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I think the article can be accepted in the current version.
The others are to be congratulated, as they managed to show within the suggestions sent, a very clear discussion of the sharp spread of the virus in poorer regions. They were successful in justifying the effect of parameter K on evaluation processes.
no comments
no comments
The study is of great importance to the scientific community, as well as to decision makers, since with the help of modeling they can optimize and act quickly in making more accurate decisions.
The authors have touched the all correction, so it may publish
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Please response to the reviewers point by point.
no comments
Introduction:
Lines 20 to 22 report the unprecedented and negative effect on global socioeconomic development and on public health and the progress of societies caused by the infection. In this sense, I suggest that the authors present in their conclusions the short, medium and long-term effects resulting from the pandemic in countries with the highest and lowest infection rates.
Results:
I suggest that the authors present a table detailing the adjustment of the models for each country, presenting the parameters and the explanatory coefficient of each adjusted model and its validation.
Conclusions:
Page 317. I suggest that the authors infer their conclusion about the factors that led economically developed countries to reach such high infection rates.
Page 318. I suggest that the authors express through the results their conclusions about the possible factors that led countries such as Brazil, characterized as developing and with a population distributed over a gigantic territorial area, to such high mortality rates.
I suggest that in the conclusion the authors express the factors that favored or hindered the spread of the infection and that may have positively or negatively influenced the k parameter of the model. Or whether the policies adopted in these countries, such as intervention strategies, can be captured or explained by the k parameter.
Figures:
I suggest improving the visual quality of Figure 1.Introduction:
Lines 20 to 22 report the unprecedented and negative effect on global socioeconomic development and on public health and the progress of societies caused by the infection. In this sense, I suggest that the authors present in their conclusions the short, medium and long-term effects resulting from the pandemic in countries with the highest and lowest infection rates.
no comments
Relevant research for understanding the dynamics involved in pandemic processes, the authors are to be congratulated for the study developed.
1. The Abstract should be rewritten in a more precise fashion highlighting the main findings.
2. Could you elaborate on the primary motivations that drove the development of this paper? What specific gaps or challenges in the field did you aim to address?
3. What inspired the use of the term "Logistic model" in this context? Are there historical or theoretical reasons behind this nomenclature?
4. Check the grammar and syntax of the whole manuscript carefully and revise the manuscript accordingly.
5. Data sources should be clearly provided for transparency and to ensure reliability of the information presented.
6. Include some theorems or propositions in your paper to strengthen your arguments and enhance the overall credibility of your research findings.
7. Please clarify whether any numerical methods have been used; this should be explicitly stated in the paper.
8. Section DISCUSSIONS may be written as RESULTS AND DISCUSSIONS
9. What are the direct applications of the results?
10. Summarize the findings of this work in "Conclusions".
The proposed design effectively applies the logistic model to COVID-19 propagation, emphasizing key growth phases and robust validation. However, it overlooks justifications for model choice, data irregularities, and the impact of interventions. Addressing these gaps, incorporating dynamic parameters, and comparing alternative models will enhance its accuracy, relevance, and real-world applicability.
The findings' validity depends on data quality, model appropriateness, and sensitivity analysis. Comparing logistic outcomes with alternative models and addressing oversimplifications strengthens reliability, ensuring results align with real-world COVID-19 dynamics.
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