Optimizing the SEIR Model Based on Genetic Algorithms by Incorporating Human Self-Isolation Willingness Factors_Vol. 3 No. 4 (JBDC 2025)_Journal of Big Data and Computing (ISSN: 2959-0590)

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Optimizing the SEIR Model Based on Genetic Algorithms by Incorporating Human Self-Isolation Willingness Factors

DOI: https://doi.org/10.62517/jbdc.202501434

Author(s)

Jingbo Hou*

Affiliation(s)

Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia *Corresponding Author

Abstract

Epidemics have consistently impacted human life, and the recent global outbreak of COVID-19 has profoundly affected both health and the economy. This underscores the urgency of optimizing infectious disease dynamics models and the necessity of conducting precise predictions of epidemic trends. Addressing the theoretical limitations of traditional SEIR models in capturing the dynamic nature of individual preventive behaviors, this study innovatively incorporates human self-isolation willingness as a variable into the model architecture. We focus on improving the genetic algorithm-based SEIR (GA-SEIR) model to construct an enhanced SEIR model grounded in genetic algorithms. Integrating epidemiology with computational intelligence, this study develops a multi-objective optimization model to achieve more precise predictions of epidemic trends. This supports targeted prevention and control decisions, thereby mitigating the impact of epidemics on human life. While numerous studies have extended the SEIR framework, existing research predominantly focuses on biological and physical transmission processes, with limited exploration of the dynamic factors of human individual behavior. The uniqueness of this study lies in incorporating human self-isolation willingness into the GA-SEIR model, known for its higher predictive accuracy, to further refine and optimize it. Adopting a cross-disciplinary perspective on the complex system coupling mechanism between "human behavior and disease transmission," this study employs a hybrid approach combining genetic algorithm (GA) optimization with multi-source data-driven modeling. It presents an enhanced GA-SEIR model that integrates human self-isolation factors, aiming to improve the model's predictive accuracy for epidemic spread and enhance the precision of control policies during public health emergencies.

Keywords

Genetic Algorithm (GA); SEIR Model; Human Self-Isolation Willingness; Epidemic Forecasting; COVID-19; Infectious Disease Modeling

References

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