陈腾飞, 龙华, 邵玉斌, 杜庆治, 张亚楠, 宋肖肖. 基于个体行为的传染病模型研究与分析[J]. 疾病监测, 2022, 37(6): 813-820. DOI: 10.3784/jbjc.202111210597
引用本文: 陈腾飞, 龙华, 邵玉斌, 杜庆治, 张亚楠, 宋肖肖. 基于个体行为的传染病模型研究与分析[J]. 疾病监测, 2022, 37(6): 813-820. DOI: 10.3784/jbjc.202111210597
Chen Tengfei, Long Hua, Shao Yubin, Du Qingzhi, Zhang Yanan, Song Xiaoxiao. Research and analysis of infectious disease model based on individual behavior[J]. Disease Surveillance, 2022, 37(6): 813-820. DOI: 10.3784/jbjc.202111210597
Citation: Chen Tengfei, Long Hua, Shao Yubin, Du Qingzhi, Zhang Yanan, Song Xiaoxiao. Research and analysis of infectious disease model based on individual behavior[J]. Disease Surveillance, 2022, 37(6): 813-820. DOI: 10.3784/jbjc.202111210597

基于个体行为的传染病模型研究与分析

Research and analysis of infectious disease model based on individual behavior

  • 摘要:
      目的  为了充分考虑种群中个体差异和人员流动对传染病发展的影响,构建一种基于个体行为的传染病模型来揭示传染病传播的动力学特征,对疫情防控措施的有效性进行评价。
      方法  以个体为基本研究对象建立仿真模型,为个体属性赋予不同数值以体现个体间的差异性,将影响传染病传播的因素以参数形式引入模型,通过改变个体的属性值来反映传染病流行期间个体的状态变化。
      结果  通过对相关参数的设置,模型仿真结果与疫情发展趋势高度吻合,并以武汉地区新型冠状病毒肺炎疫情的基本再生数为指标,验证了模型的有效性。 在此基础上,进一步讨论了个体社交活跃度对疫情发展的影响,模拟了不同防控措施下疫情的发展趋势。
      结论  该模型参数灵活,适用于传染病在多种情况下的趋势分析,能对防控措施的有效性进行科学评价,为疫情防控提供科学指导。

     

    Abstract:
      Objective   In order to fully consider the impact of individual differences and personnel movement in population on the spread of infectious diseases, an infectious disease model based on individual behavior was established to reveal the dynamic characteristics of disease spread and evaluate the effectiveness of epidemic prevention and control measures.
      Methods   The model was established based on individual subject, which gives different values to individual attributes to reflect the differences between individuals. The factors affecting the spread of infectious disease were introduced into the model in the form of parameters, and the individual state changes during the epidemic of infectious diseases were reflected by changing the individual attribute values.
      Results   Through the setting and adjustment of relevant parameters, the result of the model was highly consistent with the epidemic trend. The model was validated by using the basic reproduction number of COVID-19 in Wuhan. On this basis, the impact of individual social activity on the development of epidemic was discussed, and the trend of infectious disease epidemic under different prevention and control measures was simulated.
      Conclusion   The parameters in this model are flexible, which are suitable for the analysis on infectious diseases in various situations and can be used to evaluate the effectiveness of prevention and control measures to provide guidance for epidemic prevention and control.

     

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