Dear Miss Pung,

Thank you very much for submitting your manuscript "Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Benjamin Althouse

Academic Editor

PLOS Computational Biology

Thomas Leitner

Section Editor

PLOS Computational Biology

***********************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Comments:

I have read this paper in a detailed manner. I wish to state that your work on Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response is commendable. But a lot needs to be done. Therefore, author needs to do overall corrections of grammar and spelling if this manuscript will be accepted. Also, consider the following corrections listed below:

1.This manuscript is not structure well into Chapters/ Sections the paper supposes should be structured.

2.You did not consider the existence, uniqueness and properties of the model formulated for this work.

3.Numerical stimulated is absent from this work and so on.

There are many things to be done on this manuscript but if it will be accepted, I would direct the authors to study and include the following papers for more clarification:

•Analysis and Dynamics of Fractional order Mathematical Model of COVID-19 in Nigeria using Atangana-Baleanue operator, Computers, Materials & Continua, (2020).

•A new mathematical model of COVID-19 using real data from Pakistan. Results Phys 2021; 24:104098. doi:10.1016/j.rinp.2021.104098

•Forecasting of COVID-19 Pandemic in Nigeria Using Real Statistical Data. Communication in Mathematical Biology and Neurosciences. 2021 (2021). doi:10.28919/cmbn/5144.

•Mathematical model of COVID-19 in Nigeria with optimal control, Results in Physics, 28(2021), 104598. doi: 10.1016/j.rinp.2021.104598

•A fractional-order mathematical model for malaria and COVID-19 co-infection dynamics, Healthcare Analytics, 4(2023), 100210. doi: 10.1016/j.health.2023.100210

Reviewer #2: In this article, the authors explore how generation and serial intervals depend on various factors, such as changes in infection characteristics and isolation strategies. They further calculate statistical power for estimating the differences in generation and serial intervals across a wide range of scenarios. While this study is a useful contribution, I have some concerns.

Even though the calculation of statistical power is a major component of this study, the methods for power calculation is not explained anywhere in the main text or in supplementary materials. The calculation of statistical power depends on the statistical model as well as the assumed significance level. Moreover, I'm more concerned about the reported powers for the generation intervals. In reality, generation intervals are rarely observed. Instead, they are estimated from the observed serial intervals. Propagating uncertainties in the estimation of generation intervals from serial intervals are likely to decrease the power. If the authors are calculating the power to detect differences in the mean generation interval, assuming that generation intervals are known exactly, they are likely overestimating the power.

Moreover, it seems like the effects of epidemic dynamics have been largely overlooked in this study. Typically, when a new variant invades a population, the previous variant in typically in a declining state. As the authors explain in the introduction, differences in epidemic growth rates can further translate to differences in the observed serial intervals. It would be interesting and useful to understand how these biases affect power and the observed differences.

Minor comment

- L52 "When variant prevalence grows rapidly within a population, it may be the result of increased transmissibility" It's unclear that increased transmissibility should necessarily correlate with shorter generation intervals. One could imagine a pathogen with higher R0 but identical infectiousness profile (and therefore generation interval distribution). It would be useful to distinguish generation intervals in real epidemics vs those representing infectiousness profiles

- L61 "Observed serial intervals may be shorter during the exponential phase of an outbreak because transmission events involving

infectees with longer incubation periods have yet to be observed" This is incorrect. Serial intervals may be longer during the growth phase because infectors are more likely to have shorter incubation periods (when we consider a group of infectors who developed symptoms at the same time)

- Overall, the infectiousness profile needs to be explained more clearly

- L133: "We then fitted a cubic Hermite spline at the respective time points and constrained the slope of the spline to be zero at each point to simulate the infectiousness profile over the course of the infection" This sentence needs some unpacking

- Equation 1: it's not obvious that the exp(Lambda(t-1)) term is needed or what it's trying to capture. Equation 1 would approximate lambda in the absence of the exp(Lambda(t-1)) term

- Simulating transmission: how many days were epidemics simulated for? Until the epidemic dies out naturally?

- "Corresponding power to detect these differences under varying human contact patterns and outbreak response" How was the power calculated? Is there an underlying statistical model?

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No: Data is not available on this manuscript

Reviewer #2: No: I couldn't find access to the code used for simulating epidemic or calculating the power.

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Attachments

Attachment

Submitted filename: PCOMPBIOL-D-23-01002 Reviewers Comment.pdf

Dear Miss Pung,

Thank you very much for submitting your manuscript "Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please feel free to ignore the requests for the citations from Reviewer 1. I am satisfied with the authors's previous response to the reviewer and agree that the citations are not relevant to the current paper.

Please do address the concerns about power calculations from Reviewer 2.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Benjamin Althouse

Academic Editor

PLOS Computational Biology

Thomas Leitner

Section Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

Please feel free to ignore the requests for the citations from Reviewer 1. I am satisfied with the authors's previous response to the reviewer and agree that the citations are not relevant to the current paper.

Please do address the concerns about power calculations from Reviewer 2.

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Authors need to have broad knowledge about the other areas of mathematical modelling especially while researching on disease outbreak.

Moreover, The selected papers contribute to the introduction by providing diverse modeling approaches, global perspectives, forecasting accuracy, insights into optimal control strategies, and an interdisciplinary dimension addressing co-infection dynamics. This enriches the theoretical foundation of the research, aligning it with a broad spectrum of mathematical modeling applications and diverse aspects of the ongoing scientific discourse on infectious disease dynamics.

Therefore, I recommend that those papers eariler to be studied and included under the introduction of this manuscript. The following papers are hereby listed below

• Analysis and Dynamics of Fractional order Mathematical Model of COVID-19 in Nigeria using Atangana-Baleanue operator, Computers, Materials & Continua, (2020).

• A new mathematical model of COVID-19 using real data from Pakistan. Results Phys 2021; 24:104098. doi:10.1016/j.rinp.2021.104098.

• Forecasting of COVID-19 Pandemic in Nigeria Using Real Statistical Data. Communication in Mathematical Biology and Neurosciences. 2021 (2021). doi:10.28919/cmbn/5144.

• Mathematical model of COVID-19 in Nigeria with optimal control, Results in Physics, 28(2021), 104598. doi: 10.1016/j.rinp.2021.104598.

• A fractional-order mathematical model for malaria and COVID-19 co-infection dynamics, Healthcare Analytics, 4(2023), 100210. doi: 10.1016/j.health.2023.100210

Reviewer #2: The authors have addressed all my previous concerns. My only remaining concern is about the power calculations for the inferred distributions. Specifically, the authors state that they estimate the mean and variance of the generation interval distribution from the serial interval distribution and calculate power based on a t test. However, it doesn't seem like the authors account for the uncertainties associated with inference, which would overestimate the power. I don't necessarily suggest that the authors perform a new set of analyses but this seems like an important limitation of the current approach.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: None

Reviewer #2: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Dear Miss Pung,

We are pleased to inform you that your manuscript 'Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Benjamin Althouse

Academic Editor

PLOS Computational Biology

Thomas Leitner

Section Editor

PLOS Computational Biology

***********************************************************