-->PONE-D-26-04548-->-->Beyond traditional outbreak investigation: using genomic data for enhanced detection of COVID-19 disease clusters in Utah-->-->PLOS One

Dear Dr. Jewell,

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PLOS One

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Reviewers' comments:

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-->Comments to the Author

1. 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

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Partly

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

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-->3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

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-->4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

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-->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: Good provides accurate statistical data and is according to ethics and all data is verified and is valid for publication.This discuss about the people affected by the covid 19 and people vaccinated from covid 19

Reviewer #2: A small elaboration on the clinical relevance or significance of this method and also the implication could add to the knowledge and could also find wider acceptability among all categories of health care workers.

Reviewer #3: This study successfully combines genetic data with established epidemiological methods to discover COVID-19 clusters in Utah. The strengths are the large dataset (nearly 700,000 cases, with over 22,000 sequences analyzed), the use of multiple clustering approaches (SNP thresholds and logit regression), and the clear demonstration that genomic methods provide higher biological plausibility and more refined cluster definitions than manual case investigation alone. Importantly, the analysis identifies differences in sequencing coverage between jurisdictions and racial/ethnic groups, emphasizing the importance of equitable sampling procedures.

While the conclusions are strongly supported, the limitations, particularly poor sequencing coverage and diversity in epidemiologic cluster definitions, should be highlighted more, as they affect generalizability.

Reviewer #4: The manuscript id well-structured with vigorous analysis done and data is also well structured.

The authors have done a nice work in explaining the trend in COVId-19 and also the statistical methods.

Figures are upto the mark.

Reviewer #5: 1. The manuscript addresses an important and timely public health question regarding the integration of genomic epidemiology with traditional outbreak investigation for COVID-19 cluster detection.

2. A major strength of the study is the large dataset, including approximately 697,000 epidemiologic records and more than 22,000 SARS-CoV-2 genome sequences, which provides strong statistical power and statewide representation.

3. The use of multiple genomic clustering approaches, including SNP threshold clustering and the cov2clusters logit regression model, strengthens the methodological framework and allows meaningful comparison between genomic and epidemiologic cluster definitions.

4. The inclusion of multiple validation metrics, including silhouette scores, Adjusted Rand Index (ARI), Variation of Information (VI), and monophyly analysis, improves the rigor of the clustering evaluation.

5. The findings convincingly demonstrate that genomic clustering methods provide additional resolution compared to traditional epidemiologic investigation and may help refine outbreak definitions.

6. The examples involving nursing homes and correctional facilities are particularly informative and effectively demonstrate the practical value of genomic surveillance in outbreak investigations.

7. The rationale for selecting SNP thresholds (1–3 SNPs) and probability thresholds (0.8 and 0.9) should be explained in greater detail, particularly regarding their applicability across Alpha, Delta, and Omicron periods.

8. The manuscript should discuss more critically the impact of incomplete sequencing coverage on clustering accuracy and interpretation, especially since only a small proportion of epidemiologic clusters had complete sequencing coverage.

9. Additional sensitivity analyses using alternative clustering thresholds or parameters would strengthen the robustness and reproducibility of the conclusions.

10. More methodological detail is needed regarding regression assumptions, handling of missing data, assessment of multicollinearity, and whether corrections for multiple testing were considered.

11. Several figures, especially phylogenetic cluster comparisons, would benefit from clearer annotations and more detailed legends to improve accessibility for readers unfamiliar with genomic epidemiology.

12. The manuscript is generally well written and understandable; however, portions of the Discussion are repetitive and could be streamlined to improve clarity and readability.

13. Minor grammatical, stylistic, and formatting revisions are recommended to improve overall presentation quality.

14. Overall, this is a valuable and technically sound study that contributes meaningfully to the field of genomic epidemiology and public health surveillance. The manuscript would be strengthened further after moderate revision addressing the points above.

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Reviewer #1: Yes:  Kunal Joon

Reviewer #2: Yes:  Dr. Bineeta Kashyap

Reviewer #3: Yes:  Venkataramana Kandi

Reviewer #4: Yes:  Dr. Asiya Khan

Reviewer #5: No

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Beyond traditional outbreak investigation: using genomic data for enhanced detection of COVID-19 disease clusters in Utah

PONE-D-26-04548R1

Dear Dr. Jewell,

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,

Benjamin M. Liu, PhD, D(ABMM), MB(ASCP)

Academic Editor

PLOS One

Additional Editor Comments (optional):

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