PCLM-D-23-00174

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 climate@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pclm/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Olivier Damette

Academic Editor

PLOS Climate

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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

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

Reviewer #1: I don't know

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Reviewer #1: Yes

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Reviewer #1: Yes

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5. Review Comments to the Author

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

PCLM-D-23-00174R1

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.

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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

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Reviewer Comments (if any, and for reference):