PCOMPBIOL-D-26-00663

The pitfalls of incidence-based time series regression for inferring the effects of weather on infectious diseases

PLOS Computational Biology

Dear Dr. Gemo,

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Nicholas Geard

Academic Editor

PLOS Computational Biology

Jennifer Flegg

Section Editor

PLOS Computational Biology

Additional Editor Comments (if provided):

Thank you for your submission. The reviewers agree that your manuscript addresses a novel and important topic and has potential to be of interest to the PLOS Computational Biology readership. However, they identified several areas where revisions are needed to improve the manuscript's accessibility, clarity and impact. In particular, we ask that you address the following key points:

1. Please expand the background section to provide clearer context on time series regression models and how they are currently used in infectious disease modelling. This will help ensure the work is accessible to a broad readership.

2. Please include a discussion of how causal inference approaches could inform the use of time series regression models in this context.

3. Please consider moderating the strength of your claims where appropriate and/or provide additional references and justification to support them.

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1) Please ensure that the CRediT author contributions listed for every co-author are completed accurately and in full.

At this stage, the following Authors/Authors require contributions: Pietro Gemo, Laura Andrea Barrero Guevara, Cana Kussmaul, Sarah C. Kramer, and Matthieu Domenech de Cellès. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: Summary: This paper addresses an important topic in understanding the dynamics of seasonal outbreaks, particularly when incorporating the influences of weather, which also exhibit strong seasonal patterns. The simulation framework, where the authors can specify the true effects of climate on transmission and then assess whether time-series regression (TSR) can recover the modeled effects, is a strong approach. They find that TSR methods struggle to recover true weather effects on “observed” (albeit simulated) outbreaks, particularly depending on the climate variable or amount of measurement noise.

Major comments:

- The introduction would benefit from additional discussion of why TSR approaches may be inappropriate in this setting, including more explicit reference to the assumptions these models rely on and why are they are violated in infectious disease dynamics. The use of TSR methods in infectious disease dynamics is nuanced and would benefit from addition intuition-building in the manuscript. In particular, incorporating a discussion of causal inference frameworks (e.g., DAGs) and how feedback mechanisms and temporal dependence complicate causal interpretation would strengthen the paper. I suggest at minimum including the following relevant papers

- Ackley SF, Mayeda ER, Worden L, Enanoria WTA, Glymour MM, Porco TC. Compartmental Model Diagrams as Causal Representations in Relation to DAGs. Epidemiol Methods. 2017;6(1):20060007. doi:10.1515/em-2016-0007

- Cobey S, Baskerville EB. Limits to Causal Inference with State-Space Reconstruction for Infectious Disease. PLOS ONE. 2016;11(12):e0169050. doi:10.1371/journal.pone.0169050

- The manuscript describes the framework as disease-agnostic; however, section 3.1.1 specifies a weather-dependent transmission relation based on work in respiratory viruses. If this assumption is central to transmission across all models - which it appears to be - it should be acknowledged when interpreting the results. Alternatively, the authors could provide justification for extending this relationship to other pathogens or consider exploring alternative transmission models

- The poorer performance observed in the tropical location is interesting. This may related to nonlinear relationships between climate factors and transmission documents in tropical settings (e.g., Yuan H, Kramer SC, Lau EHY, Cowling BJ, Yang W. Modeling influenza seasonality in the tropics and subtropics. PLOS Computational Biology. 2021;17(6):e1009050. doi:10.1371/journal.pcbi.1009050 for influenza). Since the 4 locations were selected to represent different climate contexts, it would be more valuable to explicitly connect the differences in results to the underlying climates reflected in the study areas

Minor comments:

- In section 3.1, the term, “economical epidemic model” is unclear and should be defined or replaced and consistent with the legend in Figure 1 (which refers to it as causal)

- Please clarify the spatial resolution of the weather data and how it aligns with the selected locations

- In section 3.3.1, the phrase, “capturing direct causal effects” may be overstated. While the model described is mechanistic and deterministic, additional justification is needed to classify it as causal

Reviewer #2: Please see attached

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: PCOMPBIOL-D-26-00663-reviewer-response.pdf

Dear PhD Candidate Gemo,

We are pleased to inform you that your manuscript 'The pitfalls of incidence-based time series regression for inferring the effects of weather on infectious diseases' has been provisionally accepted for publication in PLOS Computational Biology.

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

Best regards,

Nicholas Geard

Academic Editor

PLOS Computational Biology

Jennifer Flegg

Section 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 #2: I thank the authors for their thorough consideration of reviewer and editor comments and subsequent revisions to the manuscript. The resubmission is much improved in terms of readability and accessibility for a more general audience, and in terms of the authors justifying their study design and/or discussing the limitations. I appreciate the addition of the sensitivity analysis as well.

I believe the authors have addressed all of my major concerns in a considered and comprehensive manner. As a suggestion, if the authors wanted to add some subheadings to the now quite long Discussion section it would help improve readability, however that is merely a suggestion and entirely optional on their part. I have no additional changes to suggest.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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Reviewer #2: No