Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching

Since the coronavirus disease (COVID-19) outbreak in December 2019, studies have been addressing diverse aspects in relation to COVID-19 and Variant of Concern 202012/01 (VOC 202012/01) such as potential symptoms and predictive tools. However, limited work has been performed towards the modelling of complex associations between the combined demographic attributes and varying nature of the COVID-19 infections across the globe. This study presents an intelligent approach to investigate the multi-dimensional associations between demographic attributes and COVID-19 global variations. We gather multiple demographic attributes and COVID-19 infection data (by 8 January 2021) from reliable sources, which are then processed by intelligent algorithms to identify the significant associations and patterns within the data. Statistical results and experts' reports indicate strong associations between COVID-19 severity levels across the globe and certain demographic attributes, e.g. female smokers, when combined together with other attributes. The outcomes will aid the understanding of the dynamics of disease spread and its progression, which in turn may support policy makers, medical specialists and society, in better understanding and effective management of the disease.


1.
I would have liked to see more discussion of the dataset used? It is stated that the authors used 22 attributes? First, why these 22 only, and second what is the total number of instances in the dataset (did you consider all countries)? I suggest you made this clear at the beginning of the materials section where you discuss the dataset 2. Figure 3 is difficult to read, I wonder if there is a better way to present the results

3.
The authors utilised class-association rules and self-organising maps to uncover these 'potential' associations. Is there a reason for this? Why not other machine learning techniques? I suggest just give some justification for these choices

Review form: Reviewer 3
Is the manuscript scientifically sound in its present form? Yes

Are the interpretations and conclusions justified by the results? Yes
Is the language acceptable? Yes

Recommendation?
Accept with minor revision (please list in comments)

Comments to the Author(s)
The article presents an analysis of the effect of COVID-19 by considering various demographic characteristics. The paper is well-written, but it would probably help readability if a list of the main abbreviations was included in the introduction.
There is an excessive number of percentages in the second paragraph of the introduction. These could be replaced by qualitative observations that explain the timeliness of the present study.
The methodologies presented are sound and adequately explained. However, a wide range of other methodologies could have been alternatively used. For this reason, it would make sense to include in the text some additional explanations regarding the reasons that made the authors choose these specific methodologies.
The authors use publicly available data, collected up to a specific date. It may be worthwhile to consider including additional (recent) data in order to test the consistency of the followed methodological approach and ensure the relevance of the conclusions over time.
The text could become clearer if some observations were expressed in more qualitative terms. For example, instead of stating that "A has negative/positive association with B", one could just state that "when A is higher/lower, B seems to be lower/higher". Some additional efforts could be made to explain contradictions between the conclusions reached in this present work and conclusions reached in prior works.

Decision letter (RSOS-201823.R0)
The editorial office reopened on 4 January 2021. We are working hard to catch up after the festive break. If you need advice or an extension to a deadline, please do not hesitate to let us know --we will continue to be as flexible as possible to accommodate the changing COVID situation. We wish you a happy New Year, and hope 2021 proves to be a better year for everyone.

Dear Dr Khan
On behalf of the Editors, we are pleased to inform you that your Manuscript RSOS-201823 "Analysing the Impact of Global Demographic Characteristics over the COVID-19 Spread Using Class Rule Mining and Pattern Matching" has been accepted for publication in Royal Society Open Science subject to minor revision in accordance with the referees' reports. Please find the referees' comments along with any feedback from the Editors below my signature.
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Kind regards, Royal Society Open Science Editorial Office Royal Society Open Science openscience@royalsociety.org on behalf of Prof Marta Kwiatkowska (Subject Editor) openscience@royalsociety.org Associate Editor Comments to Author: Three reviewers have offered a number of minor recommendations to improve your paper -final acceptance will be contingent on your incorporating these changes into the paper.
Reviewer comments to Author: Reviewer: 1 Comments to the Author(s) -I found the paper well written and technically sound in a timely topic. I enjoyed reading it. I recommend its acceptance.
Reviewer: 2 Comments to the Author(s) The study raises interesting questions, in particular understanding the covid-19 infection severity and whether it is associated with one or more demographic feature/s. The work is certainly timely, and address an issue that is challenging and 'perhaps' overlooked in the research community.
The paper is well written and structured, the literature review is thorough, and up to date with an interesting critical discussion of various relevant recent studies with some contradicting research reports and findings in the area of covid19 data-related work. The methods used are sound and well presented including good use of illustrations and presentation of tabulated results.
So overall, I think this is a good and timely study, and certainly can be accepted for publications as I believe the research community would benefit from it. That said, I have the following suggestions/ questions: 1. I would have liked to see more discussion of the dataset used? It is stated that the authors used 22 attributes? First, why these 22 only, and second what is the total number of instances in the dataset (did you consider all countries)? I suggest you made this clear at the beginning of the materials section where you discuss the dataset 2. Figure 3 is difficult to read, I wonder if there is a better way to present the results 3. The authors utilised class-association rules and self-organising maps to uncover these 'potential' associations. Is there a reason for this? Why not other machine learning techniques? I suggest just give some justification for these choices Reviewer: 3 Comments to the Author(s) The article presents an analysis of the effect of COVID-19 by considering various demographic characteristics. The paper is well-written, but it would probably help readability if a list of the main abbreviations was included in the introduction.
There is an excessive number of percentages in the second paragraph of the introduction. These could be replaced by qualitative observations that explain the timeliness of the present study.
The methodologies presented are sound and adequately explained. However, a wide range of other methodologies could have been alternatively used. For this reason, it would make sense to include in the text some additional explanations regarding the reasons that made the authors choose these specific methodologies.
The authors use publicly available data, collected up to a specific date. It may be worthwhile to consider including additional (recent) data in order to test the consistency of the followed methodological approach and ensure the relevance of the conclusions over time.
The text could become clearer if some observations were expressed in more qualitative terms. For example, instead of stating that "A has negative/positive association with B", one could just state that "when A is higher/lower, B seems to be lower/higher".
Some additional efforts could be made to explain contradictions between the conclusions reached in this present work and conclusions reached in prior works.

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Author's Response to Decision Letter for (RSOS-201823.R0)
See Appendix A.

Decision letter (RSOS-201823.R1)
The editorial office reopened on 4 January 2021. We are working hard to catch up after the festive break. If you need advice or an extension to a deadline, please do not hesitate to let us know --we will continue to be as flexible as possible to accommodate the changing COVID situation. We wish you a happy New Year, and hope 2021 proves to be a better year for everyone.

Dear Dr Khan,
It is a pleasure to accept your manuscript entitled "Analysing the Impact of Global Demographic Characteristics over the COVID-19 Spread Using Class Rule Mining and Pattern Matching" in its current form for publication in Royal Society Open Science.
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Associate Editor Comments to Author:
Three reviewers have offered a number of minor recommendations to improve your paper -final acceptance will be contingent on your incorporating these changes into the paper.
Response: Many thanks for providing us this opportunity. We have carefully addressed the comments from all reviewers and provided responses point by point as well as making the corresponding changes within the manuscript.
We believe that the reviewers detailed comments and the way we addressed their critique and suggestions has added much value to the papers' substance and presentation. The study raises interesting questions, in particular understanding the covid-19 infection severity and whether it is associated with one or more demographic feature/s. The work is certainly timely, and address an issue that is challenging and 'perhaps' overlooked in the research community.
The paper is well written and structured, the literature review is thorough, and up to date with an interesting critical discussion of various relevant recent studies with some contradicting research reports and findings in the area of covid19 data-related work. The methods used are sound and well presented including good use of illustrations and presentation of tabulated results. So overall, I think this is a good and timely study, and certainly can be accepted for publications as I believe the research community would benefit from it.
Response: Many thanks for the positive feedback.
That said, I have the following suggestions/ questions: 1) I would have liked to see more discussion of the dataset used? It is stated that the authors used 22 attributes? First, why these 22 only, and second what is the total number of instances in the dataset (did you consider all countries)? I suggest you made this clear at the beginning of the materials section where you discuss the dataset 2. Response: Authors are thankful for raising this point. We have updated the text in Section 4.1 (1 st paragraph) and 4.2 (1 st paragraph). Figure 3 is difficult to read, I wonder if there is a better way to present the results 3.

2)
Response: Firstly, this figure is very important and a unique way to present the COVID-19 spread across the globe (i.e. in a single 2D figure, one can visualise the COVID-19 severity worldwide). Following the reviewer's suggestion, we added new figure with better visualisation and quality.