通过集成的动态和网络模型,通过同质和异质人群的错误信息揭开疫苗接种行为
Komal Tanwar1, Viney Kumar2, Manish Dev Shrimali3
1Department of Mathematics, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer 305801, Rajasthan, India.
Computers in biology and medicine
|February 12, 2026
概括
错误信息显著降低了疫苗接种率,特别是在同质的人口中. 异质人群和小世界网络对疫苗错误信息的抵御力更强,突出了针对性公共卫生战略的必要性.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 疫苗错误信息对公共卫生构成重大威胁,减少了疫苗接种率,增加了疾病负担.
- 了解人口异质性对于减轻错误信息的影响至关重要,尤其是在疫苗效率低的情况下.
研究的目的:
- 量化错误信息对接种疫苗接种的影响.
- 探索人口异质性如何影响疫苗错误信息的影响.
- 分析网络拓在疾病传播和疫苗接种行为中的作用.
主要方法:
- 采用了双重方法,结合了分区建模和复杂网络分析.
- 检查了影响疾病传播和疫苗接种行为的流行病学参数.
- 在真实世界的联系网络上验证了模型.
主要成果:
- 错误信息显著降低了疫苗接种率,特别是在同质人群中,这些人群的抵抗力较低.
- 不同质的群体和小世界网络对错误信息的抵御力更强.
- 无规模网络显示疫苗覆盖率降低,错误信息放大率更高.
结论:
- 公共卫生工作必须优先考虑打击错误信息,特别是在同质的人群和无网络中.
- 建立社区网络多样性和促进可靠的疫苗信息可以提高疫苗接种率.
- 针对小世界网络的公共卫生运动可能会改善疫苗接种率,即使疫苗的疗效不同.
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