PONE-D-22-31484A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptomsPLOS ONE

Dear Dr. Richard, 

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Academic Editor

PLOS ONE

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Reviewer #1: Yes

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

Reviewer #1: Yes

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Reviewer #1: No

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Reviewer #1: Yes

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5. Review Comments to the Author

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Reviewer #1: Authors have presented an important piece of information in understanding symptom patterns of the Covid-19 and likely clinical course. I have few suggestions for consideration of the authors

1. It will be helpful for international readers if it is clarified that the beneficiaries included/not included the dependent family members of the military. Also, in the table 1, the occupation might be helpful, as a predominant in-service participants might have a different level of pre-illness physical fitness

2. It is also noted that only half of the enrolled participants completed FLU-PRO Plus survey, and further around half of these participants responded to the survey at six month. Therefore, authors may present some demographic of all enrolled participants and clinical characteristics of participants who completed the first survey, for benefit of readers to be able to compare the participating and non-participating enrollees. This may be added as supplement.

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Reviewer #1: No

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A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms

PONE-D-22-31484R1

Dear Dr Richard,

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,

Dr Gayathri Delanerolle

Academic Editor

PLOS ONE