Relationship between mobility and road traffic injuries during COVID-19 pandemic—The role of attendant factors

Kandaswamy Paramasivan; Rahul Subburaj; Venkatesh Mohan Sharma; Nandan Sudarsanam

doi:10.1371/journal.pone.0268190

Peer Review History

Original SubmissionDecember 31, 2021
2 Mar 2022 Decision Letter - Jian Wang, Editor PONE-D-21-40969Relationship between mobility and road traffic injuries during COVID-19 pandemic – the role of attendant factorsPLOS ONE Dear Dr. Paramasivan, 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.== Please submit your revised manuscript by Apr 16 2022 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. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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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 ****** 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: 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 paper uses the blockade background caused by the COVID-19 coronavirus disease epidemic to study road traffic safety. This is a novel and interesting work, and the article has clear ideas. However, the following problems still exist: 1、 The abstract and introduction of this paper do not refine the research and innovation of traffic safety under the background of pandemic. 2、 The comparison of the impact on RTC, RTI and RTD in the first and second wave blockades caused by the pandemic needs a sufficient amount of data to support. How to deal with and analyze a large amount of data? 3、 The article should describe in more detail the parameters of the proposed prediction model (equation 4) and how to apply the prediction. 4、 The reason for the difference between the predicted time series and the actual time series is whether the blockade implemented by the government has limitations and other influencing factors are not considered. 5、 The training time and experimental time of the model reach 12 months and 4 months respectively, but the verification time is only 45 days. Whether the verification time is too little leads to the lack of persuasion of the model. 6、 This paper mainly expounds the relationship between traffic flow and various accompanying factors in the context of disease pandemic, but the summarized relationship between them does not explain the actual traffic flow management and what measures to take in the context of pandemic. Reviewer #2: This paper investigates the imapct of attendant factors on relationship between mobility and road traffic injuries during COVID-19 pandemic. overal, the paper is well-writen, and results are meaingful. However, there is a problem as follow: As explained in the title, this paper focuses on the the role of attendant factors (road traffic victims' access to trauma centres, the robustness of health infrastructure, and the responsiveness of police and emergency services). However in the section "3. Results" and "4. Discussion", results of the role of attendant factors are not displayed prominently although lots of results and comparisons are provided. It is suggested to reorganize the two sections to highlight results related to the role of attendant factors. ****** 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. Reviewer #1: Yes:** Chao Sun Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0268190.r001
Revision 1
21 Mar 2022 Author Response RESPONSE TO REVIEWERS Reviewer #1: This paper uses the blockade background caused by the COVID-19 coronavirus disease epidemic to study road traffic safety. This is a novel and interesting work, and the article has clear ideas. However, the following problems still exist: 1. The abstract and introduction of this paper do not refine the research and innovation of traffic safety under the background of pandemics. We had made an effort to present the research on traffic safety during regular times and the pandemic. Specifically, paragraph three of the introduction in the previous draft is dedicated to positioning our work viz-a-viz the literature on road safety during the pandemic. However, based on the reviewer's comment, we felt it might be missed in its current state. We have now made three sub-sections under introduction providing the background, literature review covering both pre-pandemic and pandemic, and finally focusing on the innovation in the present research. 2. The comparison of the impact on RTC, RTI and RTD in the first and second wave blockades caused by the pandemic needs a sufficient amount of data to support. How to deal with and analyze a large amount of data? The data in the study is the daily count of road traffic crashes for eleven years from 1st January 2010 to 30th September 2021. Each time series contains 4291 data points. Moreover, the data used in the study corresponds to two geographic locations, making the data large and complex. Large data usually poses problems in two aspects: the storage and cleaning, and the modelling part, where we try to draw inferences from the data. The first aspect was not a significant issue in this study as the data is a univariate time series (we have three such time series) with one value for each time step which is easy to store as compared to data with multiple features for a single data point (tabular data) or images. All of the time steps had non-empty values at each time step, so not demanding the need for data cleaning. In the modelling phase, the DeepAR model used in the study belongs to a class of deep learning models that are highly suitable for learning from large time-series data, unlike commonly used models like ARIMA and exponential smoothing, which are not so robust in large data settings and saturate in performance. While deep learning models take more extended periods to train, we used GPU based hardware architecture to train our DeepAR model in this study, which drastically reduces the training time. Therefore, the right choice of the model and the hardware can be used to overcome the issues in modelling with a large dataset. A brief description of the points mentioned here is added in the paper titled "Method ". 3. The article should describe in more detail the parameters of the proposed prediction model (equation 4) and how to apply the prediction. The section entitled "Prediction" is revised to include the detailed process behind generating a forecast. After the revision, equation (4) mentioned in the paper is made clearer and more comprehensive, summarising the steps involved using mathematical notation. The hyper-parameters of the best model are mentioned in the section now entitled "Parameters and forecast accuracy of model". The section also includes the lag terms present in the best model used to generate the forecasts. 4. The reason for the difference between the predicted time series and the actual time series is whether the blockade implemented by the government has limitations, and other influencing factors are not considered. A direct relationship between mobility and RTC/RTD is well established in the literature and referenced in our paper (When mobility increases, the incidence of RTC/RTD increases). The COVID-19 induced blockade restricted the mobility of people, which is evident from the google mobility data used in the study. In wave one, when the severity of the infection was mild, the downward trend in RTD/RTI was in alignment with the mobility. However, in wave two, even after adjusting for mobility, the actual daily count of RTI-Grievous was substantially lower than predicted. This is primarily attributable to the attendant factors such as overwhelmed health infrastructure and apprehension and disinclination of the victims to seek relief at hospitals. In other words, mobility was a reasonable predictor of RTD/RTI; however, in exceptional circumstances such as the pandemic in wave two, the attendant factors influenced more than mobility. The blockade's implementation was more effectively done in Chennai city than the rest of Tamil Nadu because of a higher ratio of police officers to people in Chennai City. Similarly, the people's compliance to the restrictive orders also varied across time. However, these variations have clearly manifested into appropriate changes in mobility which forms the basis of the study. The other influencing factors of the pandemic induced blockades have been effectively captured through mobility. 5. The training time and experimental time of the model reach 12 months and 4 months respectively, but the verification time is only 45 days. Whether the verification time is too little leads to the lack of persuasion of the model The DeepAR model is trained using data from 1st January 2010 to 31st December 2019 (10 years). The validation period of the model is from 1st January 2020 to 22nd March 2020 (82 days), and the prediction period is from 23rd March 2020 to 30th September 2021 (557 days) (The periods are mentioned in the section entitled "Data"). Increasing the validation period has two issues: first, reducing the data available for training the model leads to underfitting. Second, increasing the validation size does not allow the model to learn the recent/short term relationship. Hence, a validation period of 45 days is used in the study. As is standard practice, the validation output is also unseen by the learning model. 6、 This paper mainly expounds on the relationship between traffic flow and various accompanying factors in the context of disease pandemic, but the summarized relationship between them does not explain the actual traffic flow management and what measures to take in the context of pandemic. We have explained in the section under "policy implications" the measures to be taken in the context of pandemics relating to road traffic incidents. However, considering the reviewer's remark, in this revision, we have included the summarized relationship in the CONCLUSION section as well. Further, we have also added another sub-section under "Discussion" entitled "Traffic flow management during the pandemic." Reviewer# 2 This paper investigates the impact of attendant factors on the relationship between mobility and road traffic injuries during the COVID-19 pandemic. Overall, the paper is well-written, and the results are meaningful. However, there is a problem as follow: As explained in the title, this paper focuses on the role of attendant factors (road traffic victims' access to trauma centres, the robustness of health infrastructure, and the responsiveness of police and emergency services). However, in the sections "3. Results" and "4. Discussion", results of the role of attendant factors are not displayed prominently, although lots of results and comparisons are provided. It is suggested to reorganize the two sections to highlight results related to the role of attendant factors. We thank the reviewer for pointing out the need to highlight the results of the role of attendant factor prominently. Besides revising the " Discussion " appropriately, we have specifically added an exclusive sub-section entitled the role of attendant factors and mobility in the main DISCUSSION section, besides revising the “Discussion” appropriately. Further, we have added a few lines in the conclusion section to highlight the key takeaways of the research in dealing with these attendant factors during the crisis and future. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0268190.r002
25 Apr 2022 Decision Letter - Jian Wang, Editor Relationship between mobility and road traffic injuries during COVID-19 pandemic – the role of attendant factors PONE-D-21-40969R1 Dear Dr. Paramasivan, 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, Jian Wang 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 Reviewer #2: 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 Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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 Reviewer #2: 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 Reviewer #2: 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: Thanks for the efforts of responding my comments. You have addressed my concerns to a satisfactory level. Reviewer #2: The author has revised the paper according to my suggestion, now I am happy to recommend this paper for acceptance. ****** 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: Yes:** Chao Sun Reviewer #2: No https://doi.org/10.1371/journal.pone.0268190.r003
Formally Accepted
13 May 2022 Acceptance Letter - Jian Wang, Editor PONE-D-21-40969R1 Relationship between mobility and road traffic injuries during COVID-19 pandemic – The role of attendant factors Dear Dr. Paramasivan: 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. Jian Wang Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0268190.r004

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