PONE-D-23-37489Dynamic heterogeneity in COVID-19: Insights from a mathematical modelPLOS ONE

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 [This work was supported by Rakesh Jain’s research is supported by R01-CA259253, R01-CA208205, R01-NS118929, U01-CA261842, and U01-CA 224348, Outstanding Investigator Award R35-CA197743 and grants from the National Foundation for Cancer Research, Jane’s Trust Foundation, Niles Albright Research Foundation and Harvard Ludwig Cancer Center. Lance Munn’s research is supported by R01-CA2044949. Triantafyllos Stylianopoulos’s research is supported by the European Research Council ERC-2019-CoG-863955. Chrysovalantis Voutouri is supported by Marie Skłodowska Curie Actions Individual Fellowship Global Horizon 2020 MSCA-IF-GF-2020-101028945.].  

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[This work was supported by Rakesh Jain’s research is supported by R01-CA259253, R01-CA208205, R01-NS118929, U01-CA261842, and U01-CA 224348, Outstanding Investigator Award R35-CA197743 and grants from the National Foundation for Cancer Research, Jane’s Trust Foundation, Niles Albright Research Foundation and Harvard Ludwig Cancer Center. Lance Munn’s research is supported by R01-CA2044949. Triantafyllos Stylianopoulos’s research is supported by the European Research Council ERC-2019-CoG-863955. Chrysovalantis Voutouri is supported by Marie Skłodowska Curie Actions Individual Fellowship Global Horizon 2020 MSCA-IF-GF-2020-101028945.]

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 [This work was supported by Rakesh Jain’s research is supported by R01-CA259253, R01-CA208205, R01-NS118929, U01-CA261842, and U01-CA 224348, Outstanding Investigator Award R35-CA197743 and grants from the National Foundation for Cancer Research, Jane’s Trust Foundation, Niles Albright Research Foundation and Harvard Ludwig Cancer Center. Lance Munn’s research is supported by R01-CA2044949. Triantafyllos Stylianopoulos’s research is supported by the European Research Council ERC-2019-CoG-863955. Chrysovalantis Voutouri is supported by Marie Skłodowska Curie Actions Individual Fellowship Global Horizon 2020 MSCA-IF-GF-2020-101028945.]

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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: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: N/A

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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?

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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: This manuscript by Voutouri and coworkers is an interesting work trying to translate the complexity of the interactions between a virus (SARS-CoV-2), its host and possible countermeasures (vaccines, antivirals) in mathematical terms. The adopted equations are sound and took into account several parameters. The conclusions are relevant. The manuscript is surely worth of publication. However, some aspects should be addressed before publication as detailed below:

1. What this Referee is missing is the actual application (if any) of the mathematical model developed by the Authors to Public Health. What do the Authors envision? Please explain better in the Discussion. This is relevant especially for Readers of PlosOne not entirely familiar or interested to mathematical models but more into medical/biological Research

2. I would clearly separate the Results in those coming out from the model in line with what is already well established in the COVID-19 field and supported by well controlled studies, those suggesting novel findings that need to be further addressed with specific experiments, and those likely contradicting published data. All these aspects may be represented in a Table or diagram and more than one reference should be reported for any result supporting already published data. In my opinion, this would make the manuscript clearer and more interesting for readers less familiar with mathematical models and would increase the overall merit of the study

3. In Figure 1 I would change the title as what it is shown is not the mathematical model, but the biological interplay took into account by the Authors to implement their model. Furthermore, I would use part of what is now reported in the Description of the Mathematical Model in the Supplementary file to better explain what is depicted in the Figure. Also the Authors should clearly state in the main text why they selected those pathways and based on what literature data (with references). This is essential as it constitutes the basis of the entire work

4. For a clear understanding of Figure 2, the Authors should explain what are the variants they took into account, what do they mean by favorable and unfavorable and form what published data they come to this latter definition

5. In Supplementary Table 1, every time the Authors adopt an estimate of the relative parameter from where this estimate comes from? This is very important and I could not find this information

6. Supplementary Table 2 is not clear to me? From where the Kin comes from? It is calculated? If yes from which one of the equations? If is estimated, from where? This is another crucial aspect of the manuscript. Please explain

Reviewer #2: Referee report on the paper Dynamic heterogeneity in COVID-19: Insights from a mathematical model

(PONE-D-23-37489)

The main result of the paper is to prove the heterogeneity of the clinical courses of COVID-19 as a consequence of random factors like the innate or adaptive immune response and the initial viral load by using the simulation of a mechanistic mathematical model of COVID-19 that have been developed by the authors.

The referee is not an expert in the clinical treatments the COVID-19 disease so that my comment refers to the model properties.

The model is only schematically described in the paper since it has been presented in previous papers, but the equations are reported in the supplementary material together with an impressive list of parameters that define the model and some of them are only estimated so that the existence of an overfitting problem is possible using experimental data to validate the model.

The model could be interpreted as a proposal of a`digital twin' for the COVID-19 evolution in a patient,

to perform in silico experiments, but the validation problem is still open and should be better discussed in the paper.

It is the referee's opinion that the utility of any dynamical model is the capability of providing predictions on the future evolution of the considered phenomenon using the available information at a given time and a validation procedure is realized when the model is able to predict situations not yet observed. It would be useful for the reader to know this type of validation procedure has been considered or if the paper is the first attempt to propose a validation procedure of the model.

The idea of the author to consider a range of values for the parameters is the usual procedure to estimate the sensitivity of the model to the parameter changes and to highlight the relevant parameters. Considering the complexity of the model it is not clear in the paper if there exists some control parameter whose values is critical to understand the model simulations, whereas the other parameter values have a more limited effect. Which are the control parameters of the model? The simulations presented in the paper have been chosen to test the existence of control parameters?

The authors proposed three sets of simulations to study:

the interaction of antiviral therapy with heterogeneity in vaccine induced immunity;

the interaction of antiviral therapy with heterogeneity in the innate and adaptive immune response;

the interaction of initial viral load with treatment efficacy and immune response.

In each simulation a parameter value is varied by order of magnitudes, but there is not a clear explanation if such a variability of the values are consistent with observed data or can be justified from a physiological point of view.

The results of the simulations are not unexpected, so that it is not clear if the complexity of the model is really justified: can the authors comment on the need of such a complex model to get the results presented in the paper?

Moreover it is not clear to me how to quantify the color scale in the right of the figures that distinguishes the cases between favorable and unfavorable. It would be useful if the author could give more information on how the simulation results are classified.

The last simulation in the paper considers the `rebound after antiviral therapy and optimal treatment initiation': this is an interesting case since the results show the existence of an optimal timing for the antiviral therapy. However the question if this effect is really observed in the available data is not clear, may the authors comment if this prediction is consistent with some observations.

In the final discussion the authors stress that `a major finding of the paper is the confirmation that patient clinical course may be heavily influenced by random events which are difficult to observe or control for in clinical cohorts', but this is really an unexpected result that characterizes the COVID-19 disease and justifies the complexity of the model? Due to the large variation in the parameter values, one would expect a variability in the simulation results for any mathematical model of a disease evolution, why this result is relevant for the proposed model?

The authors claim that an advantage of the model is to predict markers of the various model trajectories in order to optimize the therapy results. This is a key point for the model, but there is no suggestion of possible predictors in the paper. To define a predictor is difficult due to the lack of the necessary data from the patients, or due to the complexity of the model?

In summary, I would like to recommend the paper for publication in PLOS One, once the authors have replied to previous comments.

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Reviewer #1: No

Reviewer #2: Yes: Armando Bazzani

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Dynamic heterogeneity in COVID-19: Insights from a mathematical model

PONE-D-23-37489R1

Dear Dr. Stylianopoulos,

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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Yury E Khudyakov, PhD

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

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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

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

Reviewer #1: 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

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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

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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: The Authors have took into account my suggestions and addressed all the issues. The paper is now suitable for publication.

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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

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