PONE-D-25-57696

Spatial patterns and environmental drivers of COVID-19 outbreaks, post-Omicron

PLOS ONE

Dear Dr. Stamper,

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Yury E Khudyakov, PhD

Academic Editor

PLOS ONE

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7. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

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Reviewers' comments:

Reviewer's Responses to Questions

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: Partly

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

Reviewer #1: I Don't Know

Reviewer #2: No

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

The PLOS Data policy requires authors to make all data 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 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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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: 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: The authors investigated the impact of climate patterns on COVID-19 outbreaks during the post-Omicron wave. The methods are appropriate, and the content is innovative. However, one point requires correction: the citation in Line 5 currently appears as “[?]” and should be fixed.

Reviewer #2: The manuscript analyzes U.S. state–level emergency department (ED) data (April 2022–March 2025) to describe post-Omicron COVID-19 seasonality and investigate associations with temperature and specific humidity using descriptive metrics (epidemic intensity and circular mean timing) and panel regressions/GAMs. It reports (i) a transparent latitudinal gradient in outbreak patterns and (ii) a U-shaped relationship between specific humidity and burden, with higher risk at both low and high values and lowest risk at intermediate humidity; temperature shows a more negative/monotone association overall. The analysis relies on public ED visit percentages from the CDC, ERA5 climate data, and state-level booster coverage, with figures summarizing temporal patterns and model-based response surfaces.

The topic is timely and suitable for PLOS ONE, and the dataset window (post-Omicron) is a strength. The manuscript offers a valuable post-Omicron perspective and clearly documents the geographic structure in COVID-19 ED patterns. However, several methodological decisions limit interpretability. With stronger attention to outcome denominators, serial/spatial dependence, bimodal timing, and potential confounders, in addition to fuller model transparency and robustness checks, the conclusions can be considered well-supported, and the claims about climate associations will be much more compelling and publication-ready.

Major comments

- Using the percentage of ED visits with a COVID-19 diagnosis is defensible for mitigating testing/reporting artifacts. However, it is sensitive to changes in the ED case mix (e.g., heat waves, injuries, RSV/flu surges) that alter the denominator independent of COVID-19 transmission. Please:

o Re-run key analyses with an incidence-style outcome (e.g., estimated COVID-related ED visits per 100k population, if derivable from the CDC series) and/or with the log ratio of COVID ED visits to a stable internal control (e.g., total respiratory visits).

o At a minimum, control for state-week total ED volume (or include week×region fixed effects) to absorb denominator shifts. This control is crucial for interpreting the U-shape at high humidity/temperature in southern states, where summertime non-COVID ED volume is large.

- EI is computed based on the mean weekly curve aggregated across April 2022 and March 2025. This collapses interannual variation (including variant waves) and may inflate smoothness. Please compute EI per COVID-year (Apr–Mar) by state, analyze variance across years, and show that the latitude relationship persists year by year. Sensitivity of results to this design choice should be presented (main text or Supplement).

- The paper summarizes timing with a single circular mean across years, but the distribution is explicitly biannual (summer and winter peaks). A single mean is not a sufficient statistic and can be misleading (means can land in troughs). Please report additional timing metrics: (a) circular dispersion, (b) separate peak weeks (summer/winter) per state/year, and (c) peak asymmetry (summer minus winter magnitude). Consider re-running the latitude regressions on these peak-specific outcomes.

- Equation (3) includes state, year, and week fixed effects—a good start—but more detail is needed. Please:

o Clarify whether two-way or three-way FE are included simultaneously (state FE + year FE + week-of-year FE). If week FE is common across years, it absorbs seasonality; then the climate effect is identified through deviations from the seasonal mean. Make this explicit and discuss implications.

o Justify bin choices (5°C, 3 g/kg) analytically (e.g., using information criteria or cross-validation) rather than aligning reference bins to visual troughs (risk of post-hoc selection).

o Show partial dependence plots with CIs and within-R² to emphasize within-state identification.

- For the GAM [Equation (4)], please specify the exact basis, knot placement, smoothing parameter selection (REML is noted), and present edf, χ²/F-tests, and residual diagnostics (ACF of residuals, concurvity).

- Weekly panel data will exhibit strong serial correlation. Clustering by state is necessary but not sufficient if residuals are highly persistent. Please report tests/plots of residual autocorrelation and repeat regressions with Driscoll–Kraay or Newey–West (panel) robust errors (or at least state-level AR(1) correction) to verify inference stability.

- Neighboring states share weather and mobility catchments; ignoring spatial autocorrelation can bias SEs and inflate significance. Perform Moran’s I on residuals and, if material, implement spatial HAC corrections or include region×week FE. Alternatively, estimate a spatial error model on state-level annual summaries as a sensitivity analysis.

- Summer/winter peaks in ED COVID percentages may be entangled with RSV/flu/enterovirus waves, school terms, AC/indoor time, wildfire smoke, or holiday periods. At a minimum, add controls for:

o Influenza and RSV ED indicators (or ILI/SARI proxies) by state-week.

o Mobility or policy stringency (the Discussion notes this as future work; it belongs in robustness today).

o School in/out of session indicators.

o Wildfire smoke (PM2.5) has affected recent summers, particularly those with smoke-affected conditions. Include these in sensitivity models to demonstrate that climate associations are not proxies for unmeasured seasonal behaviors.

- The booster covariate is treated as a single percentage; please specify the series (which booster definition? updated 2023/2024 formulations?), data cadence, and lag structure. Consider lagged vaccination and age-adjusted coverage, as state averages can mask significant age/uptake differences. Show that results are robust to alternative vaccination specifications and to inclusion of prior-wave burden as a proxy for immunity.

- The paper occasionally reads as if climate “drives” the dynamics of COVID-19. Given the observational design, please temper language to “is associated with” throughout the Abstract, Author Summary, Results, and Conclusion, and emphasize that climate may “modulate” transmission conditional on variant immune escape and behavior. The Discussion begins to do this, but still infers “amplify” in places. Tighten wording, please.

- Since Apr 2022 covers multiple Omicron sublineages with differing immune escape, please include period or variant-era indicators (or break the sample) and test for parameter stability (e.g., climate coefficients pre- vs post-XBB/BA.5 transitions). A figure that locates major lineage turnovers on the time axis would be helpful.

- The fixed-effects bins suggest a U-shape for humidity and a negative slope for temperature; the GAM surface then shows the highest predicted burden at the high-humidity/high-temperature corner for Florida and at cold/dry for Wisconsin. To avoid over-interpretation:

o Quantify uncertainty on the surface [e.g., contours of standard error of the mean (SEM) or bootstrap].

o Show the empirical distribution of (T, q) combinations (you plot a polygon of support; add heat-density so readers see where the model extrapolates).

o Report marginal effects of humidity at several temperature strata (and vice versa).

- Given literature heterogeneity (absolute vs. relative humidity, dew point, WBGT, humidex, specific enthalpy, UV), include a sensitivity table swapping relative humidity, dew point, and a solar/UV proxy (ERA5 clear-sky UV if available) to show that findings are not metric-dependent.

Minor comments

- Recommendations for figures:

o Fig 1: Note Wyoming data truncation explicitly in the caption.

o Fig 2: For heatmaps and grouped curves, include N(states) per subgroup and shaded uncertainty (e.g., state bootstraps).

o Fig 3: The caption has “Fig 3. Fig 3.” duplicated; fix. Overlay monthly points with error bars for Florida/Wisconsin and add a legend for the support polygon. Provide a color bar with units (log-percent ED COVID).

- Recommendations for reproducibility & data availability:

o The GitHub link and statement are helpful; please archive a frozen release (e.g., Zenodo or similar) with a DOI, include environment lockfiles (R version noted as 4.4.3; also pin package versions), and provide download scripts for the ERA5 and CDC series so that others can exactly reproduce your extraction window and state mappings.

o Clearly document which CDC endpoints were used, their as-of date, any backfill handling, and whether data revisions occurred during analysis.

- “N/A” is written in the Ethics Statement section. Because the analysis utilizes publicly available, aggregate surveillance data, add a sentence in the Methods section stating that the data are de-identified and publicly available, and that no human subjects review was required (cite the data sources).

- Ensure the funding statement appears verbatim in the manuscript Acknowledgments/Financial Disclosure as required by PLOS ONE.

- #5: Replace the “1.2 million deaths as of September 2025 [?]” citation placeholder with a verifiable source.

- Fix typos (e.g., “the the stringency index” in #191) and grammatical errors (e.g., “Data on COVID-19 vaccination data on the state level …” in #55 and #56 OR “becomes” in #128).

- In Materials and methods, clarify spatial aggregation: ERA5 0.25° grids → state-level averages (population-weighted or area-weighted?). Specify the state centroid source for latitude and whether Alaska/Hawaii were included or excluded.

- Please note missingness: e.g., Wyoming stopped reporting in 2025; describe how weeks with missing ED data are handled (listwise deletion? imputation?). Provide a completeness table by state×year.

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Reviewer #1: Yes: Yutaro Akiyama

Reviewer #2: No

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Spatial patterns and environmental influences of COVID-19 outbreaks, post-Omicron

PONE-D-25-57696R1

Dear Dr. Stamper,

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,

Yury E Khudyakov, PhD

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. 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

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

Reviewer #1: I Don't Know

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. 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: The authors have appropriately addressed all the concerns raised in the previous review.The manuscript has been improved and is now suitable for publication

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

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