The Effect of the Built Environment on the COVID-19 Pandemic at the Initial Stage: A County-Level Study of the USA
Round 1
Reviewer 1 Report
The discussed paper is topical and very important. Authors correctly defined the aim of the paper and indicated the research gap. The structure of the paper is correct and logical.
The presented research results are important and interesting. However, it is worthwhile for the Authors to make an attempt to assess whether the presented dependencies (at least some of them) are not apparent dependencies. Is it a cause and effect relationship? Perhaps the presented differentiation can be explained by the share of vaccinated people in the population of individual counties? In my opinion, it is worth building such a model, even for control purposes.
In addition to the presentation of the research results, I suggest adding the Authors' comment (an attempt to explain the obtained results).
Detailed comments:
- table 1 - why is "T Statistical" in the title?
- figures 1 and 2S - it is difficult to read what each of the three maps concerns, please correct;
- tables 2, 3 and in the supplement - the tables contain values ​​of 0.000, which indicates no relationship; I propose to increase the accuracy of the given values;
- line 218 - please check the figure number;
- in the case of the titles of tables and figures, you do not need to put a dot, in my opinion.
In my opinion, the article can be published after the corrections have been made.
Author Response
Comments and Suggestions for Authors
Reviewer 1
The discussed paper is topical and very important. Authors correctly defined the aim of the paper and indicated the research gap. The structure of the paper is correct and logical.
The presented research results are important and interesting. However, it is worthwhile for the Authors to make an attempt to assess whether the presented dependencies (at least some of them) are not apparent dependencies. Is it a cause and effect relationship? Perhaps the presented differentiation can be explained by the share of vaccinated people in the population of individual counties? In my opinion, it is worth building such a model, even for control purposes.
- Thank you for reviewing our manuscript and for providing constructive comments! We have revised the manuscript according your comments to the best of our knowledge. We agree that it is worth building a model to include the share of vaccinated people in the population and have launched our next study following this direction. We admitted this limitation and proposed future studies to assessed whether some of the presented dependencies could be causal relationship in the discussion section:
The limitation of the study includes the lack of investigation of causal relationship between the dependent and explanatory variables as the current empirical results only suggest correlational relationship. In addition, as the vaccination programs have been deployed progressively, it is necessary to consider the spatiality of such deployment that the current study neglects to consider. Future studies should further investigate the causal relationship between these variables with the incorporation of vaccination effect and spatial autocorrelation. Additional models should include geographically weighted regression, spatial lag, and spatial error models, to emphasize the local effect of the vaccination as an embedded attribute of the build environment impact.
In addition to the presentation of the research results, I suggest adding the Authors' comment (an attempt to explain the obtained results).
Detailed comments:
- table 1 - why is "T Statistical" in the title?
- Thank you for pointing this out. The phrase has been removed.
- figures 1 and 2S - it is difficult to read what each of the three maps concerns, please correct;
- This is a valid point. We have revised both figures to increase the legibility. In Figure 1, the three maps represent (a) residential density; (b) road network density, and (c) job accessibility, respectively. In Figure S2, the three maps represent (a) infection; (b) death; and (c) mortality rate, respectively. We also provided the original editable PDF files:
From top to bottom, the three maps represent: (a) residential density; (2) road network density, and (3) job accessibility, respectively.
From top to bottom, the three maps represent (a) infection; (b) death; and (c) mortality rate, respectively.
- tables 2, 3 and in the supplement - the tables contain values ​​of 0.000, which indicates no relationship; I propose to increase the accuracy of the given values;
- We agree with the reviewer. We have increased the accuracy to four digits after the decimal place. Tables 2, 3, S3, and S4 have been revised.
- line 218 - please check the figure number;
- We moved figures and tables from the supplementary material to the main text and re-numbered them.
- in the case of the titles of tables and figures, you do not need to put a dot, in my opinion.
- Per comment, we removed the dot.
In my opinion, the article can be published after the corrections have been made.
- Thank you again for reviewing our manuscript! Your comments have substantially improved the quality of the manuscript.
Author Response File: 
Author Response.docx
Reviewer 2 Report
The paper is very interested. The authors explored the characteristics of the built environment including density, diversity, accessibility, and transit networks, to estimate the association between these factors and the spatial distribution of COVID-19 in the United States.
The paper except the first part is well organized. The authors successfully reach the goal set.
I suggest:
- Integrate the currently too small Introduction section;
- insert the objective of this research in the Introduction section, currently present in Section 2.2 COVID transmission and the built environment or in a sub-paragraph of Section 2. Literature Review;
- better organize the two sections Introduction and Literature Review;
- improve the quality of Figures 1 and 2, in which the legends cannot be read.
Also, I highlight that it is not possible to access the data:
Data Availability Statement: Data and code used for this research can be find here:
Merged county level data:
https://drive.google.com/file/d/1jXBy36qRnuqaqePC8Sh-ntRMHinZs6zK/view?usp=sharing
Processed biweekly data:
https://drive.google.com/file/d/1h15aMIGfJq7xbVWk90T3Fp0unNsdcNd4/view?usp=sharing
STATA Codes of overall and biweekly models:
https://drive.google.com/file/d/1mq6bNuTXe1Plch0wOmewa8hbPHLtYOlP/view?usp=sharing
https://drive.google.com/file/d/14VVqqNdoQUIfWUPrKdsKDa5VXq8iyzfj/view?usp=sharing
Author Response
Reviewer 2
The paper is very interested. The authors explored the characteristics of the built environment including density, diversity, accessibility, and transit networks, to estimate the association between these factors and the spatial distribution of COVID-19 in the United States.
The paper except the first part is well organized. The authors successfully reach the goal set.
- Thank you for reviewing our manuscript and for providing constructive comments! We have revised the manuscript according your comments to the best of our knowledge.
I suggest:
Integrate the currently too small Introduction section; insert the objective of this research in the Introduction section, currently present in Section 2.2 COVID transmission and the built environment or in a sub-paragraph of Section 2. Literature Review; better organize the two sections Introduction and Literature Review;
- This is a great point. We integrating the introduction and literature review sections into one section.
improve the quality of Figures 1 and S2, in which the legends cannot be read.
- This is a valid point. We have revised both figures to increase the legibility. In Figure 1, the three maps represent (a) residential density; (b) road network density, and (c) job accessibility, respectively. In Figure S2, the three maps represent (a) infection; (b) death; and (c) mortality rate, respectively. We also provided the original editable PDF files:
From top to bottom, the three maps represent: (a) residential density; (2) road network density, and (3) job accessibility, respectively.
From top to bottom, the three maps represent (a) infection; (b) death; and (c) mortality rate, respectively.
Also, I highlight that it is not possible to access the data:
- We packaged all files into the link below:
Data and STATA code used for this research can be find here:
https://drive.google.com/file/d/1RnvShu4HhKptngoSSKlcaETGts_XSxMh/view?usp=sharing
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
The authors in this last version have solved the problems highlighted in the previous version. I confirm that the supporting files are available for consultation. Now the paper has a more balanced structure and has improved in terms of exhibition clarity, so it can be considered accepted for publication in Sustainability.
