Kati8/ICLR_2023_2025 huggingface dataset:
authors: Cloves Alberto Chaves Lima
authors: Patrícia Cabral Tedesco
reviews: 
Comments: This paper proposes a type of SEIR models that uses genetic algorithms for parameter analysis and optimisation. The dataset studied is based on SARS type diseases (e.g. flu and COVID-19), and consists of sociodemographic and behavioural data from anomalous ICDs and ICPCs, located in Recife, northeast Brazil, from April 26 2020 to March 7 2021. The model is used to study the dynamics of the disease spread and identify the most important factors for the spread of the disease. The use of genetic algorithms is shown to be effective in optimising the parameters of the model, giving better predictive results.
Strengths: - The proposed model crucially considers the problem of underreporting of cases, which is a significant issue in epidemiological modelling.
Weaknesses: - The focus of the paper is not very clear. On the one hand, the paper is about the use of genetic algorithms for parameter optimisation. On the other hand, the experiments section is simply a report of the results of their model. It would be useful to have more discussion on the use of genetic algorithms for parameter optimisation, such as the advantages and disadvantages of this approach, and how it compares to other popular methods.
- The results simply just enumerate what the model outputs, without any intuition or discussion of how good/bad the model is.
Recommendation: 1: strong reject
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 1 poor
Clarity: 1 poor
reviews: 
Comments: The authors propose an extended SEIR epidemiological model called SEPAI3R3O for analyzing disease dynamics. Unlike standard SEIR models, they divide each compartment into subpopulations based on detailed characteristics, yielding over 10 parameters. To estimate these parameters, they develop a genetic algorithm optimization approach. The method was applied to model the dynamics of COVID-19 in Recife.
Strengths: 1. More detailed modeling of population subgroups.
Weaknesses: 1. The proposed SEPAI3R3O model introduces over 10 parameters, which may be difficult to reliably estimate given limited real-world observations. Moreover, modeling these parameters as time-varying further exacerbates the challenge of estimation from scarce data. The authors should discuss the uncertainty in fitted parameter values and provide sensitivity analysis.
2. Limited Novelty. The authors apply a standard genetic algorithm approach for parameter estimation. While computationally fitting an extended SEIR model is non-trivial, the optimization methodology itself does not represent a significant novel contribution. More justification is needed on the novelty of the parameter estimation technique.
3. Unvalidated Estimates. The authors estimate under-reporting rates for infections, but it is unclear how these proposed values were validated. Further analysis should be provided to support the assumed levels of under-reporting across different model compartments.
Recommendation: 1: strong reject
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: 1 poor
Clarity: 2 fair
reviews: 
Comments: The paper introduces the SEPAI3R3O epidemiological model, an extension of the SEIR model, to analyze and predict the spread of SARS-related diseases, with a focus on COVID-19 in Recife, Brazil. It applies genetic algorithms to optimize the model parameters and make accurate predictions with normalized root mean squared error between model predictions and data as a part of their fitness function. They authors also use k-fold validation during the process of optimizing model parameters. They present results indicating an effective reproduction rate R0 of 3 and a growth rate of 0.014 for the disease using the trained model.
Strengths: - The SEPAI3R3O model incorporates useful extensions like pre-symptomatic transmission and underreporting rate to capture disease nuances.
Weaknesses: - While the application of a genetic algorithm is a legitimate method for parameter optimization, applying it to a new epidemiological model doesn’t itself establish novelty. Genetic algorithms (and other optimization methods) have been widely used in parameter learning/optimization for epidemiological models.
- The paper does not establish the novelty or the necessity of the SEPAI3R3O model modification. There are no experiments comparing the model to existing models. Without a clear distinction of how SEPAI3R3O provides substantial improvements over existing models on well-motivated experiments, the contribution seems incremental. The authors must provide a comparison with other existing models to validate the improvements that SEPAI3R3O claims to offer. This could be done by demonstrating the model's performance against other models on the same dataset or showing how the additional compartments in SEPAI3R3O lead to significantly better understanding or prediction of disease spread.
- The paper is not well-written and missing a lot of details/definitions. At the end of the day, every paper should be self-sufficient to an extent for the reader. The paper doesn’t clearly mention what the data is, how the data is transformed into a format to train the model, what exactly the model is predicting, what the metrics are, and how the metrics are justified, etc. The text should be significantly improved to aid the user in understanding their contribution.
- The paper mentions the adjustment of parameters based on COVID-19 epidemiological studies, it does not specify whether the parameters used were local to the Recife population or if they were generalized from broader studies. Furthermore, the paper estimates an underreporting rate of approximately 50% but does not delve into the methodology for reaching this estimate. Detailed information on the parameter selection process would strengthen the paper's methodological rigor.
Recommendation: 3: reject, not good enough
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 1 poor
Clarity: 2 fair