Meteorological Drivers of Influenza A and B Positivity in a Subtropical Chinese City: A Six-Year Surveillance Study Integrating Distributed Lag Non-Linear Models and Deep Learning

Reviewer #2 (Public review):

[Editors' note: The Reviewing Editor has assessed the revised article without further input from the original reviewers. The Reviewing Editor noted the authors further addressed a methodological concern, and eLife's Assessment remains unchanged from the previous review.]

Summary:

The study aimed to assess the associations between meteorological drivers and influenza is important although not new. The authors used 6 years of surveillance data and deep learning models, combining distributed lag non-linear models (DLNM) with Bayesian-optimized LSTM neural networks for predictive modeling. The key interest in this area is to explore the subtropical locations, where influenza is less common and circulates year-round. The authors further claimed that such an association could be able to provide an early warning in the community.

Strengths:

Study design based on a prospective cohort to analyse the data for retrospective outcomes.

Author response:

The following is the authors’ response to the previous reviews.

Public Reviews:

Reviewer #2 (Public review):

Summary:

The study aimed to assess the associations between meteorological drivers and influenza is important although not new. The authors used 6 years of surveillance data and deep learning models, combining distributed lag non-linear models (DLNM) with Bayesian-optimized LSTM neural networks for predictive modeling. The key interest in this area is to explore the subtropical locations, where influenza is less common and circulates year-round. The authors further claimed that such an association could be able to provide an early warning in the community.

Strengths:

Study design based on a prospective cohort to analyse the data for retrospective outcomes.

We would like to express our sincere and heartfelt gratitude to all of you for your exceptionally thorough, constructive, and intellectually rigorous evaluation of our manuscript. The breadth and depth of the feedback we have received reflect a high standard of scientific scrutiny that we deeply respect and appreciate.