Peer Review History

Original SubmissionAugust 9, 2023
Decision Letter - Olivier Damette, Editor


Use of machine learning tools to predict health risks from climate-sensitive extreme weather events: A scoping review.

PLOS Climate

Dear Dr. Ssebyala,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’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.

Please submit your revised manuscript by Dec 07 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at When you're ready to submit your revision, log on to and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Olivier Damette

Academic Editor

PLOS Climate

Journal Requirements:

Please 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 If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references.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 If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Climate’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes


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

Reviewer #1: I don't know


3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. 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


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

PLOS Climate 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


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: This is an interesting review. The use of machine learning tools to predict the health risks of climate-sensitive extreme weather events is a promising research direction. The authors have screened and synthesized relevant studies and discussed the current status of the research deficit as well as the future outlook. However, the article still has some problems at present, mainly:

1. Table 1 lists the studies sorted by author's last name, which makes the reading lack of clear logical levels, and suggests that the authors consider sorting the studies in terms of research time and so on.

2. In the machine learning methods of the earliest regression model and the newer RF and other black-box models in the principle and effect of there are big differences, this article should be more differentiated and discuss the different methods.

3. the results of the part looks a little too simple, the authors divided into several aspects of the research classification, but only on the results of the list, not in-depth discussion of the differences between the studies, such as the definition of the heat wave is different whether there is a corresponding basis for the different models of the effectiveness of the advantages and disadvantages of different models, the validation of the model of the differences between the different studies and so on.

4. The article mentions that two studies have incorporated a series of other factors to construct the model, but it does not discuss them further, and needs to be supplemented with an analysis of whether the incorporation of other factors is meaningful to the construction of the model.


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Reviewer #1: No


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Revision 1

Submitted filename: Rebuttal_letter_ PCLM-D-23-00174.docx
Decision Letter - Olivier Damette, Editor

Use of machine learning tools to predict health risks from climate-sensitive extreme weather events: A scoping review.


Dear Ms Ssebyala,

We are pleased to inform you that your manuscript 'Use of machine learning tools to predict health risks from climate-sensitive extreme weather events: A scoping review.' has been provisionally accepted for publication in PLOS Climate.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team. 

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.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they'll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact

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

Best regards,

Olivier Damette

Academic Editor

PLOS Climate


Reviewer Comments (if any, and for reference):

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