Measuring COVID-19 Vulnerability for Northeast Brazilian Municipalities: Social, Economic, and Demographic Factors Based on Multiple Criteria and Spatial Analysis

Round 1

Reviewer 1 Report

In " Measuring covid-19 vulnerability for northeast brazilian municipalities: social, economic, and demographic factors based on multiple criteria and spatial analysis" authors propose a new model with the aim to identify municipalities vulnerable to COVID-19 that incorporates spatial analysis and multiple-criteria decision making.

 

Understanding which regions are the most vulnerable to Covid-19 and which socio-demographic parameters facilitate epidemic spreading is an intensely investigated subject with obvious practical ramifications. Methods of spatial analysis and multicriteria decision making have been used successfully and with much effect recently to shed light on the subject from many different perspectives, and also to outline different ways on how it could be understood. The present manuscript breaks new ground in this area and it delivers novel and important results that will surely be of interest to the readership of ISPRS International Journal of Geo-Information.

 

I have enjoyed reading this paper. I find it comprehensive and clearly written, and introducing a new approach that will surely inspire future research along similar lines. The main message is brought across fully supported by the presented results. For these reasons, I am in general in favor of publication in ISPRS International Journal of Geo-Information. I only have some minor suggestions for improvement:

 

 

- Define abbreviation MCDM.

- At some places spaces are missing behind citations (e.g. line 66, 73…).

- Could Eq. (4) be written without “in which” so that only mathematical symbols are used?

- It would also improve the paper if the figure and table captions would be made more self contained. Some figure captions (e.g. Fig. 1, 3, …) are really much to short and non-informative. I suggest to considerably complement the figure captions throughout the manuscript.

Author Response

Dear Reviewer #1

We would like to thank Reviewer #1 for the suggestions these have contributed to the improvement of our manuscript. Our replies to the reviewer’s comments are as follows.

Best regards

The authors

Author Response File: Author Response.pdf

Reviewer 2 Report

The article presents a substantial study carried out on an important and vulnerable area of ​​Brazil.

Comments on the text:

line 42-43: Definition of principal cities is vague, Rio de Janeiro and São Paulo are the principal cities in what aspect? 

Table 2: Attributes Area, Area 1 and Area 2 are not clear. For example, why is it necessary to list urban areas and urban cores? Isn't all urban cores a part of an urban area? Same for rural, and urban areas with low or high densities of buildings.

Table 2: only one Attribute presents it´s source. They all need it.

Table 2: It would be nice to see which dimension and sub dimensions mentioned in the paragraf of Line 132 and 138 are related to each variable. 

Table 2: Attributes Area, Area 1, Area 2 seems to be a construction proposed by the authors but that has not been explained in the text it´s reason/importance in an urban / territorial context.

Line 163, 195: reference in wrong format (Greco et al. 2002)

Figure 1: Title and distribution of the map elements need to be improved. Graphic scale bar is not appropriate for a map with more than one UTM fuse of range. I suggest using a lat/long grid, add state limits and capitals to the map that shows the municipalities. If you want to show how  large the study area is, I suggest presenting the Brazilian map within the American continent, showing in different colors the states that comprise the study area.

3. Data and Methods: does not explain what is the variable response used, its source and why it was chosen. (it was presented in table 3 as Day_1 (06/24), Day_2 (06/24), Day_3 (06/24) - it is not clear what year does it refers, if it is 2020, and we are at 2022, the study should present results updated, especially because the first 3 months of pandemy represent the most vulnerable places to covid at it first months. Spatial distribution has changed and a 2 year analysis would be more representative of the objective proposed

Table 4: desvpad need to be translated

Figure 2: Map presents total of cases or cases for 100 thousand inhabitants? Map title and legend must also inform this is the total of cases registered until June 30 (Cumulative cases) per 100 thousand inhabitants. If it is not per 100 thousand inhabitants please use proportional symbols instead of a choropleth map.

Table 11: date 06.30 is written with ; instead of .

Table 11 Group II 2021: is missing  )

 

The discussion of city size and density related to Covid spread is an important point at the research and can be improved. Ex: line 51 this relation  is stated based on a paper published at the first year of the pandemic.  These other articles might help:

Nicolelis, M.A.L., Raimundo, R.L.G., Peixoto, P.S. et al. The impact of super-spreader cities, highways, and intensive care availability in the early stages of the COVID-19 epidemic in Brazil. Sci Rep 11, 13001 (2021). https://doi.org/10.1038/s41598-021-92263-3

Teller, J., 2021. Urban density and Covid-19: towards an adaptive approach. Buildings and Cities, 2(1), pp.150–165. DOI: http://doi.org/10.5334/bc.89 

Amir Reza Khavarian-Garmsir, Ayyoob Sharifi, Nabi Moradpour, Are high-density districts more vulnerable to the COVID-19 pandemic?, Sustainable Cities and Society, Volume 70, 2021, 102911, ISSN 2210-6707, https://doi.org/10.1016/j.scs.2021.102911.

Author Response

Dear Reviewer #2

We would like to thank Reviewer #2 for the suggestions these have contributed to the improvement of our manuscript. Our replies to the reviewer’s comments are as follows.

Best regards

The authors

Author Response File: Author Response.pdf

Reviewer 3 Report

My main criticism is about the literature base, which could and should be broadened to both set this manuscript in the context of very related papers as well as more contextualizing ones.  In particular, it would be great to see how the results of your manuscript could be used to create larger impact (discuss this related to the existing literature!)

 

variable names should be renamed to something readable

p 4: "forCovid-19" -> "for COVID-19" (space and capitalization)

p 7: Do not indent after an equation, because the sentences go on.  (see lines 214 and 225)

p 7: a space is missing after "[35]" in line 217.

p 7: You might consider not to repeat formulas that have been published in other publications, like the one by Getis and Ord.

Figure 2: This is not a suitable depiction.  In a place where virtually no one lives, very few cases are already relevant, while in densely populated areas some few cases might not come unexpected.

The literature base discussed to contextualize could and should be larger. You might find the following paper relevant:

* https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8467210/    because it shows conceptual similarities

* https://doi.org/10.1080/17445647.2020.1776646    because it could help to understand how to mediate your results to the wider public to create higher impact

* https://www.nature.com/articles/s41598-021-04653-2   because it is also about the spatial context

Author Response

Dear Reviewer #3

We would like to thank Reviewer #3 for the suggestions these have contributed to the improvement of our manuscript. Our replies to the reviewer’s comments are as follows.

Best regards

The authors

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

It is positive to see that the figure captions have been streamlined.  This makes the figures easier to understand.

 

My main criticism about the literature base, which could and should be broadened to both set this manuscript in the context of very related papers as well as more contextualizing ones, still persists.  In particular, it would be great to see how the results of your manuscript could be used to create larger impact (discuss this related to the existing literature!)

 

The impression I get when reading your manuscript is also evidenced by only including 43 references.  While this number has been increased by 5, I still have the same impression when reading the manuscript.

 

In this sense, I would strongly suggest to check again how to better integrate your research with further existing research and aspects.

 

Think of:

 

* spatial dimension of COVID-19 spread and vulnerability (you are referring to the spatial dimension in almost every figure)

* depiction of COVID19 spread and related effects through the cartographic medium (you use a number of maps here)

* which limitations regarding these two points (spatial dimension, conveying COVID19-related information) exist as discussed in the literature?

 

In my view, not properly embedding your manuscript in the literature makes it shine much less than what it could.  I thus advise to re-consider.

 

== from my first review ==

 

The literature base discussed to contextualize could and should be larger. You might find the following paper relevant:

 

* https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8467210/    because it shows conceptual similarities

 

* https://doi.org/10.1080/17445647.2020.1776646    because it could help to understand how to mediate your results to the wider public to create higher impact

 

* https://www.nature.com/articles/s41598-021-04653-2   because it is also about the spatial context

Author Response

We would like to thank Reviewer #3 for the suggestions these have contributed to the improvement of our manuscript. Our replies to the reviewer’s comments are as follows.

 

First of all, we would like to thank you for your suggestions, which have greatly contributed to the improvement of our manuscript. We have now updated the literature base. We believe that the application of a spatial approach in this study is of great value, and we thank you again for your comments.

 

Now, we have considered your suggestions to the references. Also, we added other reference to support our discussion and results related.

 

Below the refences mentioned on text:

 

Mocnik F.; Raposo P.; Feringa W.; Raak M.; Köbben B. Epidemics and pandemics in maps - tha case of COVID-19. Journal of Maps 2020, 16, 1, 144-152.

 

Eryando T.; Sipahutar T.; Rahardiantoro S. The risk distribution of COVID-19 in Indonesia: a spatial analysis. Asia Pacific Journal of Public Health 2020, 32450-452.

 

Tang IW.; Vieira VM.; Shearer E. Effect of socioeconomic factors during the early COVID-19 pandemic: a spatial analysis. BMC Public Health 2022, 22, 1-9.

 

Ramírez IJ.; Lee J. COVID-19 emergence and social and health determinants in Colorado: a rapid spatial analysis. International Journal of Environmental Research and Public Health 17, 11, 3856.