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首页> 外文会议>BMEI 2012;International Conference on Biomedical Engineering and Informatics >A ripple-spreading network model for the study of infectious disease transmission
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A ripple-spreading network model for the study of infectious disease transmission

机译：研究传染病传播的波纹扩散网络模型

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摘要

Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential of capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process can well simulate the effect of random contacts and movements of individuals on the probability of infection, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals' physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and polices, and it is highly flexible to modifications. Genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the propose method is illustrated by a preliminary study.
机译：数学分析和建模是传染病流行病学的核心。在自然波纹扩散现象的启发下，本文提出了一种新型的波纹扩散网络模型，用于研究传染病的传播。新的流行病模型自然具有捕获鼠疫暴发中观察到的许多时空特征的良好潜力。尤其是，使用随机波纹扩展过程可以很好地模拟随机接触和个体移动对感染概率的影响，这在流行病建模中通常是一个具有挑战性的问题。诸如阈值和节点放大因子之类的一些与涟波扩散相关的参数非常适合描述个人身体健康和免疫力的重要性。新模型具有丰富的参数，可以纳入许多实际因素，例如公共卫生服务和政策，并且修改非常灵活。遗传算法用于通过参考流行病的历史数据来调整模型的参数。经过良好调整的模型可以用于分析和预测目的。初步研究表明了该方法的有效性。

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