Comparing modelling approaches for the estimation of government intervention effects in COVID-19: Impact of voluntary behavior changes

Lun Liu; Zhu Zhang; Hui Wang; Shenhao Wang; Shengsheng Zhuang; Jishan Duan

doi:10.1371/journal.pone.0276906

Peer Review History

Original SubmissionMarch 14, 2022
23 Jun 2022 Decision Letter - Lei Shi, Editor PONE-D-22-07518Comparing modelling approaches for the estimation of government intervention effects in COVID-19: Impact of voluntary behavior changesPLOS ONE Dear Dr. Wang, 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 Aug 04 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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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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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 ****** 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 ****** 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: By testing three major modeling methods, they demonstrate that quite different estimates of intervention effects could be acquired with different methods. The idea of the model seems somehow convincing and worth reporting. However, the way the current version of the manuscript is presented cannot be accepted and needs a major revision. Here are a couple of points that I think should be considered. Critical remarks: (1) The model depiction is not clear to me. The language in explaining the model should be improved. In particular, I cannot find how to get indicators ‘shared change of mobility’ and ‘value of the mobility indicator in county c on day t. (2) In Experiment 1, I suggest the authors give some details in the appendix. At least, the algorithm and the details should be given. In Experiment 2 and Experiment 3, the process of solving is not clear. (3) The reason why the authors choose these methods is not clear. In reference to what you give, their topics are all related to COVID-19. Thus, the innovative point of this paper should be emphasized. What is the difference between your content and theirs that should be given? (4) The image resolution is too low, it should be improved. Reviewer #2: The authors analyzed how the choice of modelling approach, in particular how voluntary behavior change is accounted for, affect the intervention effect estimation. They conducted the analysis by experimenting different modelling methods on a same data set composed of the 500 most infected U.S. counties. They also compared the most frequently used methods from the two classes of modelling approaches, and find that computational methods that do not account for voluntary behavior changes are likely to produce larger estimates of intervention effects as assumed. In contrast, natural experimental methods are more likely to extract the true effect of interventions by ruling out simultaneous behavior change. Among different difference-in-difference estimators, the two-way fixed effect estimator seems to be an efficient one. Totally speaking, their methods are performed appropriately and rigorously and the results seem reliable. I support its acceptance before the following issues are addressed. 1. Page 2, line 60, the authors wrote that “The modeling approaches…that do not ”. Here, what the authors said modelling approaches are all related to statistical methods. In fact, the modelling approaches are not restricted to statistics, the mathematical model, for example, SIR, SEIR or other models, act as a useful tool and has been widely studied. One can refer to these closely related books. ” Tanimoto, Jun. "Evolutionary games with sociophysics." Evolutionary Economics (2019).” And “Sun, Gui-Quan, Marko Jusup, Zhen Jin, Yi Wang, and Zhen Wang. "Pattern transitions in spatial epidemics: Mechanisms and emergent properties." Physics of life reviews 19 (2016): 43-73.” 2. The presented figures are not clear, please revise the figures to make it clearer. For example, the resolution of fig.1 is too low, and readers cannot capture the main results for the current version. Please revise it. Besides, the figure caption in fig.1, the authors simply explain fig.1A as ”Dynamics of daily R_t in the sample countries”. What is R_t? Please note that not all the readers have enough patient to read the whole text. Please revise the figure captions in a more detailed manner and conclude the main conclusions of each figure. ****** 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: No 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. 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Revision 1
15 Aug 2022 Author Response We thank the reviewers for their helpful suggestions. All the concerns have been addressed as follows. Reviewer #1: By testing three major modeling methods, they demonstrate that quite different estimates of intervention effects could be acquired with different methods. The idea of the model seems somehow convincing and worth reporting. However, the way the current version of the manuscript is presented cannot be accepted and needs a major revision. Here are a couple of points that I think should be considered. Critical remarks: Comment 1: The model depiction is not clear to me. The language in explaining the model should be improved. In particular, I cannot find how to get indicators ‘shared change of mobility’ and ‘value of the mobility indicator in county c on day t. Reply: The two phrases are revised to ‘γt denotes day t, representing the common part of mobility change across counties’ and ‘Google mobility indicator in county c on day t’ (line 178, 176). We also make a few other revisions to the language to make the description clearer. (2) In Experiment 1, I suggest the authors give some details in the appendix. At least, the algorithm and the details should be given. In Experiment 2 and Experiment 3, the process of solving is not clear. Reply: For Experiment 1, the main algorithms are provided in the Method section, which are taken from the original study of Brauner et al. (2021). Since full details of the method are provided in their original paper, we refer readers to that paper if they need further information on the method (line 208-209). In Experiment 2 and 3, OLS is used in solving the parameters. We add this information and refer readers to relevant econometric books and papers in the revised manuscript (line 222-223, 261). (3) The reason why the authors choose these methods is not clear. In reference to what you give, their topics are all related to COVID-19. Thus, the innovative point of this paper should be emphasized. What is the difference between your content and theirs that should be given? Reply: We chose these methods because they are the most commonly used in estimating COVID-19 intervention effects. Table S1 summarizes the existing studies on COVID-19 intervention effects when we performed the analysis. It shows that difference-in-difference (including two-way fixed effect model) and Bayesian model are the two most used methods. Therefore, we take Bayesian model and two difference-in-difference methods as the methods to compare. The innovation of our paper lies in that we focus on methodological comparison, in other words, how the choice of modelling approach would affect the estimation of COVID-19 intervention effects. On the other hand, most existing studies focus on the intervention effect itself. This point is more clearly demonstrated in the revised manuscript (line 51-52). (4) The image resolution is too low, it should be improved. Reply: We will follow the journal’s requirements to make sure that image resolutions are suitable for publication. Reviewer #2: The authors analyzed how the choice of modelling approach, in particular how voluntary behavior change is accounted for, affect the intervention effect estimation. They conducted the analysis by experimenting different modelling methods on a same data set composed of the 500 most infected U.S. counties. They also compared the most frequently used methods from the two classes of modelling approaches, and find that computational methods that do not account for voluntary behavior changes are likely to produce larger estimates of intervention effects as assumed. In contrast, natural experimental methods are more likely to extract the true effect of interventions by ruling out simultaneous behavior change. Among different difference-in-difference estimators, the two-way fixed effect estimator seems to be an efficient one. Totally speaking, their methods are performed appropriately and rigorously and the results seem reliable. I support its acceptance before the following issues are addressed. Reply: We thank the reviewer for the positive comments. All of the issues of concern are addressed as suggested by the reviewer. Comment 1: Page 2, line 60, the authors wrote that “The modeling approaches…that do not ”. Here, what the authors said modelling approaches are all related to statistical methods. In fact, the modelling approaches are not restricted to statistics, the mathematical model, for example, SIR, SEIR or other models, act as a useful tool and has been widely studied. One can refer to these closely related books. ” Tanimoto, Jun. "Evolutionary games with sociophysics." Evolutionary Economics (2019).” And “Sun, Gui-Quan, Marko Jusup, Zhen Jin, Yi Wang, and Zhen Wang. "Pattern transitions in spatial epidemics: Mechanisms and emergent properties." Physics of life reviews 19 (2016): 43-73.” Reply: Many thanks for suggesting the literature. The statement is revised to “While mathematical models (such as SIR and SEIR models) are classical methods in modelling epidemics (9, 10), statistical models and their combinations are widely applied in estimating the effects of interventions. Relevant statistical models employed by existing analyses can be grouped into two classes…” (line 63-65). Comment 2. The presented figures are not clear, please revise the figures to make it clearer. For example, the resolution of fig.1 is too low, and readers cannot capture the main results for the current version. Please revise it. Besides, the figure caption in fig.1, the authors simply explain fig.1A as ”Dynamics of daily R_t in the sample countries”. What is R_t? Please note that not all the readers have enough patient to read the whole text. Please revise the figure captions in a more detailed manner and conclude the main conclusions of each figure. Reply: Fig. 1 is compressed in the process of building the submission file, though we tried to use a higher resolution figure. We will follow the journal’s requirements to make sure that image resolutions are suitable for publication. And all figure captions are revised as suggested by the reviewer. Attachments Attachment Submitted filename: plos-reviewer.docx https://doi.org/10.1371/journal.pone.0276906.r002
17 Oct 2022 Decision Letter - Lei Shi, Editor Comparing modelling approaches for the estimation of government intervention effects in COVID-19: Impact of voluntary behavior changes PONE-D-22-07518R1 Dear Dr. Wang, 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, Lei Shi 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: No 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 to the author for answering my question. It would be better if the data links were presented. Reviewer #2: (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 Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0276906.r003
Formally Accepted
8 Nov 2022 Acceptance Letter - Lei Shi, Editor PONE-D-22-07518R1 Comparing modelling approaches for the estimation of government intervention effects in COVID-19: Impact of voluntary behavior changes Dear Dr. Wang: 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. Lei Shi Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0276906.r004

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