PONE-D-21-37364Impact of human mobility and networking on spread of COVID-19 at the time of the 1st and 2nd epidemic waves in Japan: an effective distance approachPLOS ONE

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Reviewers' comments:

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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

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

Reviewer #1: No

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

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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: The authors provide an application of the popular effective distance metric from Brockmann and Helbing (2013) to study the arrival times of SARS-CoV2 in Japan at prefectures level during the 1st and 2nd wave of infections. Their results suggest that Tokyo and Kanagawa prefectures were the starting geographical regions for the spreading of Covid-19 through the country.

- a detailed description of mobility data is missing, to what year to the mobility data refer to? What do they represent? Is their representativity similar? the authors mix together different types of mobility data, but it is not clear if their representativity is the same, if they cover the same number of days. To mix different sources of mobility data is a sensible process, which can determine an overrepresentation of some modes of transportation over the others.

- how are the trips considered in the dataset? If a person travels from Tokyo to Osaka while stopping through all the prefectures in the middle, are all the steps considered or you only get the number of people travelling directly from Tokyo to Osaka? For example, is any stop time considered in order to separate trips? Or the dataset only accounts for the origin and destination showed on the purchased tickets?

- The authors say “The accuracy of the spread models was evaluated using the correlation between time of arrival and effective distance, calculated according to the different starting locations: Hokkaido, Chiba, Tokyo, Kanagawa, Aichi, Osaka, Fukuoka, and Okinawa that have the major international airports serving international flights to more than fifteen destination cities. The other seven locations are prefectures in which the major international airports are located, but the number of destination cities were less than eight. “

The authors said that they had 47 prefectures in the dataset, but the models are tested taking as seeding locations only 8 prefectures, whereas later they say “the other 7 locations are prefectures in which the major international airports are located”. I am confused at this point on how many administrative areas are they considering in their model. The model is tested only on a subset of the total dataset and the destinations seem reduced to 15 out of 47. I do not understand the logic of this choice. Why not considering all the prefectures are starting locations and destination locations? The ones that you listed are surely those with the biggest airports, but you said that the mobility data includes all modes of transportations, such as trains, bus, etcetera. Hence finally all prefectures could be tested as starting (seeding) points, possibly showing similar correlation scores if they are strongly connected.

- the authors may want to discuss the role of cases underdetection and the different testing capacities implemented in the different prefectures, which may have affected the detection of the first infected person in the area, and hence the time of arrival of the virus in the geographical area. Figure 2 shows arrival times that differ by few days between all the prefectures taken into account, small differences in cases underdetection would strongly affect the correlation, and indeed points in Fig.2a look very dispersed. The p-value for these correlation should be reported in the picture.

-Why not considering the date of reaching a certain amount of daily case incidence instead of using such a sensible measure like the arrival time of the first case (see for example Ref.[1] in assessing the spreaders role)? The role of asymptomatic infections has been already widely studied in the case of SARS-CoV2, single cases do not necessarily trigger an outbreak, e.g. many reported individual and isolated cases in Europe were reported in January 2020 without starting any local epidemic. Moreover, long times of disease incubation hinders the detection of the first infected patients in their actual areas of arrival from international travel. So what is the logic of relying on such a definition from a public health point of view for SARS-CoV2, when the detection of a single case are not necessarily representative for the triggering of an outbreak in the area?

- As a more general reasoning, I would like to know what is the authors’ thought on the following problem. The model is tested only on few starting prefectures, which surely are those who are the most connected ones in terms of international flights, however they also correspond to the most populated areas in which testing capacity may be higher than other peripheral prefectures, and hence outbreak detection is more efficient. What would be the probability of correctly detecting the arrival time of an infected patient in a peripheral area that has a poor testing capacity? In brief, would we be able through the effective distance model to correctly assess the start of an outbreak from a peripheral area, given these circumstances? Or would we always detect the earliest arrival time in the most tested prefectures and mobility hubs as the result of a demographic and testing capacity bias?

- In this sense, how can be we sure that Tokyo and Kanagawa are effectively the starting prefectures of the 1st and 2nd waves, only from the correlation of effective distance and arrival times, given the possible confounder effects represented by population density? Infrastructures are planned on the basis of gravity (or radiation) models that take into account origin and destination populations, so effective distance from mobility hubs at prefectures level would reflect the population hierarchy. Given these confounding factors, how can we be sure that this model provides evidence to say that Tokyo and Kanagawa were the seeding prefectures in Japan?

- the introduction on previous works and state of the art on mobility and epidemics is insufficient. There are many many works that lately confronted the effect of different mobility data on the spatiotemporal invasion of SARS-CoV2 at sub-national level that need to be correctly referenced.

See for example these three papers and their references to build a more general overview in the introduction:

[1] Mazzoli, Mattia, et al. "Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact." PLoS computational biology 17.10 (2021): e1009326.

[2] Kraemer, Moritz UG, et al. "Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B. 1.1. 7 emergence." Science 373.6557 (2021): 889-895.

[3] Kraemer, Moritz UG, et al. "The effect of human mobility and control measures on the COVID-19 epidemic in China." Science 368.6490 (2020): 493-497.

Reviewer #2: This work analyses how the human mobility in Japan may have affected the spreading of SARS-CoV-2 in the first two waves. It does so by computing the correlation between the effective distance of a prefecture from the starting point of the epidemic in Japan and the time of first-arrival of the epidemic in that prefecture.

This idea to use the concept of effective distance to gain insights on the epidemics is not new, but to the best of my knowledge this is the first time that it has been used for the public transportation network of Japan, making the paper original worth publishing.

I only have one doubt: throughout the paper it is mentioned various times that an higher Spearman coefficient indicates a better fit with the linear model. However the Spearman coefficient measures how closely the data follow a monotonic function, which in general may not be linear. So in my opinion the paper would gain in rigorousness if this problem was addressed or if instead of “linear model” the authors used a more generic term like “positive correlation”.

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Reviewer #2: No

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Impact of human mobility and networking on spread of COVID-19 at the time of the 1st and 2nd epidemic waves in Japan: an effective distance approach

PONE-D-21-37364R1

Dear Dr. Nohara,

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.

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Kind regards,

Chiara Poletto

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

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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 #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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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: No

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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: Dear Authors,

all my concerns have been addressed.

I consider the manuscript suitable for publication.

Reviewer #2: The authors did a good job in answering my requests. I am now satisfied with the content of the paper.

However I found that the authors say in their Data Availability Statement that the data are "available upon reasonable request". If the reason behind this choice was that the data cannot be anonymized or shared publicly that should have been explained in the Data Availability Statement. Otherwise the authors are encouraged to share the data on a public repository. Please provide an explanation and change the Statement accordingly.

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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

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