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Reviewer #1: Reviewer comment

Manuscript Number: PONE-D-24-43200

Manuscript Title: A Conceptual and Computational Framework for Modeling the Complex, Adaptive Dynamics of Epidemics: the case of the SARS-CoV-2 pandemic in Mexico

General comments:

The manuscript entitled “A Conceptual and Computational Framework for Modeling the Complex, Adaptive Dynamics of Epidemics: the Case of the SARS-CoV-2 Pandemic in Mexico” by Juan Pablo Gutierrez et al., demonstrates the implementation of a hybrid intelligence model that combines human and artificial intelligence to improve preparedness for the health emergencies like infectious diseases pandemics using data from the COVID-19 pandemic. The topic is interesting and the manuscript is written well. However, the manuscript is not deemed fit for publication in its current status and it needs a minor revision and improvement. I have a few comments that should be addressed:

� Including a line number would have made it much easier to comment.

1. Abstract

� “EPI-Puma integrates data from various sources…” Define the abbreviation “EPI-Puma” when it first appears in the manuscript. The sentence also mentions integrating data from various sources but doesn’t identify which sources are used, based on what justification the data are selected and how they are incorporated.

� The abstract doesn’t provide how accurate the EPI-puma project and platform are in predicting the outcomes.

� The abstract is overly brief and lacks sufficient details. Expanding it more on the aspect of methodology and implication is recommended.

� The abstract needs to be revised and edited for improved clarity and simplicity.

2. Introduction: Well-crafted and clearly conveys the purpose. Nicely presented setting the stage for the rest of the content.

3. Conceptual and Computational Frameworks: The paragraphs under both topics are very elaborative and verbose (some are more than 20 lines), but it is overwhelming and makes it challenging to follow the flow of ideas. So, breaking it into smaller paragraphs and making it precise will enhance clarity and better understanding.

4. Conclusions and Recommendations:

- Should be number 5, not 4

- It is well-written

Reference: OK,

- Some of the references have a reference code and most don’t have it. For example, reference number 8, 23, 26, 30, 35, and 37. But most of the reference lacks it. So, make it uniform and correct it all according to the journal’s referencing style.

Reviewer #2: The present article focuses on adequate preparedness for health emergencies caused by global pandemics like COVID-19. The authors proposed a theoretical framework required for addressing many questions related to the transmission dynamics and disease trajectory of these pandemics. They introduced a hybrid intelligence model that combines human and artificial intelligence that may offer a viable solution by processing data from various sources. Eventually, it effectively mimics the social processes surrounding transmission while incorporating human interpretation to enhance our understanding of pandemics.

The topic addressed here is extremely interesting, and their simulation-backed results reported in the following part anyhow justify the strength of their model. At the same time, it also gives a solid reason to address four fundamental characteristics of an epidemic that need to be modelled by an Artificial Intelligence tool. Undoubtedly the authors dedicated themselves to performing a comprehensive analysis of the model they proposed.

However, after reviewing the manuscript (MS) is that the work in its present form still has several weaknesses. I believe, the manuscript is still lacking some crucially important points, which requires a thorough revision before getting accepted for publication in PLOS ONE.

To this end, I would like to ask the authors to append a few additional information, as listed below, that will drive to a substantial improvement to the following asking issues.

Comment #1.

The introduction part seems too short to describe some motivational facts behind this study. I would like to encourage the authors to make a robust discussion highlighting the influence of individual decision-making in uncertain environments. In addition, they may cite some articles showcasing the necessity of classical game models to trace out the opinion dynamics as well as its impact on public decision-making. Inline to that they may take help from the following textbooks/articles:

(i) Heidecke et al.; A mathematical model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities, PLOS ONE, 2024.

(ii) Tanimoto Jun; Sociophysics Approach to Epidemics, Springer, 2021.

(iii) Kim et al.; Tracing and testing multiple generations of contacts to COVID-19 cases: cost-benefit trade-offs, ROYAL SOCIETY OPEN SCIENCE, 2022.

(iv) Tanimoto Jun; Fundamentals of Evolutionary Game Theory and its Applications, Springer, 2015.

(v) Maria Martcheva; An Introduction to Mathematical Epidemiology; Springer, 2015.

(vi) Ghosh et al.; A mathematical model for COVID-19 considering waning immunity, vaccination and control measures, Scientific reports, 2023.

(vii) Daems et al.; The Race for COVID-19 Vaccines: Accelerating Innovation, Fair Allocation and Distribution, Vaccines, 2022

Comment #2.

In your proposed model, you have mentioned the basic division of variables fall into two categories, namely ecological and epidemiological. Can you please justify which type of variable is more influential while modeling an epidemic like COVID-19?

Comment #3.

The authors are encouraged to add different types of decision-making protocols into the probabilistic model they used. I believe, it would be more interesting for the general audience of PLOS ONE. For example, they can cite the following recently published articles to catch things up while elaborating the updating protocols.

(i). Mansura et. al.; An in-silico game theoretic approach for health intervention efficacy assessment, Healthcare Analytics, Vol. 5, 100318, 2024.

(ii). Kulsum et. al.; Modeling and investigating the dilemma of early and delayed vaccination driven by the dynamics of imitation and aspiration, Chaos, Solitons and Fractals, Vol. 178, 114364, 2024.

(iii). Alam et al.; A Game-Theoretic Modeling Approach to Comprehend the Advantage of Dynamic Health Interventions in Limiting the Transmission of Multi-Strain Epidemics, Journal of Applied Mathematics and Physics 10 (12), 3700-3748, 2022.

Comment #4.

It may also improve their current MS if the figure captions would be made more self-contained. More precisely, one could also consider a sentence or two saying what is the central theme or message of each figure. Also, the authors are requested to add more visuals that can convey the central theme of the proposed model for general readers.

In this regard, the authors may cite the following works:

(i). Tatsukawa et. al.; Stochasticity of disease spreading derived from the microscopic simulation approach for various physical contact networks, Applied Mathematics and Computation, 431(80):127328, 2022.

(ii). Alam et al.; A Game-Theoretic Modeling Approach to Comprehend the Advantage of Dynamic Health Interventions in Limiting the Transmission of Multi-Strain Epidemics, Journal of Applied Mathematics and Physics 10 (12), 3700-3748, 2022.

Comment #5.

I would suggest the authors to extend their abstract more with the key results. As it is, the abstract is a little thin and does not quite convey the interesting results that follow in the main paper.

Besides as above, I have no further comments, and I expect a thoroughly revised version of this manuscript would be a great contribution for PLOS ONE.

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Reviewer #1: No

Reviewer #2: No

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A Conceptual and Computational Framework for Modeling the Complex, Adaptive Dynamics of Epidemics: the case of the SARS-CoV-2 pandemic in Mexico.

PONE-D-24-43200R1

Dear Dr. Juan Pablo Gutierrez,

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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Kind regards,

Youssef El Khatib, Ph.D.

Academic Editor

PLOS ONE

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