PONE-D-21-35256Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in AndorraPLOS ONE

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Reviewers' comments:

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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

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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: 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 manuscript highlights a very important phenomenon on the role played by cases importation on local epidemics and how this can be best used in our current epidemic models to reproduce and understand the observed trends. The authors study the epidemic in Andorra which is indeed a very nice example of the role played by seeding thanks to the high ratio of external flows versus inhabitants.

I think that the manuscript is well written and clear in the research purpose that it wants to address. The methodology and the data suit well this purpose.

I think that the manuscript is suitable for acceptance after fulfilling a minor revision.

- “The value of 149 R0 can change over the course of an epidemic due to changes in human behavior and 150 NPIs; a goal of control efforts, such as those employed during the COVID-19 pandemic, 151 is to reduce R0 to bring an epidemic under control”

The authors make some confusion on the description of R0 (which does not change), probably confusing it with Rt.

Please check the definitions to avoid misconceptions and correct the paragraph.

https://royalsociety.org/-/media/policy/projects/set-c/set-covid-19-R-estimates.pdf

- line 212: “Model ii: transmission a function of trips data” → as

- line 244: It is not clear to me why C(t) should be proportional to the removals R with a delay in time. The cumulative reported cases should be reported with a certain delay after the individuals get infected, i.e. when they start being positive to tests, not when they get recovered or they isolate or die.

- line 315: “telecom data on mobility is provided by Andorra Telecom”, but then the authors say “Furthermore, telecoms data showed that 86% of entrances by foreign SIMs were either Spanish or French, and when accounting for entrances by Andorran SIMs, 68% of all entrances were by Spanish or French SIMs”.

So the mobility data includes also foreign countries SIM cards?

- on march 163h the govt of Andorra imposed a restriction to public activities and schools.

Your model is trained on this period until May 31, can this affect the calibration of your model to more general periods, like for example periods with no activity restrictions?

- Fig3 resolution is very poor and it is very difficult to understand as it is.

- Fig4 shows a flat R0 for the trips-only model. However in Fig5 total trips seem to vary a lot during the period of observation. How come this scenario does not produce any variation of Rt with respect to trips (internal mixing)?

I understand that Andorra only counts for 77,000 inhabitants and that the external flow is very high with respect to the local population relatively to other countries, however I don’t understand how a model based only on internal trips does not provide a varying reproductive number along the time of simulation.

- line 432: only to get this clear in epidemiological terms, model 1 is a homogeneous mixing, model 2 is a model of one population in which transmission depends on local mixing only, model 3 accounts for local mixing and external seeding (importation and exportation)

- line 453: “More importantly, the model (ii) that used trips data to model transmission rates (without entrances data) had results similar to, and slightly worse than, the baseline model (i) which assumed a constant transmission rate. This is not surprising, as the data indicated trips were able to increase without impacting transmission rates”

I would say that this happens also because the model is calibrated in a lockdown period, hence internal mixing does not necessarily convert to spreading opportunities because of strong social distancing measures.

- line 454: “This is also shown in that the best fit for model (ii) had parameters that flattened the impact of the trips data, resulting in a nearly flat reproduction number, R0.” indeed, the effect of the lockdown calibration, but of course using a fixed transmission parameter leads to this problem. The authors may need to address this model scenario limitation. (check gramatic of the first part of the sentence)

- Table 1: in the description of the error metric it would be better to state also the range of the values that this metric can reach. From 0 (perfect) to N (bad).

- Fig.5, very interesting, why not plotting also the new cases time-series?

- Some recent and very related work on the effect on local epidemics of population changes and seeding caused by entrances is missing from the references, see for example:

-Mazzoli, M., Valdano, E., & Colizza, V. (2021). Projecting the COVID-19 epidemic risk in France for the summer 2021. Journal of travel medicine, 28(7), taab129.

-Kraemer, M. U., Hill, V., Ruis, C., Dellicour, S., Bajaj, S., McCrone, J. T., ... & Pybus, O. G. (2021). Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B. 1.1. 7 emergence. Science, 373(6557), 889-895.

-Kraemer, M. U., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., ... & Scarpino, S. V. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497.

-Mazzoli, M., Pepe, E., Mateo, D., Cattuto, C., Gauvin, L., Bajardi, P., ... & Ramasco, J. J. (2021). Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact. PLoS computational biology, 17(10), e1009326.

Reviewer #2: The authors study the problem of how COVID-19 transmission dynamics relate to human mobility patterns. In particular, the aim is to compare the predictive power of domestic mobility and cross-border mobility and thus infer how important dynamic population mapping is in the context of infectious disease dynamics. This is a very interesting and relevant research question, as few studies on infectious disease spread take a dynamic population into account, often because mobility datasets are limited to a single country and do not include cross-border traffic which could inform population changes. The authors study the case of Andorra, use mobility data to inform an SEIR model, and make the surprising observation that within-country trips hardly had any predictive power, while taking into account cross-border mobility improved predictions.

The manuscript is very well written. The methods and results are described in clear, unambiguous language.

The underlying data is presented in detail, including the mobility data results of the serological study. How mobility is measured and how the data is processed is described clearly, where the authors make use of data-processing practices commonly used in literature (such as the stay detection and home detection). The serological study is properly discussed, including caveats stemming from the two waves of testing.

The data used in the study is made available online. The authors have also made software code used in the study available. I have downloaded the data and code and was able to replicate several results and figures used in the manuscript.

The SEIR model is set up, trained and evaluated appropriately. The two components of mobility are incorporated in a reasonable manner in the model. The use of three model scenarios is an appropriate way to test the influence of trips- and entrance-data on the prediction. I was honestly surprised by the extreme lack of predictive power of the model scenario using only trips data (model ii) and was very skeptical at first, but I could not find a flaw in the methodology. I do think that the entrances are such a good predictor as they are probably highly correlated with a host of other interventions that affect transmission rate, but the authors properly address this in the discussion. Altogether, I think the results, conclusions and their interpretations are sound.

I would recommend the manuscript for publication as is.

I have only one minor remark which I would appreciate being addressed by the authors:

It is fair to say that Andorra is a very special case compared to other countries, due to its very small population and relatively large amount of cross-border traffic and temporary visitors. I would assume it has a much more "dynamic" population than most countries, which might mean that the results are not very transferable to other countries, where cross-border traffic and temporary visitors are of less importance. I think it would be appropriate to address this in the discussion. It would also be interesting to include comparative values for Andorra and other countries regarding how dynamic their populations are, if available (such as the fraction of cross border traffic among all traffic, the number of tourists relative to population, number of temporary workers, etc).

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Reviewer #1: No

Reviewer #2: No

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Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra

PONE-D-21-35256R1

Dear Dr. Berke,

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.

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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

**********

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

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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: All my comments have been addressed, I have no further comments and I find the manuscript suitable for publication

Reviewer #2: (No Response)

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Reviewer #1: No

Reviewer #2: No