1. Reviewer #3 (Public Review):

    The new models proposed here provide some potentially useful alternatives to estimating the generation time, serial interval, and the relative infectiousness of pre-symptomatic infections. The framing of the paper seems very focused on improving fits to the transmission pair data, however, and I think it would be more impactful to consider the implications of poor estimation of pre-symptomatic transmission and the generation time. I think this shift in focus could also help strengthen the narrative of the paper, which wavers between focusing on model fitting and the importance of implications for contact tracing.

    I was a bit lost in the application of the models to the contact tracing example. The definition of the contact elicitation window (lines 142-144), where identification of contacts would occur up to x days prior to contact symptom onset, makes sense theoretically in this model comparison setting, but it is hard to translate these findings to real-world application. Are there any implications that could be useful for informing contact elicitation strategy (e.g., for how many days after time of infection or symptom onset could contact tracing have a measurable benefit in preventing onward transmissions?)

    Lines 147-151: Given that the impact on onward transmission events is so dependent on the contact tracing assumptions, I would recommend stating the assumptions explicitly here, reporting the results in relative terms as compared to a single model, or both.

    How different are the variable infectiousness model results from parameter estimates from the original studies that reported the transmission pairs data?

    Can the authors comment on the plausibility of the infectiousness distribution in their new proposed models? While better model fitting certainly provides a measurable improvement to leveraging existing data, I'm not aware of studies that support the discontinuous assumptions about infectiousness made here.

    Assuming alpha means the same thing across the models, why is the 95% credible interval so large for the Feretti model? In general, the model parameters should be more clearly explained for this model.

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  2. Reviewer #2 (Public Review):

    In this analysis, the authors consider the impact of the duration of infectiousness of a person infected with COVID-19 prior to the appearance of clinical signs. This is an important problem, as identification of disease status often relies on a self-reporting, i.e. from people experiencing clinical signs, and in the case of COVID-19 in the UK, where they have then gone on to test positive (typically with a PCR test). The greater the proportion of transmission that occurs before clinical signs appear then, the less likely that methods based on self-reporting will be sufficient to contain epidemic spread.

    The general problem is well known, with examples of previous analyses including for livestock diseases such as foot-and-mouth disease (see for example, Haydon et al. 1997 https://doi.org/10.1093/imammb/14.1.1 and the very many papers on the 2001 FMD epidemic), and most importantly the seminal paper by Fraser et al. on the SARS-CoV-1 pandemic which laid out the problem in extensive detail https://doi.org/10.1073/pnas.0307506101. In the analyses of the current SARS-CoV-2 dynamics, the authors refer to the paper by Feretti et al. (https://doi.org/10.1126/science.abb6936) which at this point represents the most prominent analysis of this type that is directly relevant to the current pandemic. More broadly, issues with exponential distributions and the impact that their use has on analyses of infection dynamics and epidemic behaviour have been well studies in other systems such as measles (e.g. Lloyd 2001 https://doi.org/10.1006/tpbi.2001.1525, and Conlan et al. 2009 https://doi.org/10.1098/rsif.2009.0284). It would be helpful for the paper to refer to this broader literature in order to contextualise the analysis though this does not of course detract from the relevance to the current COVID-19 pandemic.

    In this analysis the authors show that, by choosing a pre-infectious period that is explicitly excludes any probability of infection, they achieve a better fit to the distribution of serial interval for a large number of known transmission pairs (previously analysed in the Ferretti paper). This is an entirely sensible result and a good use of a better mechanistically informed idea of the infection process (in essence, here incorporating explicitly the inevitable delay between virus entering the body, and a person becoming infectious).

    By examining the proportion of infections that would be captured by contact tracing when considering a two-day window prior to symptom onset, they show a substantially greater efficacy for contact tracing, compared to a more standard compartmental modelling approach (where the duration of each consecutive period is independently determined).

    While the analysis itself is detailed and thoroughly explained I have some questions regarding the utility of the result when making the comparison to other models. As noted earlier, the fundamental problem is already well known, and the application to COVID-19, while useful, is better than poorer models, but only marginally better performing than the Ferretti model. The serial interval estimates are only slightly better (figure 2), there are 84% of contacts when considering tracing two days prior to symptoms, compared to what looks like about 80% for the alternative in figure 4 and by the looks of the violin plots from figure 3, quite a bit of overlap if one considers credible intervals.

    As such, while the analysis is a solid, useful addition to the literature, it could use a better exposition on how it advances scientific insight (the fundamental issues regarding exponential distributions having been identified previously), methodologically (given the thorough analysis by Fraser et al in 2004) or in terms of impact (given the limited improvement over the Ferretti model).

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  3. Reviewer #1 (Public Review):

    The authors develop a mechanistic model for inferring infectiousness profile from data on times of symptom onset in pairs of infector-infectee. The novelty of their approach lies in assuming that infectiousness of an infected individual depends also on the whether or not they have symptoms. The authors fit a data set of time of symptom onset in 191 transmission pairs to a model that assumes that infectiousness varies along the incubation period. They compare the model fit to fits from models and find that their model of differential infectiousness explains the data better than the other models considered.

    This is a carefully constructed study, and the conclusions are well supported by the analysis carried out. My only concern is that the data used were obtained during the early stage of the pandemic (January to February 2020). As the pandemic was growing in most countries during this time, we are more likely to have observed shorter serial intervals. Similarly, as isolation of infected individuals would prevent them from transmitting further, longer serial intervals are likely to be under-represented in the data. Indeed, the longest serial interval in the data used was 5 days. It would be interesting to understand whether the conclusions about the proportion of onward transmissions averted by contact tracing and subsequent isolation still hold as the pandemic progresses, and we continue to observe longer serial intervals. If the authors are unable to find more recent data, this caveat should be clearly discussed.

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  4. Evaluation Summary:

    The manuscript uses a new approach to model the infectiousness profile of COVID-19 infected individuals. The work suggests a higher proportion of pre-symptomatic infectiousness in COVID-19 than the current evidence. The findings are of great interest to public health policy makers. The methodology is of general interest to modellers working on COVID-19.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

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  5. SciScore for 10.1101/2020.11.20.20235754: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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