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<p>The emergence of novel infectious diseases, such as COVID-19, poses major challenges for disease modeling and control strategies. Understanding transmission dynamics is crucial for effective decision-making and public health interventions. In this study, we propose a novel SPAtial DEcision-Making Support system based on a Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) epidemic model within a Scale-Free network to analyze and mitigate the spread of infectious diseases, with particular focus on COVID-19. We present innovative algorithms tailored to develop the SEIQR model within a Scale-Free network framework. To confirm the efficacy of the proposed SEIQR-SF system and deepen our epidemiological understanding, we performe an empirical case study using real-world COVID-19 data from Algeria. This involved aligning the SEIQR model with the data through machine learning methods, followed by extensive sensitivity analysis to determine the most impactful parameters influencing infection dynamics. Additionally, SHapley Additive exPlanations (SHAP) values were calculated to assess the contributions of key factors to the model's predictions. The findings provided valuable insights into varying degrees of epidemiological risk. Thematic maps depicting incidence severity and risk graphs presenting the evolution of the epidemic over time were created to categorize regions. The strong correlation between the SEIQR model's numerical simulations in a Scale-Free network and real data, combined with Mean Absolute Error and Root Mean Squared Error metrics and reinforced by our analytical techniques, highlights the reliability and practical utility of the proposed approach for understanding and controlling epidemic spread.</p>

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Understanding the SEIQR-SF Epidemic Model System: A Comprehensive Overview of Disease Dynamics and Control Strategies

  • 期刊论文

  • 发表日期：

    2025年9月5日

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  • Fatima Zohra Younsi,
  • Ahmed Younsi,
  • Mohamed Enadir Mekhlouf

摘要

The emergence of novel infectious diseases, such as COVID-19, poses major challenges for disease modeling and control strategies. Understanding transmission dynamics is crucial for effective decision-making and public health interventions. In this study, we propose a novel SPAtial DEcision-Making Support system based on a Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) epidemic model within a Scale-Free network to analyze and mitigate the spread of infectious diseases, with particular focus on COVID-19. We present innovative algorithms tailored to develop the SEIQR model within a Scale-Free network framework. To confirm the efficacy of the proposed SEIQR-SF system and deepen our epidemiological understanding, we performe an empirical case study using real-world COVID-19 data from Algeria. This involved aligning the SEIQR model with the data through machine learning methods, followed by extensive sensitivity analysis to determine the most impactful parameters influencing infection dynamics. Additionally, SHapley Additive exPlanations (SHAP) values were calculated to assess the contributions of key factors to the model's predictions. The findings provided valuable insights into varying degrees of epidemiological risk. Thematic maps depicting incidence severity and risk graphs presenting the evolution of the epidemic over time were created to categorize regions. The strong correlation between the SEIQR model's numerical simulations in a Scale-Free network and real data, combined with Mean Absolute Error and Root Mean Squared Error metrics and reinforced by our analytical techniques, highlights the reliability and practical utility of the proposed approach for understanding and controlling epidemic spread.