Dear Dr Parag,

Thank you very much for submitting your manuscript "Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers" 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 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,

Joseph T. Wu

Associate Editor

PLOS Computational Biology

Virginia Pitzer

Deputy Editor-in-Chief

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:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: In "Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers", Parag and Donnelly examine out ability to detect changes in epidemic growth, as characterized by the reproductive number R, in real time. They find that there are delays in our ability to detect changes in the reproductive number, particularly changes around the critical threshold of R being above or below 1, that are inherent in the generating process and hence cannot be overcome by improved analysis of epidemic curves. In many ways there results are obvious, shifts in R cannot be detected without data, and since that data comes from subsequent generations of infection that are necessarily delayed by the generation time of the disease, there is an upper limit to how quickly we can detect these shifts. Likewise, as more cases are present when epidemics shift to a decline than when they shift to a resurgence, statistical theory tells us the former will be easier to detect than the latter.

My statement that these conclusions are in retrospect obvious is in no way a negative comment on this work…much of the best science seems obvious in retrospect. The thorough and comprehensive way in which the authors prove their point and quantify many of these delays is compelling and invaluable, and I found no technical problems with their work. I found the use of classical statistical measures, such as Fisher information, to characterize many of the results a particularly compelling. Hence, I think this analysis is a strong and important contribution to the literature.

As detailed below, I do think the presentation (particularly the figures) could be made more clear and that the authors could be a little bit more nuanced in their discussion of the practical impact of these results. However, these are simply improvements to what is already and interesting and informative paper.

Specific Comments:

1 Abstract, "This belies epidemic…"

I don't think this means quite what the authors intend, or I am misunderstanding the rest of the sentence.

2 Abstract, "Responses to recrudescent…"

I think this misses the nuance that is around a similar statement in the main text. I.e., it does not come across that what is being argued is that improvements to analysis of epidemics curves are less important than improving surveillance.

3 Introduction, "…enhancing syndromic…Such systems…"

I feel like the argument here is broader than syndromic surveillance, as highlighted by the reference to wastewater surveillance later. I.e., the authors appear to be arguing that novel methods beyond syndromic surveillance might be useful.

4 Figures 1-4

I will admit to finding the figures a bit hard to decode, and wonder if something can be done to make the meaning of the colors, axes, etc. a bit more clear.

5 Discussion, "…there are grounds….that enact interventions…at the expense of false alarms"

I wonder if this argument goes too far beyond what the analysis in the paper supports, and might warrant a more nuanced discussion. Without an analysis of the cost of false alarms, it is hard to say if a conservative approach is warranted or not. Further, this decision will depend on the cost of an intervention over the short and long term, which will likely be specific to intervention and context. As to the latter point, my perception is that in the United States public health agencies had the political capital to implement interventions only a limited number of times, while in some other countries there was more acceptance or repeated imposition of control measures during the COVID-19 pandemic. I certainly don't think it is incumbent on the authors to wade into all such thorny issues, but I think it would be worthwhile for them to acknowledge they exist and that their analysis represents only a first step in figuring out how to better optimize responses. [I now see that this was addressed a bit in the last paragraph, but still think it warrants a bit more discussion here].

6 Discussion, "…enhancing syndromic surveillance…."

See above comments about limiting to syndromic.

Reviewer #2: Please see attachment.

**********

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: Yes

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.

Attachments

Attachment

Submitted filename: Parag_review_upload.pdf

Dear Dr Parag,

We are pleased to inform you that your manuscript 'Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers' 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,

Joseph T. Wu

Associate Editor

PLOS Computational Biology

Virginia Pitzer

Deputy Editor-in-Chief

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: The authors have done a good job of addressing most of my concerns, and I think this paper will be a valuable contribution to the literature. I do still find the figures a touch challenging, but understand that the concepts being conveyed are complex.

Reviewer #2: Thank you for your comprehensive response to the points raised.

**********

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: Yes

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