PONE-D-19-34966

Robust Two-stage Influenza Prediction Model considering Regular and Irregular Trends

PLOS ONE

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Tzai-Hung Wen, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

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: The manuscript entitled “Robust Two-stage Influenza Prediction Model considering Regular and Irregular Trends” attempt to integrate time-series analysis and machine learning approaches to capture both regular trend and irregular patterns of influenza outbreaks. This is a well-written article and the result shows a promising direction for outbreak prediction of influenza.

Below are major issues

1. Page 5, Lines 131. Is there any reason to support the selection of AR(26)?

2. Figure 4. When comparing the difference values of each model, ARGO demonstrated a different direction of the deviance comparing to two-stage model and Random Forest approach. Is there any interpretation for the phenomena?

3. The result and discussion mainly focus on the performance comparison of different models. The content of discussion seems missed in this part. It’s better to add more detail discussion with previous literature in the manuscript.

Reviewer #2: The manuscript entitled ”Robust Two-stage influenza Prediction Model considering Regular and Irregular Trends” constructed a new Two-stage model to make real-time influenza forecasts based on the surveillance data and the google trend data in US and Japan. The manuscript is well organized, and the purpose of the work is clearly defined. Although the rationale of the approach presented is generally understandable, some points should be further clarified and described well.

My major concern is about the methods. Methods section lacks enough description of the main procedures of the Two-stage model. Very few words were used to describe the model. Author said “We designate the combination of the 1st Model and the 2nd Model as the Two-stage Model”, but how were these 2 models combined isn’t described. More details are needed to illustrate how to combine the two model’s results to obtain the final prediction value. Does the proposed approach only add the two models’ results? Or with any weights?

Also, how model 2 works with model 1’s outcome is not described. Eq(2) is not enough to understand how ILI rate from model 1 outcome can be used for model 2. Although “xt and ot-1 denote a current input and previous output vectors…” is described, what actual data are x and o, and how these variables correspond to ILI rate results from model 1.

Other minor comments are

- for Introduction

The introduction section should look like a concise summary of the research topic. I suggest the authors could show some previous studies' outstanding achievements based on the hospital surveillance data and UGC data (e.g. the quantity comparison of forecasting results based on the historical data and UGC data) rather than only explain the datasets.

On Page2, Ln 19, cite some particle filtering approaches done by Yang W and Shaman J for influenza forecasting.

On page 2, Ln 42, “We also think that our model is strong against noise and outliers in data”. Please change the work “think” to other words and describe your analytical results to support your statement. People think is not enough to support this conclusion.

- for Methods

What are the search queries used? Letting readers know these, e.g., by including it in supplementary materials, would be good.

Additionally, the merits of choosing ILI rate for US data and ILI patients for JPN data is not very clear to me.

Some variables in eq(2) are not expounded and need to be so.

- for Results

The mechanism shown as Figure 2 should be described in more details. I assume the solid black line represent the previous historical data. Then author should describe all the remaining dots (2 blue dots, one black, one red) following the order of the steps. For example, does left blue dot (prediction from the 1st model) occur before the above black dot. How is the 2nd blue dot predicted? And how is the red dot predicted? Why there is not red dot above the 1st left blue dot? Please describe the scenario more.

In Table 1, why the periods in training using Model1 and Model2 are different. Should both models use the same periods for training?

- for Robustness

The values given in parentheses are the difference of accuracy scores between the original data and processed data with outliers processed data. Please describe more details what is processed data and how did author process the data.

- for Conclusions

- on the sentence "The model presented herein might not work well in some countries": if the authors decide to include this sentence, they should preferably give clear reasons why and how the model can be made to work in such countries.

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Reviewer #1: No

Reviewer #2: No

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Robust Two-stage Influenza Prediction Model considering Regular and Irregular Trends

PONE-D-19-34966R1

Dear Dr. Murayama,

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

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With kind regards,

Tzai-Hung Wen, Ph.D.

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 #2: 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 #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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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: (No Response)

Reviewer #2: (No Response)

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Reviewer #1: No

Reviewer #2: Yes: Hsiang-Yu Yuan