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Does the introduction explain the objective of the research presented in the preprint?

Yes

The authors raise a significant question in public health and identify key limitations in existing infectious disease models. They then propose addressing these gaps through analysis of real-world data.

Are the methods well-suited for this research?

Highly appropriate

The authors combined three different modeling approaches to better reflect real-world infectious disease dynamics. Each approach presents a clear and practical predictive framework, which was supported by examples and code.

Are the conclusions supported by the data?

Highly supported

The article used recent data on COVID-19 cases, hospitalizations and deaths to build the model. The authors pointed out the limitations of the approach. They also conducted analyses under different scenarios and estimated uncertainty of the model.

Are the data presentations, including visualizations, well-suited to represent the data?

Highly appropriate and clear

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Very clearly

The article clearly explained each stage of the model. In the discussion section, the authors emphasized the importance of collaboration with the public health departments to refine the model and adapt it to evolving conditions.

Is the preprint likely to advance academic knowledge?

Highly likely

Would it benefit from language editing?

No

Would you recommend this preprint to others?

Yes, it’s of high quality

Is it ready for attention from an editor, publisher or broader audience?

Yes, as it is

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.