Dear Dr Santillana,

Thank you very much for submitting your manuscript "Estimating the Early Outbreak Cumulative Incidence of COVID-19 in the United States: Three Complementary Approaches" 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. It would be useful also if you could update your estimates.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript may 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,

Cecile Viboud

Associate Editor

PLOS Computational Biology

Nina Fefferman

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: I appreciate the opportunity to review “Estimating the Early Outbreak Cumulative Incidence of COVID-19 in the United States: Three Complementary Approaches” by Lu et al. The authors utilize influenza-like illness surveillance data, COVID testing volume, and reported COVID deaths to estimate cumulative symptomatic COVID cases in US states. Though I think this work is important and that the manuscript well-written, I have a few concerns regarding the authors’ methodology. Please see my comments below.

Results

Line 64: I recommend explicitly mentioning that the “Divergence” and “COVID Scaling” approaches estimate symptomatic COVID cases up to the week of April 4th and that the “mMaP” approach estimates cases up to May 16th.

Line 95: It’s briefly touched on in the discussion but I think it’s important to acknowledge that there was a “worried well” spike in ILI visits at general practitioner offices at the beginning of the epidemic in the US (early March) and then a sharp decline in ILI due to stay-at-home orders, increased use of tele-health services, seeking healthcare at providers that are not included in ILI surveillance (e.g., urgent care, emergency rooms), and the end of the flu season.

Discussion

Line 232: ILI surveillance does not likely accurately capture ILI dynamics for elderly individuals residing in nursing homes (the individuals most at risk for severe COVID and death).

Methods

General comment: I recommend including uncertainty estimates for ILI projections, which would in turn enable uncertainty estimates for COVID burden for the approaches that utilize ILI data.

Line 324: The IDEA model/Farr’s Law seems too simplistic for projecting ILI cases in the absence of COVID. Did the authors assess whether this method accurately captures ILI trajectories in prior seasons? It seems more appropriate to use a time series model fit to historical ILI data from several past seasons (state-level data are available going back to 2010-2011), which can account for seasonality in each state and provide prediction intervals for ILI projections.

Line 369: I may have misunderstood but it seems that the authors multiplied % flu positive in 2020 by ILI counts from 2019. If this is the case, I recommend utilizing ILI data for 2010/2011 to 2018/2019 to compute an ILI “seasonal baseline” with 95% confidence intervals for each state, rather than applying 2019 ILI counts to 2020 dates.

Line 392: Did the authors also explore how the decline in health-seeking behavior after stay-at-home measures potentially impacted p(visit|ILI)?

Figures: Please define "unadjusted" and "adjusted" in figure captions.

Reviewer #2: The authors present an analysis of COVID testing and ILI data to infer the true number of symptomatic COVID-19 cases in the US. This is of significant interest to researchers and policymakers. Additionally, using existing surveillance systems (such as ILI surveillance) is an important methodological approach that has been underused. However, I would recommend some revisions and updating the manuscript to reflect recent literature that estimates true infection rates, such as [1].

Major points:

1) Is the assumption of constant sCFR and IFR through time justified? I realize that the literature on IFR is constantly evolving, but increasing quality of care as well as changes in who is getting infected (e.g., nursing home outbreaks early in the pandemic) might have shifted the overall IFR (and thus sCFR) over time. Also, does adding uncertainty in the asymptomatic fraction substantially change the results? Given that uncertainty in IFR is incorporated, it seems odd to use a point estimate for the asymptomatic fraction, given that it has a substantial range associated with it (and, again, could be changing over time as the infected population changes). In any case, if these sources of uncertainty cannot be incorporated due to lack of data, I think it is important to at least address them in the discussion.

2) Why only focus on excess pneumonia deaths to adjust for underreporting of COVID-19 deaths? I believe that the CDC provides state-level data on all-cause mortality compared to COVID-19 mortality (https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm#data-tables), and these estimates could be used to provide an upper bound on true deaths for the whole dataset.

3) Is the COVID scaling approach fair? Even after accounting for sparse case counts, testing backlogs, and false negatives, I find the assumption of uniformly applied testing problematic (after line 431—line numbering seems to have gone awry in this section). Testing shortages, especially early in the epidemic, may have biased testing toward higher-risk subjects. As testing capacity increased across states, test positivity may have gone down simply due to testing more people, while true prevalence (and therefore, presumably fraction of ILI infected with COVID) remained relatively constant. While this limitation is mentioned in lines 254-255, I think that additional discussion is needed, as this assumption of unbiased testing is almost certainly broken.

Minor points:

1) I think it would help with clarity to provide the names for the three approaches (divergence, scaling, and mortality) in the introduction.

2) Line 282: “(up to 4%) of the US population may have already been infected.” It would be good to specify the date here, since I assume that this was using the estimates of true infections at the end of May. In that case, the following statement that “subsequent waves of infection may decrease in magnitude” should be revaluated in the context of extant case data for the second wave of infection that occurred during the summer.

3) Line 353: I think a reference to either the supplement or previous section is missing.

4) It seems that the mortality method assumes that deaths are reported on the actual date of death. It is not clear that all states report deaths in this way, and I am curious if accounting for delays in death reporting could be incorporated into the MAP framework.

5) References 70 and 71 seem to be misformatted.

6) Supplement, after equation 7 should “P(t=0)” be “p(t=0)?” I didn’t quite follow what the change in summation limits here was.

[1] Wu, S.L., Mertens, A.N., Crider, Y.S. et al. Substantial underestimation of SARS-CoV-2 infection in the United States. Nat Commun 11, 4507 (2020). https://doi.org/10.1038/s41467-020-18272-4

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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: No: The authors state that data will be made available upon acceptance.

Reviewer #2: No: Authors have said that data will be provided in a repository upon acceptance. It is not currently available.

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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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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, PLOS recommends that you deposit 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. For instructions, please see http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methods

Dear Dr Santillana,

Thank you very much for submitting your revised manuscript "Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: four complementary approaches" for consideration at PLOS Computational Biology. The paper went back to the two reviewers, who are very pleased with the revisions. One of the reviewers has made a few additional light suggestions. 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,

Cecile Viboud

Associate Editor

PLOS Computational Biology

Nina Fefferman

Deputy 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:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: I appreciate the authors' careful attention in addressing my comments on the previous submission. I do not have any additional feedback for the revised manuscript.

Reviewer #2: I thank the authors for their thoughtful responses to my comments and concerns and I believe that the addition of GLEAM to the methods tested is helpful. However, I think would be helpful to provide more information on the particulars of the authors' implementation of GLEAM. Specifically:

1) Line 619: "We assume varying levels of effectiveness of the mitigation policies...":

It was unclear to me how this was accomplished. Does this mean that effectiveness varied based on which policies different states adopted, or did the same policy adopted in two different states have different effectiveness?

2) Line 621: "We then perform model selection...": This is related to my previous point, but I think more clarity is needed regarding what precise models were compared.

3) Are the specific assumptions about the prior distribution of key parameters for the U.S. model available (e.g., incubation period, IFR, duration of infectiousness, etc.)? Are they the same as the assumptions made for the global calibration of the model [1]?

4) Does the model's assumption of no pre-symptomatic transmission affect the estimates of the true number of infections? This might be useful to add to Table 2, or as a point in the discussion of limitations.

[1] Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, Pastore Y Piontti A, Mu K, Rossi L, Sun K, Viboud C, Xiong X, Yu H, Halloran ME, Longini IM Jr, Vespignani A. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020 Apr 24;368(6489):395-400. doi: 10.1126/science.aba9757. Epub 2020 Mar 6. PMID: 32144116; PMCID: PMC7164386.

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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: No: The authors state "The data will be held in a public repository after acceptance at " ext-link-type="uri" xlink:type="simple">https://github.com/andrenguyen/mil-covid19-usa". The github repo is not currently accessible.

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, PLOS recommends that you deposit 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. For instructions see http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methods

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.

Dear Dr. Santillana,

Thank you very much for submitting your revised manuscript "Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: four complementary approaches" for consideration at PLOS Computational Biology.

The revised manuscript went back to the reviewers, who agree that their major comments have been addressed. Reviewer 2 has made a few residual comments/suggestions, which we expect you will be able to address easily. 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,

Cecile Viboud

Associate Editor

PLOS Computational Biology

Nina Fefferman

Deputy 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:

[LINK]

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-emailutm_source=authorlettersutm_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 Dr. Santillana,

We are pleased to inform you that your manuscript 'Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: four complementary approaches' 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,

Nina H. Fefferman

Deputy Editor

PLOS Computational Biology

Nina Fefferman

Deputy Editor

PLOS Computational Biology

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