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人类行为对传染病期间病毒株进化的影响。

Effect of Human Behavior on the Evolution of Viral Strains During an Epidemic.

机构信息

Department of Mathematics, Kennesaw State University, Marietta, GA, 30060, USA.

Department of Mathematics, University of Georgia, Athens, GA, 30602, USA.

出版信息

Bull Math Biol. 2022 Nov 5;84(12):144. doi: 10.1007/s11538-022-01102-7.

Abstract

It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here, we explore a complementary trend: the pathogen itself might experience a force of selection to become less "visible," or less "symptomatic," in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.

摘要

文献中众所周知,人类的行为会随着对他人疾病的观察而发生变化,而这种行为变化可能是传染病传播的一个重要因素。人们注意到,在传染病存在的情况下,人类在疾病预防方面的行为特征受到选择的影响。在这里,我们探讨了一个补充趋势:病原体本身可能会经历一种选择压力,使其在这种人类行为趋势存在的情况下变得不那么“明显”或不那么“有症状”。我们使用基于随机 SIR 代理的模型,研究了两种具有交叉免疫性的病毒株的共同进化,其中常驻株具有症状,而突变株无症状。我们假设,如果一个邻居感染了有症状的菌株,个体就会进行自我调节的社交隔离(SD)行为。我们观察到,无症状携带者的比例随着时间的推移而增加,自我调节 SD 的水平越高,效果越明显。增加强制性 SD 使效果更加显著,而感染和行为改变之间的时间延迟的存在则降低了无症状菌株的优势。这些结果在随机几何网络、无标度网络和代表新奥尔良居民社会行为的合成网络中是一致的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/496b/9638455/aeb3063cc351/11538_2022_1102_Fig1_HTML.jpg

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