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Effectiveness of contact tracing on networks with cliques

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posted on 2024-02-15, 09:57 authored by Abbas K. Rizi, Leah KeatingLeah Keating, James GleesonJames Gleeson, DAVID O'SULLIVANDAVID O'SULLIVAN, Mikko Kivelä

Contact tracing, the practice of isolating individuals who have been in contact with infected individuals, is an effective and practical way of containing disease spread. Here we show that this strategy is particularly effective in the presence of social groups: Once the disease enters a group, contact tracing not only cuts direct infection paths but can also pre-emptively quarantine group members such that it will cut indirect spreading routes. We show these results by using a deliberately stylized model that allows us to isolate the effect of contact tracing within the clique structure of the network where the contagion is spreading. This will enable us to derive mean-field approximations and epidemic thresholds to demonstrate the efficiency of contact tracing in social networks with small groups. This analysis shows that contact tracing in networks with groups is more efficient the larger the groups are. We show how these results can be understood by approximating the combination of disease spreading and contact tracing with a complex contagion process where every failed infection attempt will lead to a lower infection probability in the following attempts. Our results illustrate how contact tracing in real-world settings can be more efficient than predicted by models that treat the system as fully mixed or the network structure as locally treelike.


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Physical Review E, 2024, 109, 024303


American Physical Society

Other Funding information

A.K.R. would like to thank Lasse Leskelä and Takayuki Hiraoka for the fruitful discussion during the preparation of this work. The simulations presented above were performed using computer resources within the Aalto University School of Science “Science-IT” project. A.K.R. and M.K. acknowledge funding from Project No. 105572 NordicMathCovid which is part of the Nordic Programme on Health and Welfare funded by NordForsk. This work was also supported by the Academy of Finland (Grants No. 349366 and No. 353799) and by Science Foundation Ireland [Grants No. 18/CRT/6049 (L.A.K.), No. 16/IA/4470 (J.P.G.), No. 16/RC/3918 (J.P.G.), and No. 12/RC/2289 P2 (J.P.G.)] with cofunding from the European Regional Development Fund. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

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