reviews: 
Comments: This study proposes a variation of the SEIR model, termed SEPAI3R3O, optimized with genetic algorithms, focusing on SARS-related data in Recife, Brazil. The proposed approach, combining genetic algorithms with epidemiological modeling, is promising, and using real data is a plus in validating the approach.
Strengths: 1) Genetic algorithms to optimize the epidemic model parameters.
2) Using real data to assess the validity of the approach.
Weaknesses: Main Criticisms:
1. Insufficient Literature Review: The paper lacks a broader overview of existing
epidemiological models (see e.g. [2]) and optimization strategies (see e.g. [5-6]). Notably, there is no mention of comparable models like OpenABM [1] or gleam [3]. A section on similar works is essential to evaluate the relevance and novelty of your approach within the field.
2. Lack of Comparative Analysis: Absence of comparison with other optimization algorithms or epidemiological models weakens the paper's impact. Inclusion of comparisons, especially with differential compartmental models and agent-based models, would significantly strengthen the study.
3. Methodology and Reproducibility: The methodology is clear, but including open-source code would greatly enhance reproducibility and scientific verification.
4. Incomplete Results Section: The data presented covers a very narrow time window, which is insufficient to assess the model's predictive accuracy and applicability to broader pandemic dynamics.

Specific Observations:

• Protection Rate Definition: The term "protection rate" is not clearly defined.

• Vague statement: The author writes: "This approach, using the SEPAI3R3O model, allowed
us to obtain a more accurate representation of the spread of the virus in vulnerable areas
of Recife." More accurate with respect to?

• Inconsistencies in Data Presentation: Table 2 shows significantly different values from
other sections without adequate explanation.

• Underreporting Rate. The author writes: "When the pandemic began manifesting in mid-
March, the underreporting rate was 0.50%. " I think it is 50%, isnt'?

• Unended sentence. The sentence: "The average protection rate over the period under
analysis was 0.014 (95 ".
      
• Simulated Data on Death Rates: The paper should explain why the simulated data shows higher death rates than observed. Usually, the number of deaths is a good indicator of the outgoing epidemic status and usually is not underreported. Do you have an explanation? In this case, the reduction is about 20/30%, from what I can guess from the plot fig.1 plot c.

[1] Hinch, Robert, et al. "OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing." PLoS computational biology 17.7 (2021): e1009146.

[2] Iranzo, Valeriano, and Saúl Pérez-González. "Epidemiological models and COVID-19: a comparative view." History and Philosophy of the Life Sciences 43.3 (2021): 104.

[3] Gleam Epidemiological Model

[4] Lau, Hien, et al. "Evaluating the massive underreporting and undertesting of COVID-19 cases in multiple global epicenters." Pulmonology 27.2 (2021): 110-115.

[5] Gopal, K., Lee, L. S., & Seow, H. (2020). Parameter Estimation of Compartmental Epidemiological Model Using Harmony Search Algorithm and Its Variants. Applied Sciences, 11(3), 1138.

[6] Bent, O. E., Wachira, C., Remy, S. L., Ogallo, W., & Walcott-Bryant, A. (2021). A Framework for Inferring Epidemiological Model Parameters using Bayesian Nonparametrics. AMIA Annual Symposium Proceedings, 2021, 217-226.
Recommendation: 3: reject, not good enough
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 2 fair
Clarity: 1 poor