Dear Dr. Fujita,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Guangyin Jin

Academic Editor

PLOS ONE

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[SF is an employee at Shionogi & Co. There was no involvement of Shionogi & Co in the publication process. The other author declares no conflicts of interest.]. 

We note that one or more of the authors have an affiliation to the commercial funders of this research study : [Shionogi & Co].

1. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: 1- Add numeric values for model efficiency metrics to the abstract.

2- Using research papers published in 2025 and adding them to the related works.

3- Explaining the proposed algorithm in clear and simple steps for easy follow-up

4- Discuss tables and graphs better and reflect a full understanding of what is happening.

5- For conclusions not based on direct numerical values and future work not sufficiently explained

Reviewer #2: This study proposes a physics-informed spatial identity neural network for time-series forecasting of epidemic cases. The method integrates spatiotemporal information with domain knowledge for forecasting, and its performance is demonstrated using real data collected from Japan and the USA. The following comments are provided for the authors’ consideration:

1.The prediction performance heavily depends on the partitioning of datasets, and the proposed method is primarily applicable to time-series data with stationary patterns. This is evident from the results in Table 1 and Table 2. The proposed method does not consistently outperform others in all cases. The authors should highlight the limitations of the study and explain potential areas for improvement.

2.Lines 86–87: The key assumption for predicting time-series data is that there is no regime change in the dataset. Once the data becomes non-stationary, forward predictions become challenging. The authors should discuss how to detect regime or pattern changes.

3.Line 108: The integration of deep learning models (e.g., GNN) with epidemiological domain models has been explored by previous studies. The authors should clearly highlight the key methodological contributions of this work.

4.Line 146: How is the dimension of the latent space determined? The authors should also discuss the sensitivity of the results to this dimension.

5.Line 227: The data is pre-processed to remove seasonality. In practice, it is very difficult to detect periodicity in the early stages of prediction. The authors should elaborate on how this was handled.

6.The following references should be cited to strengthen the study’s context and relevance:

oTime series analysis and gated recurrent neural network model for predicting landslide displacements

oEnsemble learning of soil–water characteristic curve for unsaturated seepage using physics-informed neural networks

oInterpretation of spatio-temporal variation of precipitation from spatially sparse measurements using Bayesian compressive sensing (BCS)

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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.

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Reviewer #1: No

Reviewer #2: No

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Enhancing epidemic forecasting with a physics-informed spatial identity neural network

PONE-D-25-28281R1

Dear Dr. Fujita,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Guangyin Jin

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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Reviewer #1: (No Response)

Reviewer #2: (No Response)

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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

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