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疫情在状态空间和参数空间之间的悖论。

A paradox of epidemics between the state and parameter spaces.

机构信息

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

出版信息

Sci Rep. 2018 May 14;8(1):7517. doi: 10.1038/s41598-018-25931-6.

Abstract

It is recently revealed from amounts of real data of recurrent epidemics that there is a phenomenon of hysteresis loop in the state space. To understand it, an indirect investigation from the parameter space has been given to qualitatively explain its mechanism but a more convincing study to quantitatively explain the phenomenon directly from the state space is still missing. We here study this phenomenon directly from the state space and find that there is a positive correlation between the size of outbreak and the size of hysteresis loop, implying that the hysteresis is a nature feature of epidemic outbreak in real case. Moreover, we surprisingly find a paradox on the dependence of the size of hysteresis loop on the two parameters of the infectious rate increment and the transient time, i.e. contradictory behaviors between the two spaces, when the evolutionary time of epidemics is long enough. That is, with the increase of the infectious rate increment, the size of hysteresis loop will decrease in the state space but increase in the parameter space. While with the increase of the transient time, the size of hysteresis loop will increase in the state space but decrease in the parameter space. Furthermore, we find that this paradox will disappear when the evolutionary time of epidemics is limited in a fixed period. Some theoretical analysis are presented to both the paradox and other numerical results.

摘要

最近从大量的传染病复发的实际数据中发现,在状态空间中存在滞后环现象。为了理解这一现象,已经从参数空间进行了间接调查,定性地解释了其机制,但仍然缺乏更有说服力的研究来直接从状态空间定量解释这一现象。我们在这里直接从状态空间研究这一现象,发现疫情爆发的规模与滞后环的规模之间存在正相关关系,这意味着滞后是实际情况下传染病爆发的自然特征。此外,我们令人惊讶地发现滞后环的大小对感染率增量和暂态时间这两个参数的依赖性存在悖论,即当传染病的进化时间足够长时,两个空间之间的行为是矛盾的。也就是说,随着感染率增量的增加,滞后环的大小在状态空间中会减小,但在参数空间中会增加。而随着暂态时间的增加,滞后环的大小在状态空间中会增加,但在参数空间中会减小。此外,我们发现当传染病的进化时间限制在一个固定的周期内时,这个悖论将会消失。我们对这个悖论和其他数值结果进行了一些理论分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ddb/5951842/ea52c5bfaf08/41598_2018_25931_Fig1_HTML.jpg

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