PONE-D-21-39547

Supervised learning using routine surveillance data improves outbreak detection of Salmonella and Campylobacter infections in Germany

PLOS ONE

Dear Dr. Zacher,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected.

Specifically:

ACADEMIC EDITOR: In the current paper, authors have presented supervised hidden Markov models for disease outbreak detection, which use reported outbreaks that are routinely collected in the German infectious disease surveillance system and have not been leveraged so far. This allows to directly integrate labeled outbreak data in a statistical time series model for outbreak detection. The novelty of the paper is limited and is not sufficient to be published in PLOS ONE journal. Hence I reject the paper.

I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision.

Yours sincerely,

Sriparna Saha, PhD

Academic Editor

PLOS ONE

Additional Editor Comments:

In the current paper, authors have presented supervised hidden Markov models for disease outbreak detection, which use reported outbreaks that are routinely collected in the German infectious disease surveillance system and have not been leveraged so far. This allows to directly integrate labeled outbreak data in a statistical time series model for outbreak detection. The novelty of the paper is limited and is not sufficient to be published in PLOS ONE journal. Hence I reject the paper.

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Reviewers' comments:

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PONE-D-21-39547R1Supervised learning using routine surveillance data improves outbreak detection of Salmonella and Campylobacter infections in GermanyPLOS ONE

Dear Dr. Zacher,

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

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PLOS ONE

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Additional Editor Comments (if provided):

Two reviews are consistent with the quality of the manuscript, so I suggest the author make minor changes by taking the suggestions of the two reviewers into account.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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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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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

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Reviewer #1: I have not reviewed an earlier version of this paper.

Overall I think that it presents a fair assessment regarding the question of whether using past outbreaks to allow supervised learning has the potential to outperform unsupervised learning which is the main purpose of the work.

The conclusion "“….our results are promising that leveraging outbreak data with supervised learning will improve disease outbreak detection.” seems fair although the will might be softened to may.

I don't think that the approach as presented offers a reason to change to this for those currently deploying other algorithms - and this is not claimed by the authors. More generally, in common with the comparator approaches, the fact of an outbreak being probable is returned but this is a limited benefit. Although done a lot, this is very limited information and, for example, doesn’t make clear which cases belong to the outbreak and which do not. In practice the capacity of genetic sequencing and analysis to accurately detect and characterise outbreaks in Salmonella in particular makes such approaches largely redundant for this use case. The authors make a fair hand at noting that what is in practice detected is limited.

One technical issue is that modelling an outbreak as multiplicative relative to background rates appears strange. A secular trend of increasing incidence or a seasonal peak period would then need a larger outbreak to be detectable than when baseline levels are low. An additive model for the outbreak term might be a better fit. This approach was also applied to the simulation with outbreaks size proportional to the route of the variance of weekly counts such that the signal to be detected also carried this unlikely premise. The discussion might consider this choice in simulation and analysis and the alternative of non-multiplicative relationships.

The idea of seeking what is special about an outbreak vs modelling aberration from normal is appealing conceptually. I would have thought it might end up performing equivalently mathematically but the authors findings suggest that it does not and may be better in practice as well as a better theoretical fit as per their conclusion pasted in above.

Reviewer #2: Zacher and Czogiel propose a supervised HMM method for detecting potential disease outbreaks in Germany. This method takes advantage of the routinely collected outbreak data as known hidden states for improving detection performance. The effectiveness of this method was verified in experiments. The manuscript was well written. This paper will be ready for publication if the following minor problem is addressed.

Page 1, line 17

"on par or better than" --> "on par with or better than"

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Reviewer #1: No

Reviewer #2: No

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Supervised learning using routine surveillance data improves outbreak detection of Salmonella and Campylobacter infections in Germany

PONE-D-21-39547R2

Dear Dr. Zacher,

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

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Kind regards,

Hong Qin

Academic Editor

PLOS ONE