Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy.
INFN, Sezione di Milano Bicocca, Gruppo Collegato di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy.
J R Soc Interface. 2022 May;19(190):20220048. doi: 10.1098/rsif.2022.0048. Epub 2022 May 11.
Effective contact tracing is crucial to containing epidemic spreading without disrupting societal activities, especially during a pandemic. Large gatherings play a key role, potentially favouring superspreading events. However, the effects of tracing in large groups have not been fully assessed so far. We show that in addition to forward tracing, which reconstructs to whom the disease spreads, and backward tracing, which searches from whom the disease spreads, a third 'sideward' tracing is always present, when tracing gatherings. This is an indirect tracing that detects infected asymptomatic individuals, even if they have been neither directly infected by nor directly transmitted the infection to the index case. We analyse this effect in a model of epidemic spreading for SARS-CoV-2, within the framework of simplicial activity-driven temporal networks. We determine the contribution of the three tracing mechanisms to the suppression of epidemic spreading, showing that sideward tracing induces a non-monotonic behaviour in the tracing efficiency, as a function of the size of the gatherings. Based on our results, we suggest an optimal choice for the sizes of the gatherings to be traced and we test the strategy on an empirical dataset of gatherings on a university campus.
有效的接触者追踪对于在不扰乱社会活动的情况下控制疫情传播至关重要,特别是在大流行期间。大型集会起着关键作用,可能有利于超级传播事件的发生。然而,到目前为止,还没有充分评估对大型群体进行追踪的效果。我们表明,除了向前追踪(重建疾病向谁传播)和向后追踪(从谁那里搜索疾病传播)之外,在追踪聚会时,还始终存在第三种“侧向”追踪。这是一种间接追踪,即使感染的无症状个体既未被直接感染,也未将感染直接传播给索引病例,也能检测到这些个体。我们在 SARS-CoV-2 的传染病传播模型中,在单纯形活动驱动的时间网络框架内分析了这种效应。我们确定了三种追踪机制对抑制传染病传播的贡献,表明侧向追踪会导致追踪效率随聚会规模的增大而出现非单调行为。根据我们的结果,我们建议选择要追踪的聚会规模的最佳选择,并在大学校园的聚会数据集上测试该策略。