PCOMPBIOL-D-25-00938

Real-time Forecasting of Data Revisions in Epidemic Surveillance Streams

PLOS Computational Biology

Dear Dr. Tang,

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Kind regards,

Philipp Martin Altrock, Ph.D.

Academic Editor

PLOS Computational Biology

Roger Kouyos

Section Editor

PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one of the reviews is uploaded as an attachment.

Reviewer #1: Summary

This article addresses the problem of data revisions in epidemiological surveillance. This problem, often due to variable data reporting delays, can lead to errors and bias in epidemiological inference. As this problem is prevalent, this manuscript addresses an important issue in the epidemiology community. This paper introduces a method to correct epidemiological quantities for possible data revisions. The authors compare it with state of the art methods.

The problem is clearly outlined and put into a clear scientific and public health context, including a comprehensive literature review in the introduction. The notation is clearly introduced. The model, its features and hyperparameters are clearly explained, although a less technical summary of the model could improve readability. We appreciate the discussion of properties of the selected evaluation metrics. The experimental setup is clearly explained. The results are clearly stated and sufficient intuition is given to the reader, and the limitations are stated clearly. The comparison with other methods is thorough, and the novelty of Delphi-RF is made apparent.

Originality:

Although I am no expert in that exact field, the authors make a convincing case in this manuscript that the research presented here is original.

Innovation:

The approach presented here appears innovative.

High importance to researchers in the field:

The research presented here addresses an issue which is commonly regarded as highly important in the field.

Significant biological and/or methodological insight:

The research is presented with sufficient biological and methodological insight.

Rigorous methodology:

The methodology appears here rigorous, and the evaluation of the model is sufficiently thorough.

Substantial evidence for its conclusions:

The conclusions are supported with sufficient evidence, and the limitations are clearly stated.

Specific comment: Fig 7. Is titled with “Boxenplot”, which to my (vague) knowledge refers to the Seaborn implementation of the “letter value plot”. However they look and are described in the Figure caption like regular boxplots?

Reviewer #2: See attachment

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

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: Review_D_25_00938.pdf

Dear PhD student Tang,

We are pleased to inform you that your manuscript 'Real-time Forecasting of Data Revisions in Epidemic Surveillance Streams' 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.

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

Best regards,

Philipp Martin Altrock, Ph.D.

Academic Editor

PLOS Computational Biology

Roger Kouyos

Section 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 #2: Thanks for addressing my feedback. I have no further comments.

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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: 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 #2: No