Modeling the effect of observational social learning on parental decision-making for childhood vaccination and diseases spread over household networks.

Frontiers in epidemiology Pub Date : 2024-01-12 eCollection Date: 2023-01-01 DOI:10.3389/fepid.2023.1177752
Tamer Oraby, Andras Balogh
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Abstract

In this paper, we introduce a novel model for parental decision-making about vaccinations against a childhood disease that spreads through a contact network. This model considers a bilayer network comprising two overlapping networks, which are either Erdős-Rényi (random) networks or Barabási-Albert networks. The model also employs a Bayesian aggregation rule for observational social learning on a social network. This new model encompasses other decision models, such as voting and DeGroot models, as special cases. Using our model, we demonstrate how certain levels of social learning about vaccination preferences can converge opinions, influencing vaccine uptake and ultimately disease spread. In addition, we explore how two different cultures of social learning affect the establishment of social norms of vaccination and the uptake of vaccines. In every scenario, the interplay between the dynamics of observational social learning and disease spread is influenced by the network's topology, along with vaccine safety and availability.

模拟观察性社会学习对家长儿童疫苗接种决策和家庭网络传播疾病的影响。
在本文中,我们介绍了一种新型模型,用于家长对通过接触网络传播的儿童疾病进行疫苗接种的决策。该模型考虑了由两个重叠网络组成的双层网络,这两个重叠网络要么是厄尔多斯-雷尼(随机)网络,要么是巴拉巴西-阿尔伯特网络。该模型还采用了贝叶斯聚合规则,用于社会网络上的观察性社会学习。这个新模型包括其他决策模型,如投票模型和 DeGroot 模型,作为特例。利用我们的模型,我们展示了有关疫苗接种偏好的社会学习如何在一定程度上汇聚意见,从而影响疫苗接种率并最终影响疾病传播。此外,我们还探讨了两种不同的社会学习文化如何影响疫苗接种社会规范的建立和疫苗的接种。在每种情况下,观察性社会学习的动态与疾病传播之间的相互作用都会受到网络拓扑结构以及疫苗安全性和可用性的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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