Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: Parameter estimates, identifiability, sensitivity and the eclipse phase profile

Korosec, Chapin S.; Betti, Matthew; Dick, David W; Ooi, Hsu Kiang; Moyles, Iain; Wahl, Lindi M.; Heffernan, Jane M.

doi:10.1016/j.jtbi.2023.111449

Cited by 9 publications

(20 citation statements)

References 66 publications

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“…It is therefore possible that our eclipse mutation results (Fig. 3), with neutral strain properties [23] determined from SARS-CoV-2 data gathered at a similar time as the Shen et al [66] study, are revealing the high probability of such a mutation to emerge at this time of the pandemic, and this result further supports the importance of therapeutics that target the M protein [65].…”

Section: Discussionsupporting

confidence: 77%

“…This ongoing conflict between the host and pathogen creates an arms race, resulting in diverse outcomes that can either enhance or diminish virulence [33]. In this study, we used a stochastic model to explore the fate of neutral and advantageous SARS-CoV-2 mutations, where the wildtype strain was calibrated to SARS-CoV-2 within-host viral load data collected from February 2020 through to February 2021 in Berlin, Germany [21], with model parameters determined through previous work [23].…”

Section: Discussionmentioning

confidence: 99%

“…In this work, our within-host model is similar in structure to models of withinhost infection that have been used to quantify the dynamics of SARS-CoV-2 within an infected individual [20][21][22], and estimate viral fitness traits for a number of SARS-CoV-2 VOCs (i.e., attachment rate, budding rate) [20,[23][24][25][26] A schematic of our in-host model of SARS-CoV-2 infection with three eclipse stages (Eq S1) is shown in Fig 1A . A schematic of the stochastic model employed to determine transmission probabilities of rare mutations is shown in Fig 1B . Parameters for "wildtype" SARS-CoV-2 infection (see Section S1.2 and Discussion) are determined from the literature and are listed in Table S1.…”

Section: Methodsmentioning

confidence: 99%

“…We use the parameter estimates from ref. [23], fitted to within-host data from ref. [21], to define the wildtype (WT) strain in the deterministic model, as these parameters were determined to have the best identifiability and agreed well with previous literature.…”

Section: S12 Parameter Estimates and Initial Conditionsmentioning

confidence: 99%

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Within-host evolution of SARS-CoV-2: how often arede novomutations transmitted?

Korosec

Wahl

Heffernan

2023

Preprint

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Despite a relatively low mutation rate, the large number of SARS-CoV-2 infections has allowed for substantial genetic change, leading to a multitude of emerging variants. Using a recently determined mutation rate (per site replication), as well as parameter estimates for within-host SARS-CoV-2 infection, we apply a stochastic transmission-bottleneck model to describe the survival probability of de novo SARS-CoV-2 mutations. For narrow bottlenecks, we find mutations affecting per-target-cell attachment rate (with phenotypes associated with fusogenicity and ACE2 binding), have similar transmission probabilities to mutations affecting viral load clearance (with phenotypes associated with humoral evasion). We further find that mutations affecting the eclipse rate (with phenotypes associated with reorganization of cellular metabolic processes and synthesis of viral budding precursor material) are highly favoured relative to all other traits examined. We find mutations leading to reduced removal rates of infected cells (with phenotypes associated with innate immune evasion) have limited transmission advantage relative to mutations leading to humoral evasion. Predicted transmission probabilities, however, for mutations affecting innate immune evasion are more consistent with the range of clinically-estimated household transmission probabilities for de novo mutations. This result suggests that although mutations affecting humoral evasion are more easily transmitted when they occur, mutations affecting innate immune evasion may occur more readily. We examine our predictions in the context of a number of previously characterized mutations in circulating strains of SARS-CoV-2. Our work offers both a null model for SARS-CoV-2 substitution rates and predicts which aspects of viral life history are most likely to successfully evolve, despite low mutation rates and repeated transmission bottlenecks.

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Section: Discussionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: S12 Parameter Estimates and Initial Conditionsmentioning

confidence: 99%

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Within-host evolution of SARS-CoV-2: how often arede novomutations transmitted?

Korosec

Wahl

Heffernan

2023

Preprint

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“…In-host modelling has been extended to COVID-19 with studies looking at infection (cf. Hernandez-Vargas and Velasco-Hernandez (2020); Perelson and Ke (2021); Kim et al (2021); Néant et al (2021); Sadria and Layton (2021); Lin et al (2022); Ke et al (2022); Korosec et al (2023) ) and vaccination (cf. Farhang-Sardroodi et al (2021); Korosec et al (2022); Gholami et al (2023)).…”

Section: Introductionmentioning

confidence: 99%

Determination of significant immunological timescales from mRNA-LNP-based vaccines in humans

Moyles

Heffernan

Korosec

2022

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A compartment model for an in-host liquid nanoparticle delivered mRNA vaccine is presented. Through non-dimensionalisation, five timescales are identified that dictate the lifetime of the vaccine in-host: decay of interferon gamma, antibody priming, autocatalytic growth, antibody peak and decay, and interleukin cessation. Through asymptotic analysis we are able to obtain semi-analytical solutions in each of the time regimes which allows us to predict maximal concentrations and better understand parameter dependence in the model. We compare our model to 22 data sets for the BNT162b2 and mRNA-1273 mRNA vaccines demonstrating good agreement. Using our analysis, we estimate the values for each of the five timescales in each data set and predict maximal concentrations of plasma B-cells, antibody, and interleukin. Through our comparison, we do not observe any discernible differences between vaccine candidates and sex. However, we do identify an age dependence, specifically that vaccine activation takes longer and that peak antibody occurs sooner in patients aged 55 and greater.

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