We present and use an agent-based model to study interventions for suppression, mitigation, and vaccination in coping with the COVID-19 pandemic. Unlike metapopulation models, our agent-based model permits experimenting with micro-level interventions in social interactions at individual sites. We compare common macro-level interventions applicable to everyone (e.g., keep distance, close all schools) to targeted interventions in the social network spanned by households based on specific (potential) transmission rates (e.g., prohibit visiting spreading hubs or bridging ties). We show that, in the simulation environment, micro-level measures of 'locking' of a number of households and 'blocking' access to a number of sites (e.g., workplaces, schools, recreation areas) using social network centrality metrics permits refined control on the positioning on the immunity-mortality curve. In simulation results, social network metric-based vaccination of households offers refined control and reduces the spread saliently better than random vaccination.

中文翻译：

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社交网络基于度量的干预？在大都市地区使用基于代理的 COVID-19 大流行模型进行实验

我们提出并使用基于代理的模型来研究应对 COVID-19 大流行的抑制、缓解和疫苗接种干预措施。与元种群模型不同，我们的基于代理的模型允许在单个站点的社交互动中尝试微观层面的干预。我们将适用于每个人的常见宏观干预措施（例如，保持距离、关闭所有学校）与基于特定（潜在）传播率的家庭所跨越的社交网络中的有针对性的干预措施（例如，禁止访问传播中心或搭桥关系）进行比较。我们表明，在模拟环境中，“锁定”多个家庭和“阻止”访问多个站点（例如，工作场所、学校、娱乐区）使用社交网络中心性指标可以对免疫死亡率曲线上的位置进行精确控制。在模拟结果中，基于社交网络度量的家庭疫苗接种提供了精细的控制，并且比随机疫苗接种明显更好地减少了传播。