Overcoming vaccine hesitancy by multiplex social network targeting: an analysis of targeting algorithms and implications

Applied Network Science

Research

Marzena Fügenschuh¹  Feng Fu² 

[1] Berliner Hochschule für Technik, Luxemburgerstr. 10, 13353, Berlin, Germany;Department of Mathematics, Dartmouth College, 03755, Hanover, NH, USA;

关键词: Opinion formation;    Dueling contagions;    Multilayer networks;    Network-based interventions;    Influence maximization;   

DOI  :  10.1007/s41109-023-00595-y

 received in 2023-02-20, accepted in 2023-09-13,  发布年份 2023

来源: Springer

【 摘 要 】

Incorporating social factors into disease prevention and control efforts is an important undertaking of behavioral epidemiology. The interplay between disease transmission and human health behaviors, such as vaccine uptake, results in complex dynamics of biological and social contagions. Maximizing intervention adoptions via network-based targeting algorithms by harnessing the power of social contagion for behavior and attitude changes largely remains a challenge. Here we address this issue by considering a multiplex network setting. Individuals are situated on two layers of networks: the disease transmission network layer and the peer influence network layer. The disease spreads through direct close contacts while vaccine views and uptake behaviors spread interpersonally within a potentially virtual network. The results of our comprehensive simulations show that network-based targeting with pro-vaccine supporters as initial seeds significantly influences vaccine adoption rates and reduces the extent of an epidemic outbreak. Network targeting interventions are much more effective by selecting individuals with a central position in the opinion network as compared to those grouped in a community or connected professionally. Our findings provide insight into network-based interventions to increase vaccine confidence and demand during an ongoing epidemic.

【 授权许可】

CC BY   
© Springer Nature Switzerland AG 2023

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