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复发性流行病的非周期性爆发及其网络建模。

Non-periodic outbreaks of recurrent epidemics and its network modelling.

作者信息

Zheng Muhua, Wang Chaoqing, Zhou Jie, Zhao Ming, Guan Shuguang, Zou Yong, Liu Zonghua

机构信息

Department of Physics, East China Normal University, Shanghai, 200062, China.

College of Physics and Technology, Guangxi Normal University, Guilin 541004, China.

出版信息

Sci Rep. 2015 Nov 2;5:16010. doi: 10.1038/srep16010.

Abstract

The study of recurrent epidemic outbreaks has been attracting great attention for decades, but its underlying mechanism is still under debate. Based on a large number of real data from different cities, we find that besides the seasonal periodic outbreaks of influenza, there are also non-periodic outbreaks, i.e. non-seasonal or non-annual behaviors. To understand how the non-periodicity shows up, we present a network model of SIRS epidemic with both time-dependent infection rate and a small possibility of persistent epidemic seeds, representing the influences from the larger annual variation of environment and the infection generated spontaneously in nature, respectively. Our numerical simulations reveal that the model can reproduce the non-periodic outbreaks of recurrent epidemics with the main features of real influenza data. Further, we find that the recurrent outbreaks of epidemic depend not only on the infection rate but also on the density of susceptible agents, indicating that they are both the necessary conditions for the recurrent epidemic patterns with non-periodicity. A theoretical analysis based on Markov dynamics is presented to explain the numerical results. This finding may be of significance to the control of recurrent epidemics.

摘要

几十年来,对反复出现的疫情爆发的研究一直备受关注,但其潜在机制仍存在争议。基于来自不同城市的大量真实数据,我们发现,除了流感的季节性周期性爆发外,还存在非周期性爆发,即非季节性或非年度性行为。为了理解非周期性是如何出现的,我们提出了一个SIRS疫情网络模型,该模型具有随时间变化的感染率和少量持续存在的疫情种子的可能性,分别代表了来自较大年度环境变化和自然界自发产生的感染的影响。我们的数值模拟表明,该模型可以重现反复出现的疫情的非周期性爆发,具有真实流感数据的主要特征。此外,我们发现疫情的反复爆发不仅取决于感染率,还取决于易感个体的密度,这表明它们都是具有非周期性的反复出现的疫情模式的必要条件。提出了基于马尔可夫动力学的理论分析来解释数值结果。这一发现可能对反复出现的疫情的控制具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab7d/4629194/bc1631882801/srep16010-f1.jpg

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