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<p>This paper explores an application of Membrane Systems, also known as P Systems, in the field of infectious diseases epidemiology research. The objective is to use the theoretical foundations of P Systems to enhance our understanding of epidemiological dynamics, and develop a model that integrates various aspects for simulating complex scenarios of communicable diseases. The article draws inspiration from existing research that employs P Systems to model epidemiological processes, particularly in the context of COVID-19. These studies yet highlight the advantages of using membrane models, such as the scalability, flexibility, and ability to capture hierarchical relationships within scenarios. The proposed model incorporates a population structure, with individual properties and infection transmission rules, to generate a disease dynamics, according to a human dynamic behavior logic which creates realistic simulation scenarios. The analysis of experimental results reveals valuable insights, including the impact of vaccination coverage, the timing of contagion peaks, and the predictive accuracy of the model. The results emphasize the importance of vaccination in controlling the spread of infectious diseases, and highlight the influence of population awareness and caution on disease dynamics.</p>

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A dynamic behavior epidemiological model by membrane systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月1日

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  • Davide Valcamonica,
  • Alberto d’Onofrio,
  • Muhammad Mazhar Fareed,
  • Giuditta Franco,
  • Claudio Zandron

摘要

This paper explores an application of Membrane Systems, also known as P Systems, in the field of infectious diseases epidemiology research. The objective is to use the theoretical foundations of P Systems to enhance our understanding of epidemiological dynamics, and develop a model that integrates various aspects for simulating complex scenarios of communicable diseases. The article draws inspiration from existing research that employs P Systems to model epidemiological processes, particularly in the context of COVID-19. These studies yet highlight the advantages of using membrane models, such as the scalability, flexibility, and ability to capture hierarchical relationships within scenarios. The proposed model incorporates a population structure, with individual properties and infection transmission rules, to generate a disease dynamics, according to a human dynamic behavior logic which creates realistic simulation scenarios. The analysis of experimental results reveals valuable insights, including the impact of vaccination coverage, the timing of contagion peaks, and the predictive accuracy of the model. The results emphasize the importance of vaccination in controlling the spread of infectious diseases, and highlight the influence of population awareness and caution on disease dynamics.