PCOMPBIOL-D-25-01054

Network structure induced bias in estimates of intrinsic generation times

PLOS Computational Biology

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PLOS Computational Biology

Denise Kühnert

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PLOS Computational Biology

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Both reviewers recognize this work as an important contribution to the field, and they offer constructive comments that will improve the manuscript.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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Reviewer #1: Please see attached PDF.

Reviewer #2: This paper attacks an interesting problem in tracking infectious diseases – how measurable generation time distributions are biased by the underlying network structure. It is interesting and well developed but some results need to be better explained and justified. The discussion of the formulation and its consequences should be expanded to better capture the comparison (and in some cases convergence) to more familiar homogenous models as well as to clarify other practicalities. Suggestions for improvements follow:

1. When presenting the equations in sections 2.1-2.2 the notation and meaning of \gamma and \beta has not yet been explained. The equation for R_f^A is introduced but its origin is unclear as no mention of a reference or supplementary section is provided. Analogous comments also hold for R_f^Q and its variables.

2. When the epidemic grows exponentially both b_A^exp and b_Q^exp seem to maybe link to the generation time expected from a homogeneous model. Is this true? If it is, this may also help better expose the connections between these models and the SIR etc.

3. The use of the annealed and quenched networks is interesting and I enjoyed seeing the expansion of the Champredon and Dushoff equations. Given this comparison, I think it would also be good (and perhaps broaden readership) if the authors make explicit any conditions under which both network types collapse to the homogenous form, leading to the recovery of Champredon and Dushoff equations.

4. The authors highlight that if the population is heterogeneous then using aggregates like the susceptible count will bias estimation of an R0 or an equivalent statistic. This is part of a broader discussion about what is measurable (e.g., intrinsic generation times are not usually observable). Continuing in that line, it would be useful to explain how measurable the alternatives such as the number of susceptible links are and whether if it may be that the bias from using susceptibles is hard to overcome practically.

5. The authors comment that the intrinsic generation time remains constant throughout an epidemic. Is this true? There have been works discussing changes in the generation time due to interventions and trying to account for any biases this may introduce into estimates of R0/Rt [1,2]. Empirically there has also been investigation via the serial interval (which is of course not the same as the generation time but often used as an approximation) [3] but does raise the question about changing generation times. The authors should discuss these papers (and any I missed that also talk about the wider context of generation times) and, if the intrinsic generation time changes, what impact it would have on their results, formulae and derived R0/Rt like quantities.

6. Not enough context or explanation is provided for the equations in the case studies. From where are they derived or taken? This requires the reader to have too much background knowledge to fully grasp what is happening. Please explain in more detail there.

7. In Fig 1-2 no expression is given for how R0 relates to \beta and \gamma. Further, while I note a GitHub link is provided, the simulations need to be better detailed. Additionally, no information is provided about the other statistics of the generation time (and in fact no distributions are plotted). This would be useful together with some comments about how (as these network models are heterogeneous and stochastic) representative and useful a deterministic estimate (e.g., of R0) will be in these settings.

8. As a key result of this work is not just the exact expressions but highlighting the bias from using just susceptible counts or incidence, the work would benefit from a figure of further simulation/explanation showing what would go wrong if we did use the naïve aggregate.

9. Seems strange to use R to refer to severity in the abstract. Perhaps transmissibility?

10. There have been relatively recent studies [4] trying to get at serial intervals via genomic data. While not essential for the paper, it would be interesting to know if there is potential to overcome some of the biases the authors reveal with such data.

References:

1. https://royalsocietypublishing.org/doi/10.1098/rsif.2022.0128

2. https://royalsocietypublishing.org/doi/10.1098/rspb.2023.1664

3. https://www.science.org/doi/10.1126/science.abc9004

4. https://www.nature.com/articles/s41467-023-40544-y

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Dear Dr. Poletto,

We are pleased to inform you that your manuscript 'Network structure induced bias in estimates of intrinsic generation times' has been provisionally accepted for publication in PLOS Computational Biology.

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Best regards,

Claudio José Struchiner, M.D., Sc.D.

Academic Editor

PLOS Computational Biology

Denise Kühnert

Section Editor

PLOS Computational Biology

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