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arxiv: 2605.17129 · v1 · pith:ZRRXFDCOnew · submitted 2026-05-16 · 🧬 q-bio.PE · q-bio.QM

Within-host immunology to age-of-infection epidemiology via a virtual cohort

Julien Arino , Morgan Craig , Clotilde Djuikem , Kang-Ling Liao , St\'ephanie Portet This is my paper

Pith reviewed 2026-05-20 14:24 UTC · model grok-4.3

classification 🧬 q-bio.PE q-bio.QM
keywords within-host modelage-of-infectionsynthetic populationvirtual cohortSARS-CoV-2individual heterogeneityepidemiological modeltransmission dynamics
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The pith

Individual immune variation in SARS-CoV-2 can be scaled directly into population transmission models via ranked virtual patient profiles.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out a chain of steps that turns numerical results from a model of virus and immune responses inside one person into inputs for a model of how the disease moves through many people. First, the within-host model is run many times to spot which parameters create the widest range of outcomes. Those parameters then define a group of simulated individuals whose response curves are saved over time since infection. The curves are ordered from mildest to most severe, including death, and this ordering supplies the parameters for an age-of-infection SIR model at the population scale. A reader would care because the method supplies a concrete route to include biological differences in epidemic forecasts without requiring full individual-level data.

Core claim

By numerically identifying the within-host parameters that produce the largest variation in model outputs, the authors generate a synthetic population whose time-dependent severity profiles, ranked from mild infection to death, serve as direct inputs to parameterize an age-of-infection structured epidemiological model, thereby establishing a one-directional link from individual heterogeneity to population-level transmission dynamics.

What carries the argument

The virtual cohort, a synthetic population created from the most variable within-host parameters and then ranked by severity as a function of time since infection, which supplies the parameter values for the age-of-infection SIR model.

Load-bearing premise

The parameters that produce the largest variation inside the within-host model are sufficient by themselves to generate synthetic severity profiles that match real-world heterogeneity and can be fed straight into the population model without further calibration against observed data.

What would settle it

Finding that the distribution of mild, severe, and fatal outcomes in the synthetic cohort deviates substantially from the proportions recorded in real SARS-CoV-2 case data would show that the direct parameterization step does not hold.

Figures

Figures reproduced from arXiv: 2605.17129 by Clotilde Djuikem, Julien Arino, Kang-Ling Liao, Morgan Craig, St\'ephanie Portet.

Figure 1
Figure 1. Figure 1: Conceptual overview of the method. Step 0 – Choose a within-host differential equations model describing the course of an infection in hosts and an age-of-infection structured epidemiological model describing the spread of the pathogen in a pop￾ulation. Determine which age-of-infection characteristics need to be extracted to parametrise the model, in order to guide the next steps. Step 1 – Carry out a glob… view at source ↗
Figure 2
Figure 2. Figure 2: Flowchart of the simplified within-host model. The state variables are target cells ( [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Ranked global sensitivity scores of parameters, where the global sensitivity is defined as the sum of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Peak lung damage and timing (in days) of peak, [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effects of thresholds ξ h and ξ d . (a) Length of hospital stay as a function of the hospitalisation threshold ξ h when ξ d = 85%. Boxplots show the distribution of hospital stay duration (days). Pink dots indicate the mean duration. Percentages correspond to the fraction of surviving hospitalised patients. (b) Percentage of the types of outcomes (mild, ICU and deaths) depending on the lethality threshold … view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of the infectious period duration [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Estimation of population-level age-of-infection disease-induced death rate from the virtual cohort. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Estimated (a) time-to-recovery probability distribution [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Transmissibility estimated from within-host viral load using four different summary functions, the [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of between-host incidence trajectories [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
read the original abstract

We present a methodology providing a one-directional link from within-host individual heterogeneity to population-level disease transmission dynamics. The methodology works in several steps. A within-host model is investigated numerically to determine pathogen and immunological parameters leading to the largest variation of model responses. These key parameters are used to generate a synthetic population of individuals whose temporal immunological response profiles are recorded. These responses are ranked in terms of the severity of experienced outcomes, from mild infections to death, as a function of time since infection. This is used to parametrise an age-of-infection structured epidemiological model to study the transmission dynamics of the disease at the population level. The approach is illustrated using a within-host model describing SARS-CoV-2 infection and an SIR population-level model.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript presents a methodology to link within-host immunological heterogeneity to population-level transmission dynamics. It identifies key parameters from a within-host SARS-CoV-2 model that maximize response variation, generates a virtual cohort of synthetic individuals with ranked severity profiles over time since infection, and uses these ranks to parametrize an age-of-infection structured SIR epidemiological model.

Significance. If the virtual cohort's severity rankings prove representative of real heterogeneity, the approach could provide a structured, one-directional pipeline for incorporating individual immunological variation into epidemiological models, potentially improving predictions of transmission and outcomes for pathogens like SARS-CoV-2. The framing as a virtual cohort bridge between scales is conceptually clear, but the absence of any empirical validation or comparison limits its current significance.

major comments (3)
  1. [Abstract and Methods] Abstract and Methods: The claim that the virtual cohort 'faithfully transmits heterogeneity' to the epidemiological model rests on the assumption that parameters maximizing numerical variation in the within-host model produce severity profiles representative of real SARS-CoV-2 outcome distributions; no quantitative validation, error analysis, or comparison against empirical cohort data is provided to support this transfer.
  2. [Results] Results: The ranked time-since-infection severity profiles are directly inserted into the age-of-infection SIR transmission rates without calibration against observed distributions of infectiousness or clinical severity; this makes the population-level dynamics dependent on an untested modeling choice rather than data-constrained heterogeneity.
  3. [Methods] The selection of 'key within-host parameters' is described only at a high level as those leading to largest variation; without explicit criteria, sensitivity analysis, or justification that these parameters capture the dominant sources of real-world outcome heterogeneity, the downstream epidemiological conclusions lack a clear foundation.
minor comments (2)
  1. [Methods] Notation for the severity ranking function and its mapping to transmission rates could be made more explicit to improve reproducibility.
  2. [Figures] Figure captions should include more detail on how the virtual cohort is sampled and ranked to aid reader interpretation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive review and the opportunity to strengthen the manuscript. We have revised the text to clarify the scope of our methodological pipeline, replace imprecise language, expand the description of parameter selection with explicit sensitivity analysis details, and add an explicit discussion of limitations including the absence of empirical validation. Our point-by-point responses follow.

read point-by-point responses

  1. Referee: [Abstract and Methods] The claim that the virtual cohort 'faithfully transmits heterogeneity' to the epidemiological model rests on the assumption that parameters maximizing numerical variation in the within-host model produce severity profiles representative of real SARS-CoV-2 outcome distributions; no quantitative validation, error analysis, or comparison against empirical cohort data is provided to support this transfer.

    Authors: We agree the original phrasing risked overstating biological representativeness. The pipeline is intended to propagate numerical heterogeneity generated by the within-host model; it does not claim that the resulting severity profiles match real-world distributions. We have replaced 'faithfully transmits heterogeneity' with 'transmits the modeled heterogeneity' throughout the abstract and methods. A new limitations paragraph has been added to the Discussion that explicitly states the lack of empirical validation or error analysis against cohort data and identifies this as a target for future work. revision: yes

  2. Referee: [Results] The ranked time-since-infection severity profiles are directly inserted into the age-of-infection SIR transmission rates without calibration against observed distributions of infectiousness or clinical severity; this makes the population-level dynamics dependent on an untested modeling choice rather than data-constrained heterogeneity.

    Authors: The direct use of ranked profiles is a deliberate, uncalibrated modeling choice that allows the pipeline to remain one-directional and data-light at the population scale. We accept that this renders the quantitative epidemiological outputs illustrative of the method rather than calibrated predictions. In revision we have added a sensitivity analysis that re-ranks profiles under alternative severity metrics and reports the resulting variation in R0 and peak incidence; we have also rewritten the Results and Discussion to state clearly that the population-level findings demonstrate the linking procedure and are not intended as calibrated forecasts. revision: partial

  3. Referee: [Methods] The selection of 'key within-host parameters' is described only at a high level as those leading to largest variation; without explicit criteria, sensitivity analysis, or justification that these parameters capture the dominant sources of real-world outcome heterogeneity, the downstream epidemiological conclusions lack a clear foundation.

    Authors: Parameter selection was performed via a variance-based global sensitivity analysis (Sobol indices) on the within-host model outputs, retaining parameters whose total-order index exceeded 0.1 for peak viral load or infection duration. We have expanded the Methods section to report the full sensitivity procedure, the numerical threshold applied, and the resulting parameter set. While we acknowledge that real-world heterogeneity may include factors absent from the within-host model, the selection is now explicitly justified as the dominant sources of variation within the given model. revision: yes

Circularity Check

0 steps flagged

No circularity: forward simulation pipeline from within-host variation to age-of-infection parametrization

full rationale

The paper outlines a sequential methodology consisting of numerical exploration of a within-host model to identify parameters producing largest response variation, generation of a synthetic virtual cohort with recorded temporal profiles, ranking of those profiles by severity as a function of time since infection, and direct insertion of the ranked profiles to parametrize an age-of-infection structured SIR model. This constitutes a one-directional forward chain with no self-definitional steps, no fitted inputs renamed as predictions, and no load-bearing self-citations or imported uniqueness theorems that would reduce the central claim to prior work by the same authors. The derivation remains self-contained as an explicit modeling pipeline rather than any closed loop equivalent to its inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that numerical identification of high-variance parameters in the within-host model produces a representative synthetic population and that severity ranking can be mapped directly onto transmission parameters without further empirical adjustment.

free parameters (1)
  • key within-host parameters
    Parameters selected numerically for producing largest variation in model responses; their specific values are fitted or chosen to maximize heterogeneity before cohort generation.
axioms (1)
  • domain assumption The within-host model responses can be ranked by severity in a manner that directly informs age-of-infection transmission rates.
    Invoked when moving from ranked virtual cohort profiles to parametrization of the epidemiological model.

pith-pipeline@v0.9.0 · 5669 in / 1444 out tokens · 31347 ms · 2026-05-20T14:24:15.939924+00:00 · methodology

discussion (0)

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