基于社会网络指标的干预措施?基于agent的大都市地区COVID-19大流行模型实验

B. Vermeulen, Matthias Mueller, A. Pyka
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引用次数: 8

摘要

我们提出并使用基于主体的模型来研究抑制、缓解和接种疫苗的干预措施,以应对COVID-19大流行。与元人口模型不同,我们的基于主体的模型允许在个别地点的社会互动中进行微观干预实验。我们比较了适用于每个人的常见宏观层面干预措施(例如,保持距离,关闭所有学校)与基于特定(潜在)传播率(例如,禁止访问传播中心或桥接关系)的家庭跨越的社会网络中的有针对性干预措施。我们表明,在模拟环境中,使用社交网络中心性指标“锁定”一些家庭和“阻止”进入一些地点(例如,工作场所、学校、娱乐场所)的微观层面措施允许对免疫-死亡率曲线上的定位进行精细控制。在模拟结果中,基于社会网络指标的家庭疫苗接种比随机疫苗接种提供了更精细的控制,并显着降低了传播。
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Social Network Metric-Based Interventions? Experiments with an Agent-Based Model of the COVID-19 Pandemic in a Metropolitan Region
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.
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