Peer Review History

Original SubmissionFebruary 29, 2020
Decision Letter - Siew Ann Cheong, Editor

PONE-D-20-05906

Modelling and Prediction of the 2019 Coronavirus Disease Spreading in China Incorporating Human Migration Data

PLOS ONE

Dear Dr. Tse,

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.

In particular, both reviewers would like the authors to include comparisons against similar studies already published, on the effects of human migration. Please also be sure to address Reviewer #2's comments on the infection starting in early November 2019 instead of during the Spring Festival in February 2020, and to use more current numbers than those available at the time of submission.

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We look forward to receiving your revised manuscript.

Kind regards,

Siew Ann Cheong, Ph.D.

Academic Editor

PLOS ONE

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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: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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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: No

Reviewer #2: Yes

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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

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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 study used SEIR compartments to simulate the dynamics of COVID-19. It is an important issue now but there are some concerns as follows.

1. Many studies incorporated migration data into SEIR model for simulating epidemic dynamics. Authors need to highlight the significant findings of the study.

2. Recent related studies on modeling COVID-19 which simulating the dynamics Wuhan and Hubei for estimated infected persons were published. Authors need to add more comparisons with these studies.

3. Some notations need to be further clarify. For example, the notation of denominators in Equation 6 and Equation 7 should be P_i (t) and P_j (t), respectively.

4. If N_i^s (t) means the susceptible population in city i at time t, isn’t it similar to S_i (t)? please explain their difference more clearly.

5. Which is your notation of initial infection number? I_i (t_0) in line 228 or λ_I I_i (t_0) in line 236?

6. What is the fitting result of the following parameters: δ_i, λ_I, and λ_E?

7. Figure 5 displays the result of forecasting; it should add 95% confidence intervals or error bars to show the variations of estimated values.

8. What is the spatial variation of the prediction? For example, whether the cities strongly interacting with Wuhan have more precise prediction results than the other cities? Or, whether high-population-density cities have more accurate predictions? These comparisons may reflect the value of incorporating human migration data into a SEIR model so that model results can benefit real epidemic prevention tasks.

Reviewer #2: This is a sound analysis of two publicly available data set focusing on intercity migration in China.

The authors may benefit from aa recent paper on migration and covid-19 spread published in April issue of Migration Letters journal. That can be useful to better frame the context of this paper indicating wider link between human mobility and disease diffusion.

Authors may revisit the sentence in second page: "The COVID-19 outbreak, however, began

to occur and escalate in a special holiday period in China (about days surrounding the Lunar New Year), during which a huge volume of intercity travel took place, resulting in outbreaks in multiple regions connected by an active transportation network." Because now we know the virus was out and about in as early as early November.

The data needs to be critically presented; Authors indicate the possibility of incompleteness or inaccuracy of official covid data but it seems they assume Baidu data is free of problems. It is important to note the selectivity bias here. This data is collected by an app, which means there are a lot of questions about its representability. This should be clearly noted so readers can interpret it accordingly.

In the conclusion, authors state "The Coronavirus Disease 2019 (COVID-19) epidemic has hit China hard, 331 and as of February 20, 2020, a total of 74,579 infection cases have been 332 confirmed in China, with death toll reaching 2,119." It is a live incidence but it can be useful if they can include the latest statistics regarding the pandemic while making sure the data and analysis refer to an earlier period.

**********

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Reviewer #1: No

Reviewer #2: No

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Revision 1

Reviewer: 1

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 study used SEIR compartments to simulate the dynamics of COVID-19. It is an important issue now but there are some concerns as follows.

1. Many studies incorporated migration data into SEIR model for simulating epidemic dynamics. Authors need to highlight the significant findings of the study.

Authors’ Response: This work was completed on February 19, 2020 (medRxiv 10.1101/2020.02.18.20024570). We used a short historical epidemic spreading data and migration data to develop the model and the corresponding system identification algorithm. At the time of performing this work, there was no attempt in combining SEIR model, migration data and system identification techniques to analyze and predict the spread of COVID-19. The results thus have important indicative values on the effectiveness of using limited initial outbreak data in predicting pandemic progression. Remarks have been added to the Discussions section to highlight this. The main findings were listed in the Results section.

2. Recent related studies on modeling COVID-19 which simulating the dynamics Wuhan and Hubei for estimated infected persons were published. Authors need to add more comparisons with these studies.

Authors’ Response: The following information has been added to the Results section.

“For Wuhan, our model shows that the cumulative number of infections was 105,244 (95% CrI [64297,146191]), which was consistent with previous estimation of 75,815 infected cases (95% CrI [37304,130330]) [15]”.

[16] Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet. 2020 Feb 29; 395(10225):689-97.

3. Some notations need to be further clarify. For example, the notation of denominators in Equation 6 and Equation 7 should be P_i (t) and P_j (t), respectively.

Authors’ Response: The notations have been revised.

4. If N_i^s (t) means the susceptible population in city i at time t, isn’t it similar to S_i (t)? please explain their difference more clearly.

Authors’ Response: Thank you for pointing this out. N_i^s represents the size of the group of susceptible, infected, exposed and removed individuals. Thus, we have N_is(t_0) = S(t_0)+E(t_0)+I(t_0)+R(t_0). This has been included in the Method section of revised paper.

5. Which is your notation of initial infection number? I_i (t_0) in line 228 or λ_I I_i (t_0) in line 236?

Authors’ Response: I_i (t_0) represents the actual infected number at time t_0, while λ_I I_i (t_0) represents the initial infection number used in the model. We have clarified this in the paper.

6. What is the fitting result of the following parameters: δ_i, λ_I, and λ_E?

Authors’ Response: The fitting result of δ_i, λ_I, and λ_E have been added, while Figure 6 (c) shows the distribution of \\delta_i.

7. Figure 5 displays the result of forecasting; it should add 95% confidence intervals or error bars to show the variations of estimated values.

Authors’ Response: The 95% confidence intervals (CrI) have been added to Figures 4 and 5, and in the text.

8. What is the spatial variation of the prediction? For example, whether the cities strongly interacting with Wuhan have more precise prediction results than the other cities? Or, whether high-population-density cities have more accurate predictions? These comparisons may reflect the value of incorporating human migration data into a SEIR model so that model results can benefit real epidemic prevention tasks.

Authors’ Response: The experimental results show that several factors, such as strong interaction with Wuhan and high population density, influence the prediction results to some extent. Actually, the spread of COVID-19 in a city is highly influenced by the control measures, which vary from city to city. If a city adopted strict control measures, the spread of COVID-19 may be much slower and less severe than the predicted results.

Reviewer: 2

Comments to the Author

This is a sound analysis of two publicly available data set focusing on intercity migration in China.

The authors may benefit from a recent paper on migration and covid-19 spread published in April issue of Migration Letters journal. That can be useful to better frame the context of this paper indicating wider link between human mobility and disease diffusion.

Authors’ Response: This work was completed on February 19, 2020 (medRxiv 10.1101/2020.02.18.20024570). We used a short historical epidemic spreading data and migration data to develop the model and the corresponding system identification algorithm. At the time of performing this work, there was no attempt in combining SEIR model, migration data and system identification techniques to analyze and predict the spread of COVID-19. The results thus have important indicative values on the effectiveness of using limited initial outbreak data in predicting pandemic progression. Remarks have been added to the Discussions section to highlight this.

Authors may revisit the sentence in second page: "The COVID-19 outbreak, however, began to occur and escalate in a special holiday period in China (about days surrounding the Lunar New Year), during which a huge volume of intercity travel took place, resulting in outbreaks in multiple regions connected by an active transportation network." Because now we know the virus was out and about in as early as early November.

Authors’ Response: We have checked the literature and available data carefully, and found that the “official” data (up to today from the Chinese National Health Committee) indicated the earliest confirmed case in China being December 8, 2019. Indeed, the spread could have started earlier, but our data analysis could only work according to the official data which showed surges in infected numbers in many Chinese cities beginning mid January, which was the period of “spring rush” in China. We have also edited the text so as to emphasize that we referred to the rapid spread in China which was in the period before the Lunar New Year when huge volume of intercity travel took place.

The data needs to be critically presented; Authors indicate the possibility of incompleteness or inaccuracy of official covid data but it seems they assume Baidu data is free of problems. It is important to note the selectivity bias here. This data is collected by an app, which means there are a lot of questions about its representability. This should be clearly noted so readers can interpret it accordingly.

Authors’ Response: Several works adopted Baidu data to investigate the spread of COVID, which have been cited in the paper. Also, we clarified that the Baidu data were expected to be inexact and served to provide indicative travel volumes which were sufficient for the model fitting. This would serve to alert our readers about this issue.

[14] Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, y Piontti AP, Mu K, Rossi L, Sun K, Viboud C. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020 Apr 24; 368(6489): 395-400.

[15] Lai S, Ruktanonchai NW, Zhou L et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China [published online May 4, 2020]. Nature. 2020;10.1038/s41586-020-2293-x. doi:10.1038/s41586-020-2293-x

In the conclusion, authors state "The Coronavirus Disease 2019 (COVID-19) epidemic has hit China hard, 331 and as of February 20, 2020, a total of 74,579 infection cases have been 332 confirmed in China, with death toll reaching 2,119." It is a live incidence but it can be useful if they can include the latest statistics regarding the pandemic while making sure the data and analysis refer to an earlier period.

Authors’ Response: We have revised the Introduction to include the latest worldwide figures while emphasizing that this work was completed on February 20, 2020.

Attachments
Attachment
Submitted filename: Author_reply_20200722.pdf
Decision Letter - Siew Ann Cheong, Editor

Modeling and Prediction of the 2019 Coronavirus Disease Spreading in China Incorporating Human Migration Data

PONE-D-20-05906R1

Dear Dr. Tse,

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,

Siew Ann Cheong, Ph.D.

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 #2: All comments have been addressed

Reviewer #3: (No Response)

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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 #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: 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 #2: Authors addressed the key concerns raised by myself and other reviewer. This revised version is of acceptable quality for publication.

Reviewer #3: In this work, the authors attempt to modify the classic SEIR model of disease propagation to include data on human mobility. Specifically, the new model seeks to incorporate fluctuations in the total population into the SEIR, something that was previously taken to be fixed. This work is clearly timely and important and the approach seems reasonable. The authors have addressed the comments of previous reviewers.

My major complaint is that it would be nice to see some verification of the numbers. Clearly, when this paper was originally written that would not be possible as they were predicting the future, but that is no longer the case. Looking at official case numbers and timelines, it seems the authors have done a reasonable job making predictions, but some quantitative measure of correctness at this point would be both possible, and a nice addition. Otherwise, it is not clear the to the reader whether this model is viable for future outbreaks.

More minor comments follow:

1. Page 2, around line 29 states that that cities far from Wuhan have a linear relationship between # of infections and distance, but on a log plot, that is not particularly clear.

2. Page 4, lines 119-123, the authors have, a the reviewers suggestion, attempted to acknowledge the problems with Baidu data by stating that the m_ij need only be accurate relative to one another, but it is not exactly clear why this is the case, since the absolute numbers are used to make predictions of individuals and there is no immediately obvious scaling factor.

3. Page 4, line 129, "as seen in Figure 3" is floating here and should be deleted

4. Page 5, equation 4. The alpha factor does not exist in this set of equations for S and E, though it does show up in later equations. It is not clear to my why this was omitted.

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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 #2: No

Reviewer #3: No

Formally Accepted
Acceptance Letter - Siew Ann Cheong, Editor

PONE-D-20-05906R1

Modeling and Prediction of the 2019 Coronavirus Disease Spreading in China Incorporating Human Migration Data

Dear Dr. Tse:

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. Siew Ann Cheong

Academic Editor

PLOS ONE

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