Dear Dr. Kuhn,

Thank you very much for submitting your manuscript "Modeling the Onset of Symptoms of COVID-19: Effects of SARS-CoV-2 Variant and Patient Comorbidities" 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.

Apologies for the delay in coming to a decision. As you'll see, the reviewers were split on the importance of the study, and we were unable to secure a third reviewer despite many attempts. I think you may have some work to convince the negative reviewer, but I am willing to give you a chance at it. If this does not work out, then you may consider PLoS Digital Health instead.

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,

Bard Ermentrout

Associate Editor

PLOS Computational Biology

Virginia Pitzer

Deputy Editor-in-Chief

PLOS Computational Biology

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

I think you may have some work to convince the negative reviewer, but I am willing to give you a chance at it. If this does not work out, then you may consider PLoS Digital Health instead.

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: This paper discusses the order of symptoms after SARS-CoV-2 infections. Using Hasse diagrams and large datasets of self-reported symptoms, comorbidities, and cancer status, the authors study how likely a set of four principal symptoms (fever, cough, nausea/vomiting, and diarrhea) are likely to occur and in which order. They expand on previous work in which they performed the same analysis on early data from Wuhan, before the emergence in the Summer of 2020 of the D614G mutation, which is now the globally-dominant variant. They report that fever and cough interchange as the first most likely symptom in those infected with SARS-CoV-2 without the D614G mutation and those infected with D614G. I found the methodology and analyses sound and the paper to be well-written. I do have a couple questions:

1) Line 251: The earlier dataset did not report nausea/vomiting, so they weren't included in the analyses of the Japanese data (which is of high interest given that the early data was taken before the emergence of D614G). What is the impact of this on predicted outcomes, since nausea/vomiting is in the third most likely path in the USA dataset, and is also found in the early China dataset? Limitations need be more clearly stated in the discussion.

2) Lines 269-279: The analysis of Google search terms is a weak point of the present paper, but does not affect the results. Given the prevalence of reporting on COVID-19, it seems impossible to untangle whether increases in specific search terms are related to increased symptoms or increases (global) reporting of symptoms in other countries. Again, limitations should be discussed.

3) Lines 445-446: Should be updated to reflect the variants of concern now in circulation.

Reviewer #2: Larsen et al. here analyzed the symptom data from thousands of SARS-COv2 infected patients in China, US and Japan. They modeled the order of symptom onset, and its potential association with virus mutations.

This is an interesting exercise, and could fit into a modeling journal, albeit the implications are somewhat limited. Clinically, the symptoms and their order are not specific enough to really help in diagnosis. Plus, in the US and Western world in general, there is a widespread access to testing, so I am not sure that this is really needed. This however could be more interesting in countries where access to testing is more limited, but in that case specific data to these countries should be analyzed. The association between symptom order and viral strains is potentially interesting but is very speculative in absence of individual data. Many factors could influence the symptom order (including seasonal change, population characteristics), not to mention the reliability and precision of such self-reported symptom to infer subtle changes in the order of symptom onset. This can nonetheless be an interesting modeling exercise, but much more attention should be given to the data and statistical aspects.

• Clarify where the data can be accessed to, how the data look like without any modeling. This is critical to ensure data transparency and reproducibility. I don’t see any basic statistics to give a sense of the data to the reader, nor a sense on how much the modeling exercise is reasonable in regard to the information available. How many individuals, their basic socio-demographic characteristics (age, BMI), what are the symptoms collected, for how long, the prevalence of each symptom over time since symptom onset, how many are pcr-confirmed, how many are antibody confirmed…

• Statistical aspects: very unclear how one path is preferred to the other, what are the statistical criterion associated with the decision inferred? As the paper stands, it is very difficult to understand the methodology, and how the statistical models have been chosen, as well as the confidence associated with each preferred path.

• The order of symptoms is important but probably even more important is the timing of these symptoms. Can you use the model to project on the timing of onset of these symptoms?

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: No: please provide an access to the data set and/or a detailed description of the data used (see comments)

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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.

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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

Dear Dr. Kuhn,

Thank you very much for submitting your manuscript "Modeling the Onset of Symptoms of COVID-19: Effects of SARS-CoV-2 Variant and Patient Comorbidities" 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,

Bard Ermentrout

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 responded to my comments, thank you. In their revisions, the authors note

"To investigate these possible factors, we determined the most likely orders of symptom onset for various datasets from different cities in the USA (Detroit, Michigan, New York, New York, and Atlanta, Georgia)"

and performed similar analyses in both China and Japan. It is unclear to me whether data for these cities were included in the original country-level datasets? If so, these results are not surprising, and the analysis would need to be redone using independent data (validation step).

Reviewer #2: I thank the authors for their detailed response to my comments. Some of my initial comments have been well addressed, but I still have comments on the data description and statistical aspects for your consideration and that of the Editor.

• Thank you for adding the raw data. It appears from Table S1 that there is a huge heterogeneity in the data used. Data coming from cities, as well as data in individuals with comorbidities/cancer are very sparse. Since the results are not different than in the general population, I would recommend to put most of these analyses in the supplementary and to focus on the main message.

• Instead I would recommend to put the description of the data in the main manuscript (focusing on the data that are used in the main). Following my initial comment, it is very important that the model predictions do not appear as a black box. Even if PLoS CB is a modeling journal, readers need to know what information is immediately visible from the data, and which one is extracted from the model. I would therefore recommend to have a figure showing, for the main data used, the raw frequency data, focusing on the first and second symptoms. We need to get a sense of the data and not only the results.

• Regarding statistical aspects, I would recommend to systematically include confidence interval along with every point estimate of transition probability. This is particularly important given the heterogeneity in the population size used in the study.

• If I understand correctly lines 706-717, there is no formal statistical test to determine a type I error and how much confidence we have that one path is preferred to another. If there is no possibility to test the likelihood of symptom order (at least the two first one), this should perhaps be added in the discussion

• The public health implications should be clarified. I am not sure to understand the rationale for “support the practice of reporting the order of symptom onset from patients”. I don’t think that one would expect that this reporting could substitute or even complement viral sequencing. This is not a problem for a publication, the interest of the approach lies in its description of potentially different symptom order across viral strains, and the possibility that it may be one additional factor for increased transmission associated with D614G, but I don’t see the need to “oversell” the approach. Please consider modify these aspects in the abstract and in the discussion.

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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: The code was not provided

Reviewer #2: Yes

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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

Dear Dr. Kuhn,

We are pleased to inform you that your manuscript 'Modeling the Onset of Symptoms of COVID-19: Effects of SARS-CoV-2 Variant' 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,

Bard Ermentrout

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 #2: I thank the authors for acknowledging limitations on the precision of the estimates. As said originally the possibility that different strains may lead to different symptom order is interesting and could account for some differences in transmission rates. My comments on the data description remains to some extent, and I am still not sure to be able to evaluate to what extent reliable conclusions on symptom order can be obtained from data set that only contain prevalence data of each symptom, but no information on their sequential aspects. I recognize however that the method used in the paper is not in my area of expertise.

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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 #2: No: please update the link to datasets. Some of them are no longer available (such as the first dataset referenced from WHO)

**********

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 #2: No