurrence of multiple peaks in viral load, viral recrudescence after symptom loss, and prolonged viral positivity. We applied the model to analyze published datasets of longitudinal viral load measurements from patients exhibiting diverse outcomes. The model provided excellent fits to the data. The best-fit parameter estimates indicated a nearly 80-fold stronger innate immune response and an over 200-fold more sensitive CD8 T-cell response in patients with mild compared to severe infection. These estimates provide quantitative insights into the likely origins of the dramatic inter-patient variability in the outcomes of SARS-CoV-2 infection. The insights have implications for interventions aimed at preventing severe disease and for understanding the differences between viral variants.

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Section: Discussionmentioning

confidence: 99%

Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms

2022

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“…Quantifying the changes in the preference of the Omicron variant for different entry routes may help evaluate the efficacies of entry inhibitors in clinical development and optimise entry inhibitor-based treatments (Gunst et al, 2021; Zhuravel et al, 2021). Mathematical models of SARS-CoV-2 kinetics have provided valuable insights into COVID-19 disease progression, drug action, and the effectiveness of vaccines and treatments (Amidei and Dobrovolny, 2022; Chatterjee et al, 2022; Desikan et al, 2021; Garg et al, 2021; Gonçalves et al, 2020; Goyal et al, 2020; Ke et al, 2021; Kissler et al, 2021; Néant et al, 2021; Padmanabhan et al, 2022; Perelson and Ke, 2020). Here, adapting a previously developed mathematical model of SARS-CoV-2 entry (Padmanabhan et al, 2020) to the analysis of in vitro data on variants (Garcia-Beltran et al, 2021; Hoffmann and et al, 2021), we quantified the altered usage of host proteases required for entry by the Omicron variant relative to the original strain and the Delta variant.…”

Section: Discussionmentioning

confidence: 99%

Modelling how the altered usage of cell entry pathways by the SARS-CoV-2 Omicron variant may affect the efficacy and synergy of TMPRSS2 and Cathepsin B/L inhibitors

Padmanabhan

Dixit

2022

Preprint

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The SARS-CoV-2 Omicron variant appears to exhibit altered cell tropism and entry properties compared to other variants. Here, analysing recent data, we found a strong positive correlation between the entry efficiency of the Omicron variant and the relative usage of the host protease Cathepsin B/L by the original SARS-CoV-2 strain for cell entry. We developed a mathematical model to quantify entry efficiency in in vitro assays and found that the Omicron variant displayed >4-fold improved efficiency in using Cathepsin B/L for entry in 293T-ACE2 cells compared to the original strain. The preferential usage of Cathepsin B/L over TMPRSS2 for entry may explain the altered cell tropism of the Omicron variant and have implications for our understanding of its infectivity and transmissibility as well as for interventions.

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“…Mechanistic models have been extensively applied to characterize various epidemiological processes (e.g., transmission, recovery, vaccination, disease-induced death, etc. ), parameterize these critical processes, and provide projections of disease dynamics [ 10 , 11 ]. In particular, mechanistic compartmental models are usually expressed as an array of coupled ordinary differential equations (ODEs), Partial differential equations (PDEs), or stochastic differential equations (SDEs) of various processes [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

Hatami

Chen

Paul

et al. 2022

IJERPH

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The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte–Concord–Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model’s predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.

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Mechanistic Models of COVID-19: Insights into Disease Progression, Vaccines, and Therapeutics

Cited by 18 publications

References 62 publications

Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms

Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms

Modelling how the altered usage of cell entry pathways by the SARS-CoV-2 Omicron variant may affect the efficacy and synergy of TMPRSS2 and Cathepsin B/L inhibitors

Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

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