由疫苗有效性和信念驱动的疫苗接种的社会模仿动态。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-10-13 eCollection Date: 2025-10-01 DOI:10.1371/journal.pcbi.1013586
Feng Fu, Ran Zhuo, Xingru Chen
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引用次数: 0

摘要

麻疹和水痘等疫苗可预防疾病的疫苗接种覆盖率下降,使这些疾病出人意料地卷土重来,并在人们对疫苗越来越犹豫之后构成重大的公共卫生挑战。选择退出和拒绝接种疫苗往往是由于对疫苗有效性的看法和夸大风险的信念。在这里,我们量化了相互竞争的信念-疫苗厌恶与疫苗中立-对疫苗接种的社会模仿动态的影响,以及疾病传播的流行病学动态。这些信念可能是预先存在的和固定的,或者是共同进化的态度。信念、行为和疾病动态之间的相互作用表明,个体并非完全理性;相反,他们根据信仰、个人经历和社会影响来决定是否接种疫苗。我们发现,一小部分固定的疫苗厌恶信念的存在会显著加剧疫苗接种困境,使滞后回路的临界点对个体接种疫苗的感知成本和疫苗有效性的变化更加敏感。然而,在相互竞争的信念与疫苗接种行为同时传播的情况下,它们的双刃剑影响可能导致疫苗信念和行为之间的自我纠正和一致。结果表明,与没有信念的情况相比,疫苗信念和行为的共同进化使人群对疫苗成本和有效性观念的突变更加敏感。我们的工作为利用疫苗中立态度的社会传染来克服疫苗犹豫提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social imitation dynamics of vaccination driven by vaccine effectiveness and beliefs.

Declines in vaccination coverage for vaccine-preventable diseases, such as measles and chickenpox, have enabled their surprising comebacks and pose significant public health challenges in the wake of growing vaccine hesitancy. Vaccine opt-outs and refusals are often fueled by beliefs concerning perceptions of vaccine effectiveness and exaggerated risks. Here, we quantify the impact of competing beliefs - vaccine-averse versus vaccine-neutral - on social imitation dynamics of vaccination, alongside the epidemiological dynamics of disease transmission. These beliefs may be pre-existing and fixed, or coevolving attitudes. This interplay among beliefs, behaviors, and disease dynamics demonstrates that individuals are not perfectly rational; rather, they base their vaccine uptake decisions on beliefs, personal experiences, and social influences. We find that the presence of a small proportion of fixed vaccine-averse beliefs can significantly exacerbate the vaccination dilemma, making the tipping point in the hysteresis loop more sensitive to changes in individuals' perceived costs of vaccination and vaccine effectiveness. However, in scenarios where competing beliefs spread concurrently with vaccination behavior, their double-edged impact can lead to self-correction and alignment between vaccine beliefs and behaviors. The results show that coevolution of vaccine beliefs and behaviors makes populations more sensitive to abrupt changes in perceptions of vaccine cost and effectiveness compared to scenarios without beliefs. Our work provides valuable insights into harnessing the social contagion of even vaccine-neutral attitudes to overcome vaccine hesitancy.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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