University of Southern California, Los Angeles, CA, USA.
Health Educ Behav. 2020 Apr;47(2):235-248. doi: 10.1177/1090198120901497. Epub 2020 Feb 24.
This study models how new ideas, practices, or diseases spread within and between communities, the diffusion of innovations or contagion. Several factors affect diffusion such as the characteristics of the initial adopters, the seeds; the structure of the network over which diffusion occurs; and the shape of the threshold distribution, which is the proportion of prior adopting peers needed for the focal individual to adopt. In this study, seven seeding conditions are modeled: (1) three opinion leadership indicators, (2) two bridging measures, (3) marginally positioned seeds, and (4) randomly selected seeds for comparison. Three network structures are modeled: (1) random, (2) small-world, and (3) scale-free. Four threshold distributions are modeled: (1) normal; (2) uniform; (3) beta 7,14; and (4) beta 1,2; all of which have a mean threshold of 33%, with different variances. The results show that seeding with nodes high on in-degree centrality and/or inverse constraint has faster and more widespread diffusion. Random networks had faster and higher prevalence of diffusion than scale-free ones, but not different from small-world ones. Compared with the normal threshold distribution, the uniform one had faster diffusion and the beta 7,14 distribution had slower diffusion. Most significantly, the threshold distribution standard deviation was associated with rate and prevalence such that higher threshold standard deviations accelerated diffusion and increased prevalence. These results underscore factors that health educators and public health advocates should consider when developing interventions or trying to understand the potential for behavior change.
本研究模型化了新思想、实践或疾病在社区内部和社区之间的传播方式，即创新或传染病的传播。几个因素会影响传播，如初始采用者（即种子）的特征、传播发生的网络结构以及阈值分布的形状，即焦点个体采用所需的先采用同伴的比例。在这项研究中，模拟了七种播种条件：（1）三种意见领袖指标，（2）两种桥接措施，（3）边缘定位的种子，（4）随机选择的种子进行比较。模拟了三种网络结构：（1）随机，（2）小世界，（3）无标度。模拟了四种阈值分布：（1）正态；（2）均匀；（3）β7,14；（4）β1,2；所有这些分布的平均阈值都为 33%，但方差不同。结果表明，采用度数中心度高和/或逆约束的节点进行播种可以实现更快、更广泛的传播。随机网络的传播速度比无标度网络快，流行度也比无标度网络高，但与小世界网络没有区别。与正态阈值分布相比，均匀分布的传播速度更快，β7,14 分布的传播速度更慢。最重要的是，阈值分布标准差与传播速度和流行度相关，即较高的阈值标准差会加速传播并增加流行度。这些结果强调了健康教育者和公共卫生倡导者在制定干预措施或试图理解行为改变的潜力时应考虑的因素。
