PCLM-D-25-00277

Rainfall-driven disruptions in respiratory epidemic surveillance: evidence from COVID-19 in France and New York State

PLOS Climate

Dear Dr. Eugenio Valdano,

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

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Climate’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: 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: No

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3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data 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—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data 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—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: Dear Authors,

I have reviewed the manuscript titled: "Rainfall-driven disruptions in respiratory epidemic surveillance: evidence from COVID-19 in France and New York State". This is a novel concept, presented vary rationally. By and large the mascipt is well written. I have few comments that i think should be considered by the authors.

ABSTRACT-

> This is a really important finding that rainfall hinders surveillance. that's a public health problem! To make the paper hit even harder, just slip in a quick phrase about why you think people aren't getting tested. Is it mainly that it's too much hassle to get to the clinic in the rain (disrupted access), or are they just staying home more so they feel less likely to have been exposed (behavioral change)? A little nod to the assumed mechanism would make your conclusion feel even more rock-solid.

INTRODUCTION-

> since the study's focus is uniquely on testing rates, the motivation for this specific outcome measure could be stronger. The authors should explicitly state why reduced testing is a critical, separate concern from reduced transmission—for example, by emphasizing that reduced testing leads to a critical loss of situational awareness for public health authorities, hindering timely response and intervention despite any potential drop in incidence. This will reinforce the paper's novel contribution to the literature, which goes beyond weather's impact on contact rates alone.

> The Introduction effectively sets up the study by discussing how extreme weather disrupts behavioral patterns and public health responses. To enhance the paper's significance regarding social-equity, add a study that directly addresses how climate events impact vulnerable groups' access to diverse forms of healthcare. This is a exhaustive review article. This connects your finding (reduced COVID testing, especially in poorer areas) to a wider, systemic issue—that weather-driven disruptions compound socioeconomic and gender-based vulnerabilities across various health domains. This strengthens the public health relevance of your paper beyond just respiratory epidemics.

Afzal F, Das A, Chatterjee S. Drawing the linkage between women’s reproductive health, climate change, natural disaster, and climate-driven migration: Focusing on low-and middle-income countries-a systematic overview. Indian Journal of Community Medicine. 2024 Jan 1;49(1):28-38.

METHODS

> the four model choices tested for $O(t)$ (trend only/trend + weekly, additive/multiplicative) are a crucial decision. The authors should briefly justify, in the final text (perhaps in the Results section), why the AIC-selected model's structure (e.g., multiplicative with weekly effects) makes the most conceptual sense in a public health context. For instance, does a multiplicative weekly effect mean testing is proportionally reduced on weekends regardless of the current trend? Briefly commenting on the chosen model's public health interpretation would strengthen the validity of the final $\Delta$ estimates.

> For the French data, daily rainfall was a simple average of individual weather stations within a department. Given the highly localized nature of rainfall and the large size of departments, did the authors assess whether using a weighted average (e.g., weighted by population density or proximity to testing centers) would produce different results? Please clarify.

RESULTS

> Ensure all figure no. and table numbering are in sequence and are proper, and Citations of tables and figures are correct.

DISCUSSION

> In the limitations, specify the p-value or confidence interval of the New York State income-effect correlation (even if non-significant) to demonstrate the weak result, or state the magnitude and direction of the effect in NYS to allow for qualitative comparison with France.

> To really show how bad the problem is, you should bring in the study by Afzal et al. (2020) on low COVID awareness in places like rural India. Your paper shows that the rain hits poorer areas hardest, and this study adds a huge point: when a health crisis (like low COVID awareness) mixes with a climate problem (like rainfall), communities that are already struggling face a double punch when trying to get care. This helps prove your point that we need to think about all these different types of vulnerability together.

Afzal F, Siddiqui R, Khan MR, Afzal M, Usmani N. COVID-19-a public health emergency: what do we know? A cross-sectional study on community awareness level towards COVID-19 in Uttar Pradesh, India. Int J Commun Med Public Health. 2020 Nov;7(11):4562-9.

> You can offer a smart, practical solution by suggesting that public health should use AI-powered tools. Mention the work by Bhola et al. (2023), which successfully used a COVID chatbot for screening and counseling. You can suggest that these kinds of AI systems should be upgraded to include real-time rainfall and weather data. That way, if it's pouring rain, the AI can instantly guide people to the closest and most accessible testing site, or suggest a different option, directly solving the "getting there" problem you identified.

Bhola C, Afzal F, Kumar SV. Identification of Predictors for Utilization of Artificial Intelligence Powered COVID-19 Chatbot for Self-Screening and Health Counselling. Indian Journal of Science and Technology. 2023 Aug 28;16(32):2540-7.

> I suggest you split the last two/three paragraphs of your Discussion into a new, separate section titled "Conclusion." Giving these powerful final points their own dedicated section will follow standard academic formatting, make your study's public health impact stand out, and ensure your critical final message is clearly emphasized for the reader. Please check few recent papers published by the journal and the journal template guidelines.

CONCLUSION

> Comment mentioned above. Please

Kindly make the above suggested changes and resubmit for next round of review.

Best of Luck.

Reviewer #2: 1. Visual Integration of Figures 1 and 2 are presented well, but they are mentioned after several results. For example, the text refers to Fig. 1a-b for the time window and Fig. 1c-f for the Prophet fit results. It would be clearer to mention these figures earlier in the text.

2. The data availability statement for this statement needs to be checked against the journal's guidelines. It lists the sources, but it should directly confirm that all data are publicly available and without restrictions.

3. In the Discussion section, the claim that the larger effect seen in France suggests that New York residents "may be accustomed to more frequent and heavier rainfall" is important. Although weather profiles support this claim, it would help to reference other literature about how different regions adapt to weather events.

4. It is recommended for the authors to emphasize how infectious disease, climate, and public health technology connect, especially regarding preparedness and monitoring. The recommended reference from the paper: “COVID-19 Pandemic: A Worldwide Critical Review with Machine Learning Model-based Prediction”. This paper highlights the role of Machine Learning (ML) in combating the COVID-19 outbreak, including for diagnosis, treatment, and impact prediction. It can be used in the Introduction when discussing the importance of data and models during the pandemic, or in the Methods to contextualize the use of the ML-based Prophet model

5. The paper presents that rainfall "can reduce the uptake of diagnostic testing, delay the detection of infections, and amplify the disadvantages faced by vulnerable populations." This is a key idea of the paper and should be connected to earlier work, if it exists. discuss how snow, rain, and floods affect transmission, but a clear link to how these factors disrupt surveillance is needed.

6. The geographic resolution is referred to as Admin-2 (departments in France and counties in New York). The text mentions averaging rainfall data from stations within the same department for France. It would be helpful to briefly clarify how COVID-19 testing data in France was reported, especially if it was at the department level, to ensure consistency.

7. The discussion about adaptation is insightful. The finding that "a larger average yearly number of rainy days was associated with a smaller reduction in tests" should be clearly linked to Figures 2b and 2f to support the argument.

Reviewer #3: Dear Authors,

Thank you for submitting this timely and important study. Your findings on how rainfall reduces COVID-19 testing, particularly amplifying vulnerability in low-income areas, represent a valuable scientific contribution to the climate and public health field. Your statistical methods are robust and appropriate, and the work is technically sound.

Therefore, my recommendation for your manuscript is Minor Revisions.

I find two major strengths that make this work highly suitable for PLOS Climate, high Novelty and Relevance,the core finding-that rainfall consistently reduces diagnostic testing, amplifying existing vulnerabilities is a novel and highly significant contribution to the intersection of climate change and epidemic surveillance.

Strong Scientific Method,the application of advanced models (Poison time series regression) incorporating weekly seasonality and socioeconomic factors across two distinct geographies (France and New York) provides a robust and credible foundation for the conclusions.

I recommend you focus on the following issues, which need clarification or slight modification;Regarding Causality: Since your study is observational (not experimental), it cannot definitively claim that rain causes the reduction in testing. Please slightly temper the causal language in the Abstract and Conclusion, focusing more on association rather than absolute causality. This adiustment will increase the paper's scientific accuracy.

The Discussion explains why the percentage reduction in tests is larger in France (~20%) compared to New York (~9%). Please add a stronger sentence in the Final Conclusion emphasizing the practical implications of this significant regional difference for public health preparedness.

Clarity for Future Research and please specify in the Discussion or Conclusion which types of regions future research should prioritize to make the international applicability of your work clearer.Addressing these points will prepare your manuscript for publication. This is excellent work.

Reviewer #4: The manuscript examines the intersection of climate and health surveillance by analyzing the influence of rainfall patterns on diagnostic testing activity during the COVID-19 pandemic in France and New York State. This topic is well aligned with PLOS Climate’s mission to advance understanding of climate impacts on human systems, particularly in the context of public health. The paper is clearly written, the data are accessible through public repositories, and the motivation is well situated within the climate and infectious disease literature.

However, significant methodological limitations currently preclude firm conclusions. The use of simple Poisson regressions without adjustment for serial and spatial correlation likely underestimates uncertainty. The Prophet-derived offset may introduce post-treatment bias because it depends on COVID-19 case trends, which could themselves be influenced by weather. Additionally, important covariates such as mobility, testing policies, and holidays were not included, which limits the ability to draw causal inferences. The transformation from regression coefficients to percent changes also appears to be incorrect (Δ = 100·(e^{25α} − 1) should be used), affecting the accuracy of reported magnitudes. Addressing these issues would enhance both the internal validity and robustness of the results.

The findings are potentially significant but should be presented more cautiously as associative rather than causal. Future iterations would benefit from the application of distributed-lag or negative-binomial models, corrections for spatial dependence, and sensitivity analyses that incorporate behavioral and policy variables. The manuscript is clearly presented, and it meets PLOS Climate’s requirements for data and ethical transparency.

Recommendation: The study is promising and within the journal’s scope; however, in its current form, it does not meet the standards for methodological rigor. Substantial re-analysis and clarification are required before it can be considered for publication.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Aylin GhasempourAylin GhasempourAylin GhasempourAylin Ghasempour

Reviewer #4: No

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Rainfall-driven disruptions in respiratory epidemic surveillance: evidence from COVID-19 in France and New York State

PCLM-D-25-00277R1

Dear Eugenio Valdano,

We are pleased to inform you that your manuscript 'Rainfall-driven disruptions in respiratory epidemic surveillance: evidence from COVID-19 in France and New York State' has been provisionally accepted for publication in PLOS Climate.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they'll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact climate@plos.org.

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

Best regards,

Cheng He, Ph.D.

Academic Editor

PLOS Climate

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Additional Editor Comments (if provided):

Reviewer Comments (if any, and for reference):

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->?>

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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4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data 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—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data 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—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Dear Authors,

I have reviewed the revised draft of the manuscript titled: "Rainfall-driven disruptions in respiratory epidemic surveillance: evidence from COVID-19 in France and New York State".

All reviewer's comments are addressed. I recommend this draft should be ACCEPTED and considered for publication.

By and large the manuscript is well written and focuses on important knowledge gap.

Best of Luck.

Reviewer #2: I recommend the manuscript for acceptance. The authors have revised the manuscript in response to the previous evaluation. They have addressed every comment and technical concern raised by the reviewers. The current revised version meets the journal's high standards.

1. The authors thoroughly responded to reviewer questions, showing dedication.

2. The manuscript is clearer and better organized, especially with new figures and a conclusion.

3. Methods are more robust, including spatial mixed-effects Poisson models and GEE for spatial and temporal correlations.

4. The study uniquely examines environmental factors like rainfall influencing epidemic surveillance, a vital but overlooked public health area.

5. Results improved through better data analysis, including population-weighted rainfall averages and relevant factors like holidays.

6. Overall rigor increased with clearer causality language and a broader climate adaptation discussion.

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what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

Reviewer #2: No

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