Sideward contact tracing and the control of epidemics in large gatherings

被引:11
|
作者
Mancastroppa, Marco [1 ,2 ]
Guizzo, Andrea [1 ,2 ]
Castellano, Claudio [3 ]
Vezzani, Alessandro [1 ,2 ,4 ]
Burioni, Raffaella [1 ,2 ]
机构
[1] Univ Parma, Dipartimento Sci Matemat Fis & Informat, Parco Area Sci 7-A, I-43124 Parma, Italy
[2] Ist Nazl Fis Nucl, Grp Collegato Parma, Sez Milano Bicocca, Parco Area Sci 7-A, I-43124 Parma, Italy
[3] Ist Sistemi Complessi ISC CNR, Via Taurini 19, I-00185 Rome, Italy
[4] Ist Mat Elettron & Magnetismo IMEM CNR, Parco Area Sci 37-A, I-43124 Parma, Italy
关键词
temporal networks; epidemic spreading; contact tracing; empirical group distribution; COVID-19; SOCIAL NETWORKS; IMPACT;
D O I
10.1098/rsif.2022.0048
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:11
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