Application of principal component analysis on temporal evolution of COVID-19

Ashadun Nobi; Kamrul Hasan Tuhin; Jae Woo Lee

doi:10.1371/journal.pone.0260899

Peer Review History

Original SubmissionAugust 1, 2021
20 Sep 2021 Decision Letter - Stefan Cristian Gherghina, Editor PONE-D-21-24857Application of principal component analysis on temporal evolution of COVID-19PLOS ONE Dear Dr. Lee, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.The manuscript requires further several revisions regarding study novelty and contribution to the related domain, related literature, quantitative framework and discussion, as well as future research directions. Please submit your revised manuscript by Nov 04 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. 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Currently, your Funding Statement reads as follows: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” Please include your amended statements within your cover letter; we will change the online submission form on your behalf. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: No ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: No ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: This is an interesting contribution to the existing literature, but the paper suffers from several shortcomings listed in the following comments. - The paper should be checked by a native. - A discussion section should be added. - The introduction should be updated by recent researches. - The novelty and contribution should be clearly bolded. - The authors should consider some works about Data Analysis that can be applied to model different datasets. For example, - The authors should consider some works about Data Analysis that can be applied to model Covid Datasets. For example, Time series modelling to forecast the confirmed and recovered cases of COVID-19, Travel medicine and infectious disease 37, 101742. Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models, Chaos, Solitons & Fractals 140, 110151. Principal component analysis to study the relations between the spread rates of COVID-19 in high risks countries, Alexandria Engineering Journal 60 (1), 457-464. Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries, Chaos, Solitons & Fractals 140, 110230. Factor analysis approach to classify COVID-19 datasets in several regions, Results in Physics 25, 104071. It’s better to suggest some subjects for future works. Best regards, Reviewer #2: Outstanding work. PCA in the context of time series is a reliable method to identify similarities between countries in terms of severity and to anticipate new surges. I would suggest to contrast their findings with similar studies in the discussion and to suggest ways to use their analysis in a practical way to forecast new surges and outbreaks. I suggest the authors consider contrasting the study with one produced by our group: Duarte P, Riveros E. Understanding the cycles of COVID-19 incidence: Principal Component Analysis and interaction of biological and socio-economic factors. Annals of Medicine and Surgery. 2021 Jun 1:102437. Reviewer #3: I failed to identify some points as follows: 1. What is the hypothesis and problem that you address in this paper? 2. Does only PCA enough to explain this complex issue as the scientist are striving to know the behavior of the virus and its mutation. 3. What could be the implications of your study in epidemiology? 4. Is that only focus to check the applicability of PCA methods? 5. `By studying the temporal evolution of the correlations of the different affected countries, we uncover the periods when there were major changes in the correlation structure of the global pandemic in the analyzed countries.` to know this country wise data is enough I think what new things can be added by this research? is unclear. 6. The variation in confirmed cases and deaths among different countries is obvious it need not to say after PCA. 7. Why the dynamic trend is changing is not explored here. Multiple factors might be in fact certainly involved from governance to economy. Overall, I did not find any novel outcome from this study. Thus, I am not recommending its publication in this journal. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. 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Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0260899.r001
Revision 1
21 Oct 2021 Author Response Thank you for your fruitful comments. Reviewer #1: This is an interesting contribution to the existing literature, but the paper suffers from several shortcomings listed in the following comments. - The paper should be checked by a native. Ans: A native person checked the paper. - A discussion section should be added. Ans: We added the discussion in the manuscript. Please see the last paragraph of result analysis. - The introduction should be updated by recent researches. Ans: The introduction is updated by recent articles. We added some sentences in the third paragraph of Introduction part. - The novelty and contribution should be clearly bolded. Ans: The novelty is given in the last paragraph of introduction part. - The authors should consider some works about Data Analysis that can be applied to model different datasets. Ans: We add the related articles and update our work. We add new references [14-21]. Reviewer #2: Outstanding work. PCA in the context of time series is a reliable method to identify similarities between countries in terms of severity and to anticipate new surges. I would suggest to contrast their findings with similar studies in the discussion and to suggest ways to use their analysis in a practical way to forecast new surges and outbreaks. I suggest the authors consider contrasting the study with one produced by our group: Duarte P, Riveros E. Understanding the cycles of COVID-19 incidence: Principal Component Analysis and interaction of biological and socio-economic factors. Annals of Medicine and Surgery. 2021 Jun 1:102437. Ans: We added the discussion in the last paragraph of result analysis. We add the reference in [16]. Reviewer #3: I failed to identify some points as follows: 1. What is the hypothesis and problem that you address in this paper? Ans: We analyze the time series of COVID-19 to extract information about relations and the dynamics of COVID-19 in the top 25 countries. We want to investigate the dynamical similarity among countries. The similarity of strategic policies should be influenced to control the COVID-19. We gave some points in last paragraph of Introduction. 2. Does only PCA enough to explain this complex issue as the scientist are striving to know the behavior of the virus and its mutation. Ans: Of course, only PCA is not enough to explain these issues. However, we believe that the PCA is one of the powerful methods to understand the dynamics of disease spreading. Genome sequence analysis is necessary to know the behavior of the virus and its mutation. However, genome sequence analysis is critical in understanding the COVID-19 and preventing the spread of the disease. Different techniques such as PCA, Machine learning, artificial neural network can be applied to analyze genome sequence of COVID-19. We will apply these techniques on gene data for further investigation. We include some methods to explain the dynamical features of COVID-19 in second paragraph of Introduction part. 3. What could be the implications of your study in epidemiology? Ans: The transmission of diseases in the severe affected countries can be understood by using PCA. From the PCA, we can observe the strong correlation of the dynamical change for the spreading disease. We can propose the similar strategic policies to those countries. I include these points in last paragraph in Conclusion part 4. Is that only focus to check the applicability of PCA methods? Ans: Actually, we apply PCA technique to the correlations for the change of COVID-19 cases and death among the severe affected countries to understand the states and dynamic of COVID-19 in the analyzed countries. This analysis is also helpful to determine the relationships among these countries. In future, we will apply feature ranking of machine learning on gene data to understand the behavior of virus and its mutation. 5. `By studying the temporal evolution of the correlations of the different affected countries, we uncover the periods when there were major changes in the correlation structure of the global pandemic in the analyzed countries.` to know this country wise data is enough I think what new things can be added by this research? is unclear. Ans: PCA is a technique to reduce data dimension. Applying PCA on correlation structure, we try to show that only first two PCs are enough to observe the changes of the states in the analyzed countries and to make relationships among these countries. We gave some comments in the last paragraph in section <Pandemic states of countries>. 6. The variation in confirmed cases and deaths among different countries is obvious it need not to say after PCA. Ans: Yes. You are right. We wanted to identify the periods to observe the COVID-19 states in the analyzed countries by heat map so that we can make a comparative study among the countries in the basis of severity. 7. Why the dynamic trend is changing is not explored here. Multiple factors might be in fact certainly involved from governance to economy. Ans: The change of correlation structure among the countries is identified by the largest eigenvalue and is used to observe the dynamics of COVID-19. The correlation structure for the change of COVID-19 cases and deaths among the countries is altered due to lock down, making social distance, hard immunity and for other policies and consequently, the dynamic trend is changed. Some articles considered other factors like socio-economic factors in new added Ref [16]. In the future study, we will consider other possible factors. Attachments Attachment Submitted filename: jwl_rebuttal.docx https://doi.org/10.1371/journal.pone.0260899.r002
19 Nov 2021 Decision Letter - Stefan Cristian Gherghina, Editor Application of principal component analysis on temporal evolution of COVID-19 PONE-D-21-24857R1 Dear Dr. Lee, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Stefan Cristian Gherghina, PhD. Habil. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: (No Response) ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No https://doi.org/10.1371/journal.pone.0260899.r003
Formally Accepted
23 Nov 2021 Acceptance Letter - Stefan Cristian Gherghina, Editor PONE-D-21-24857R1 Application of principal component analysis on temporal evolution of COVID-19 Dear Dr. Lee: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Stefan Cristian Gherghina Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0260899.r004

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