A Segmented Linear Regression Study of Seasonal Profiles of COVID-19 Deaths in Italy: September 2021–September 2024

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. The figures used to present the regression segments are generally informative but lack detailed captions and do not provide adequate guidance for interpretation. The seasonal color coding is not sufficiently explained, and the link between figures and numerical results in the main text remains underdeveloped. Each figure should be introduced more formally, with a description of what it shows and why it is relevant. In addition, the visual presentation could be improved by including annotations or summary statistics directly on the figures to facilitate quick interpretation. 
2. The model evaluation in the paper relies exclusively on the coefficient of determination for assessing fit. Although R squared is useful, it is not sufficient on its own to validate segmented linear models, especially in the context of time series data with potential autocorrelation. The paper should include additional metrics such as mean squared error, root mean squared error, or adjusted R squared. Including residual plots or performing diagnostic checks for autocorrelation would further support the statistical soundness of the approach. 
3. The authors state that their analysis intentionally avoids investigating causal mechanisms behind observed mortality trends. Although neutrality is sometimes appropriate, the lack of even a speculative discussion on potential causes reduces the utility of the study for health policy and planning. Including a brief section that discusses potential drivers such as declining vaccine effectiveness, emergence of new variants, or population behavior would give more practical relevance to the observed patterns. The absence of this discussion makes the paper feel incomplete and may limit its applicability to real-world decision-making in public health contexts.
4. A key limitation of this study is its omission of digital data sources that capture public engagement, sentiment, or health communication trends on social platforms like YouTube during the same seasonal windows. These elements offer essential insights into behavioral responses and societal perception. The absence of such data reduces the scope of the analysis. The authors are encouraged to review recent works such as https://doi.org/10.1109/CCWC62904.2025.10903713 and https://dl.acm.org/doi/proceedings/10.1145/3677117, which present frameworks integrating social media discourse, sentiment, and toxicity analysis for more context-aware pandemic monitoring.
5. Although the code and dataset have been made publicly available, the model lacks a validation procedure that would demonstrate its generalizability. There is no mention of dividing the dataset into training and testing sets, performing cross-validation, or conducting sensitivity analyses. These omissions reduce the reliability of the results and make it unclear whether the model can be applied to future or unseen data. The paper would be improved by adding a validation protocol and reporting performance metrics on held-out data. This addition would show that the model is not only descriptive but also useful for forecasting.

Author Response

Please look at the attached Letter for Reviewers.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper is on “A Segmented Linear Regression Study of Seasonal Profiles of COVID-19 Deaths in Italy: September 2021 – September 2024”, analyzing time series data of COVID-19 deaths using a piecewise linear regression model to identify and quantify seasonal mortality trends. Addressing the following concerns will improve the paper:

-While the authors acknowledge that segmented linear regression is not ideal for count data, the paper would benefit from a comparative analysis against Poisson or negative binomial models, to confirm the robustness of the identified trends and clarify the added value of the chosen method.

- The seasonal definitions used (for example extended winter/fall) are somewhat arbitrary and could introduce bias. A clearer justification or sensitivity analysis exploring how different seasonal boundaries affect results would enhance the study’s credibility.

- The paper avoids discussing drivers of seasonal mortality shifts, yet draws public health conclusions. A clearer boundary between observational description and policy recommendations, or inclusion of covariates like vaccination rates or mobility, would strengthen the relevance of findings.

-While figures are informative, some  like Figure 5 are dense and may overwhelm readers. A more intuitive visual summary or heatmap of slope magnitude and significance across seasons and years could improve accessibility and interpretation.

Author Response

Please look at the attached Letter for Reviewers.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors The current manuscript (MS) offers a data-driven analysis of the number of COVID cases in Italy in the period 2021-2024. Although the peak of and largely the interest of  the public to COVID has dropped enormously over the last couple of years, it is clearly that [similar as with the standard constantly mutating flu] the civilization will be forced to live with the COVID virus forever. That is why the new methods to battle and even better to predict and to avoid the new epidemics are invaluable.   The current MS, however, requires a number of changes prior to reaching a publication quality; the authors are encouraged to address the points listed below in the revised version of the MS.   All figures should be of a unified size (e.g., 8 cm in width for single-column plots) and be prepared in the same style (regarding frames, ticks, geometric proportions, axis-caption fonts (size and style), etc.).
  The quantities presented in the plots should (at best) have their full name, mathematical notation, and physical units being presented along each axis, all separated by commas, in order to facilitate the perception by the reader (e.g., "Force, f, N").   The caption of each figure should be detailed enough and self-explanatory, being also understandable without reading a respective part of the main text.
  The seasonal variations in the number of cases (and the respective number of deaths) is natural and sort of evident. How the authors fit the dynamics of the increase and of the decrease of the dynamics is however somewhat unusual. Typically, the process of infection spreading is a multiplicative exponentially growing process called in the community of stochastic processes the geometric or exponential Brownian motion (GBM). This process should thus produce the straight lines for the LOG of the number of cases as a function of time, rather than for the number of cases as a function of time themselves. The authors are therefore encouraged to have a look at the GBM and its mathematical properties described in details in the recent and relevant Ref. [https://doi.org/10.1088/1367-2630/aa7199], where the process of GBM is also applied to the dynamics of exponentially growing stock-market prices.
  The coefficients of those regression methods are still interesting. Can one e.g. make some correlations between the speed how the amount of cases increases in each of say of the summer periods and the vaccination campains launched in Italy at the respective times. I mean, can one see from the current data that for the situations when a larger part of the population is freshly vaccinated the dynamics of the increase of the new cases of COVID is also the lowest. Or this statement is not true? In simple words, do you statistically significantly see from the data that the vaccination politics resulted in a visible reduction of the dynamics of COVID spreading?

Author Response

Please look at the attached Letter for Reviewers.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have revised their paper as per my comments and feedback from the previous review round. I do not have any additional comments at this point. I recommend the publication of this paper in its current form. 

Author Response

A Letter to Reviewers is attached below. Thank you.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have improved the paper and it is ready to be published.

Author Response

A Letter to Reviewers is attached below. Thank you.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

In the current version, some comments of the referees were indeed implemented, but (in contrast to a long and detailed letter of reply) the amount of actual essential changes in the revised manuscript is rather limited. Firstly, the graphs are still nearly unreadable (with tiny axis captions and microscopic different symbols which are impossible to distinguish). Secondly, a comparison with a theoretical stochastic process of reset GBM (geometric Brownian motion)---which is one-to-one model of an exponential increasing infection being instantly and harshly restricted either by a total vaccination or by a strict quarantine---is still not presented in the text. A deeper revision of the text and serious improvement  of the graphical presentation is therefore still needed/expected.

Author Response

A Letter to Reviewers is attached below. Thank you.

Author Response File: Author Response.pdf