Polarization of opinions about vaccination can have a negative impact on pandemic control. In this work we quantify this negative impact for the transmission of COVID-19, using an agent based simulation in an heterogeneous population with multi-type networks, representing different types of social interactions. We show that the clustering of unvaccinated individuals, associated with polarization of opinion, can lead to significant differences in the evolution of the pandemic compared to deterministic model predictions. Under our realistic baseline scenario these differences are a 33pc increase of the effective reproduction number, a 157pc increase of infections at the peak and a 30pc increase in the final cumulative attack rate.
翻译:在这项工作中,我们使用一种基于代理的模拟方法,在代表不同类型社会互动的多类型网络的多种人群中进行模拟,对COVID-19传播的负面影响进行量化;我们表明,与意见的两极分化相关联,将未接种人员集中在一起,可导致与确定性模型预测相比,该流行病的演变出现巨大差异;在现实的基线假设下,这些差异是有效生殖数增加33分,高峰期感染增加157分,最后累积袭击率增加30分。