Dear Mr. Abbood,

Thank you very much for submitting your manuscript "EventEpi–A Natural Language Processing Framework for Event-Based Surveillance" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Benjamin Althouse

Associate Editor

PLOS Computational Biology

Virginia Pitzer

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: See attached.

Reviewer #2: Dear Authors,

Congratulations for your hard work.

It is well written. Additional information should be provided to help public health experts not familiar with natural processing language algorithms be able to judge the presented results.

From a public health point of view I have the following comments, questions and proposed modifications to the text:

General:

• Please use “Public health surveillance” instead of “Infectious disease epidemiology” or “epidemiological surveillance” in the context of this paper.

• Avoid the use of NLP acronym, consider using the full term “natural language processing” across the text?

• Please define each acronym at least once (for example TPR: true predictive ratio).

• Specify what you mean by “disease”. Is this limited to laboratory specific diseases such as measles, cholera or yellow fever; or it also includes syndromes such as cutaneous rash, watery diarrhoea, jaundice? This is especially important for EBS, as its purpose is mainly to detect unknown or unexpected diseases that cannot be well captured by the routine data reporting performed by healthcare facilities.

Author summary:

• What did this research do and find: 4th point (last): misleading sentence, as mentioned in the results only countries and diseases were correctly detected, not dates or counts.

Introduction

• Line 3 – 4: One of the most important goals of "Public health surveillance" is the "timely" detection and response to an acute public health event; the other being to monitor the health status of the population to drive health policy. In this paper, early detection and response is the topic of interest, yet public health surveillance cannot be reduced to that.

• Lines 14-15: “traditional surveillance” relies on “routine reporting from healthcare facilities” and not from “laboratory confirmation” (most cases are not laboratory confirmed in many settings and for many diseases).

• Lines 20-22: add “routine” in the sentence: “It enables public health agents […] recognition of human cases in the **routine** reporting system”.

• Line 29: why the use of the word “precision” instead of “specificity”?

• Lines 45-46: why only “confirmed-case count”? And not counts of suspect cases, for example?

Figure 1

• Don't use the light "pink-orange" background for boxes as it is confusing with the orange part of the figure describing the relevance scoring.

• Define briefly "word2vec", "EpiTator", "SVM", "kNN", "LR" either in the figure or in its description.

• To facilitate understanding, maybe consider splitting the "supervised learning" row in two: the classification phase (relevance scoring), and the identification of the appropriate data (key information extraction). And use the terms "relevance scoring" and "key information extraction" in the figure.

Material and methods:

• Add a section to detail how you assessed the performance of the key information extraction and of the classifiers, this would encompass among other lines 85-89, 267-273 in the methods, and some paragraphs from the results, for example lines 288 to 295.

• In the above proposed section, please add a quick description of all indicators used to assess each extraction and classification method (i.e. indicators described in the results and in tables 2 to 4).

• For epidemiologists the term “sensitivity” will be clearer than “recall”.

• Lines 79-80: if correct, add “in each class” in the following sentence: “However INIG, as other […] in the keys entitities *in each class*”.

• Lines 120-121: you mention the problem of events/incidents involving several countries but you don’t specify how you solved it (for example to have an accurate number of cases for each country). Please specify how many events/incidents it concerned and how you solved this problem. Why didn’t you remove these events for training the algorithm?

• Table 1: please specify what a “sample” is; and what is considered “positive” or “negative” samples.

• Lines 159-161: if I understood well, the key information for a single class is the recognized entity from the sentence with the highest probability. How did you deal with sentences having more than one recognized entity for a single class?

• Please make clear the number of articles used in your samples:

◦ As I understand, you had 3232 articles, 160 relevant (included in the IDB) and 3072 irrelevant.

◦ And then you tested your classifiers with 20% of your sample, please provide figures of relevant and irrelevant articles used to test your classifiers.

◦ As I understand, you had two classes: relevant or irrelevant article. If I am correct, please make it clear, including in the sections related to the test of the classifiers.

Results:

• When you say all countries and diseases (except one) were correctly recognized, please provide the figures; i.e. for how many articles presents in the IDB country and disease were correctly extracted. Same for dates and counts.

• Lines 299-302: not clear what the results related to EpiTator and the ones related to the most frequent approach are.

• Please also provide figures for table 4. For example, for each classification modality, how many documents were classified as relevant and not relevant, how many were truly relevant and how many truly not relevant?

• Similarly, it would be good to know if other incidents not logged in the IDB but still of interest were identified using the automatized screening approach, i.e. documents identified as “relevant” by the classifier, that were not in the IDB, but that should have been after a review by a public health expert.

Conclusion:

• The most important added value of the tool would be to pre-screen large amounts of data to identify a sample that would be then manually screened by public health experts.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: None

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Reviewer #1: No

Reviewer #2: Yes: Jose Guerra

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

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Attachments

Attachment

Submitted filename: Review for PCOMPBIOL-D-19-01790.pdf

Dear Mr. Abbood,

Thank you very much for submitting your manuscript "EventEpi–A Natural Language Processing Framework for Event-Based Surveillance" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please address reviewer 2's very minor points.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. 

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Benjamin Althouse

Associate Editor

PLOS Computational Biology

Virginia Pitzer

Deputy Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Please address reviewer 2's very minor points.

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I am satisfied that the authors have addressed the concerns highlighted in the review. I also congratulate them on making the data accessible for future research.

Reviewer #2: Dear Authors,

Thank you for your impressive work in the improvement of the paper, it looks very good now.

I am fully satisfied with your explanations to my questions and the modifications performed to the manuscript.

Please find below some very minor comments and proposed modifications, feel free to consider them or not.

Introduction:

* Line 40: typo, "spent" instead of "spend".

Results:

* Tables 1 and 2: the term "support" is still not very clear, even with the added explanation, maybe you could consider the use of another term such as "sample used".

* The confusion matrices are very good and self-explanatory, I strongly believe they should be part of the main manuscript instead of being in the supplementary material.

S1 Appendix:

* line 554: typo "classification" instead of "classifcation"

Best regards

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: None

Reviewer #2: None

**********

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: No

Reviewer #2: Yes: José Guerra

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methods

Dear Mr. Abbood,

We are pleased to inform you that your manuscript 'EventEpi–A Natural Language Processing Framework for Event-Based Surveillance' has been provisionally accepted for publication in PLOS Computational Biology.

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

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.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

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

Best regards,

Benjamin Althouse

Associate Editor

PLOS Computational Biology

Virginia Pitzer

Deputy Editor

PLOS Computational Biology

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