PONE-D-25-08806Harnessing Hybrid Perception on Multi-scale Features for Hand-Foot-Mouth Disease Multi-region Prediction Based on Seq2SeqPLOS ONE

Dear Dr. zhu,

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Academic Editor

PLOS ONE

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This work is funded by:

Bingbing Lei, Scientific research project of Ningxia Education Department(NYG2024084);

Tao Zhou, National Natural Science Foundation of China(62062003).

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Reviewers' comments:

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Comments to the Author

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Partly

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1、The author lacks a clear statement of the research motivation. In the contribution section, the author directly lists the frequency-enhanced channel attention mechanism, but does not explain why this module was considered beforehand.

2、The author only discusses time series prediction models in the context of the prediction task for hand, foot, and mouth disease in the related work section. However, we know that time series prediction models are widely used and have a long research history. The author lacks an in-depth review of the relevant work.

3、The section on model structure lacks a clear and easy-to-understand diagram of the model architecture.

4、The experimental results section lacks a detailed description of the dataset and the data preprocessing methods.

5、In the experimental results section, the author does not provide an introduction to the baseline models, instead placing some of this information in the introduction section, which seems inappropriate. Additionally, all the baseline models listed by the author are deep learning models, lacking comparisons with classical hand, foot, and mouth disease prediction models and traditional machine learning models. It is recommended to include these comparisons.

6、The experimental results section only analyzes the individual experiments and lacks an overall summary of the experimental results.

7、The model should be validated on multiple datasets.

Reviewer #2: Dear Editors and Authors,

I have reviewed the manuscript and hereby put forward some suggestions for revision.

Overall, the author manages to clearly convey their work, especially the designed DL framework. However, there are still aspects that need improvement. Specifically, the accuracy of vocabulary and grammar usage requires attention. Regarding the dataset, it is rather simplistic. For instance, the data from Chengdu mentioned in Reference 21 could be incorporated for experiments and comparisons. This would enhance the comprehensiveness and validity of the research. Finally, the author is advised to explore and incorporate more SOTA DL methods. This would not only bring novelty to the study but also potentially lead to more accurate and efficient results in predicting HFMD cases.

1.Please provide the links to the data and code.

2.The abstract contains some errors and inappropriate places, please revise carefully, such as:

a.there are some grammar errors in the abstract, please check carefully.(also in the main body of this manuscript)

b.some descriptions in the abstract are unclear. For example: ‘significant information pertinent to the analysis.’

c.why is the title of the paper included in the abstract?

3.Please correct the use of proprietary terms and abbreviations, such as SVR.

4.Lines 34-42 of the main text are logically disorganized, with unclear sentence structure and poor consistency in word usage.

5.Why not attempt to test and compare the data of Chengdu city on this dataset, as mentioned in reference 21?

6.After introducing the three main contributions of the Seq2Seq-HMF model, the results of the comparative experiments and ablation experiments are directly mentioned, lacking a brief explanation of the experimental setup and objectives. It would be beneficial to supplement that these experiments were conducted to verify the effectiveness of the model and the functions of each module, enabling readers to better understand the significance of the experimental results.

7.Table 1 has not been explained in the main text. If it is necessary to list the symbols used in the text, please choose an appropriate way.

8.Since your method does not involve GNNs, I believe that there is no need for you to provide a detailed description of GNNs in the main text. A simple description is sufficient, just like the descriptions of other methods in the above text. If you want to facilitate the readers' understanding, you can choose to place the relevant content in the sup.

9.Why are the "Evaluation metrics" placed in the "Experimental results" chapter?

10.In line 295, you mentioned "a state vector depth of D = 128". Does this refer to the hidden layer parameter of the LSTM or some other parameter? Please provide a detailed explanation. Since the number of layers in a recurrent neural network generally should not be too large, an excessively large number of layers can lead to problems with gradient propagation.

11.Figure 5 looks rather chaotic. The information it contains has not been reasonably compressed but is merely presented in an intuitive manner. Please highlight the information you intend to convey. For multiple variables, you can use evaluation metrics for comparison, or display them separately and emphasize their excellent performance.

12.In Figures 6 and 7, all the lines are mixed together, making it impossible to observe useful information. Additionally, in these figures, each color represents the prediction results of a fixed model. Why not just have one legend? It seems that your figures were drawn using Python. Please integrate all the legends. In the Python code, only one plot should have the ‘plt.legend’ set.

13.In Figure 8, you might consider placing different evaluation metrics in separate graphs. Also, the form of "R2" is not reasonable. You can manually modify the text description in the graph and express R square correctly using a mathematical expression.

Reviewer #3: The manuscript PONE-D-25-08806 proposes a Seq2Seq-based deep learning model for predicting the spatial dynamics of hand, foot, and mouth disease (HFMD). While the model architecture appears innovative, the following points require clarification and improvement:

1. The title and introduction emphasize multi-region HFMD prediction as a key contribution. However, Figures 6 and 7 focus on single-region case predictions. How does simultaneous multi-region prediction fundamentally differ from separate single-region modeling? How is the claimed spatiotemporal dependency explicitly demonstrated in the current experiments? Visualizing spatial interaction patterns (e.g., using ArcGIS) to compare multi-region and single-region predictions would strengthen this claim.

2. Given that multi-region joint prediction could theoretically be achieved through conventional multi-task learning, please address: What specific advantages does the proposed complex architecture offer over simpler multi-task learning frameworks? Is the reported performance improvement statistically significant compared to baseline multi-task approaches?

3. While the model emphasizes generalization and accuracy, interpretability is critical for epidemiological applications. Please discuss: How does Seq2Seq-HMF provide insights into HFMD transmission drivers? Are there mechanisms to trace how spatiotemporal dependencies influence predictions?

4. Were input features standardized/normalized to mitigate bias? How were missing values or outliers handled? Could these methods inadvertently suppress long-term trends in the data?

5. HFMD exhibits seasonal patterns. Was periodicity explicitly modeled (e.g., via month/season encoding)? How sensitive are the results to the spatial weight matrix (e.g., adjacency-based vs. distance/population-mobility-based)? Have alternative spatial weighting schemes been tested for robustness?

6. The design of STPE encoder cells and FECAM decoder modules is presented as novel. What existing methodologies inspired these components? Are there foundational references that support the architectural choices? Citations should be provided.

7. All figures require resolution enhancement to ensure readability of labels, legends, and annotations.

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Reviewer #1: Yes: Chao Duan

Reviewer #2: No

Reviewer #3: No

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Submitted filename: review.docx

Harnessing Hybrid Perception on Multi-scale Features for Hand-Foot-Mouth Disease Multi-region Prediction Based on Seq2Seq

PONE-D-25-08806R1

Dear Dr. zhu,

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

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #3: Yes

**********

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

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The author has effectively addressed the relevant questions, enriched the experimental section, and revised the results section, making the article more persuasive.

Reviewer #3: After thoroughly evaluating the revised manuscript and the authors' comprehensive responses, I am pleased to recommend that this paper be accepted for publication.

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7. 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: Yes: Chao Duan

Reviewer #3: No

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