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Temporal clustering of social interactions trades-off disease spreading and knowledge diffusion
Title / Series / Name
Publication Volume
Publication Issue
Pages
Authors
Cencetti, Giulia
Lucchini, Lorenzo
Santin, Gabriele
Battiston, Federico
Moro, Esteban
Pentland, Alex
Lepri, Bruno
Lucchini, Lorenzo
Santin, Gabriele
Battiston, Federico
Moro, Esteban
Pentland, Alex
Lepri, Bruno
Editors
Keywords
complex networks
epidemics
network structures
simple and complex contagion
Biotechnology
Biophysics
Bioengineering
Biomaterials
Biochemistry
Biomedical Engineering
epidemics
network structures
simple and complex contagion
Biotechnology
Biophysics
Bioengineering
Biomaterials
Biochemistry
Biomedical Engineering
URI
https://hdl.handle.net/20.500.14018/26683
Abstract
Non-pharmaceutical measures such as preventive quarantines, remote working, school and workplace closures, lockdowns, etc. have shown effectiveness from an epidemic control perspective; however, they have also significant negative consequences on social life and relationships, work routines and community engagement. In particular, complex ideas, work and school collaborations, innovative discoveries and resilient norms formation and maintenance, which often require face-to-face interactions of two or more parties to be developed and synergically coordinated, are particularly affected. In this study, we propose an alternative hybrid solution that balances the slowdown of epidemic diffusion with the preservation of face-to-face interactions, that we test simulating a disease and a knowledge spreading simultaneously on a network of contacts. Our approach involves a two-step partitioning of the population. First, we tune the level of node clustering, creating 'social bubbles' with increased contacts within each bubble and fewer outside, while maintaining the average number of contacts in each network. Second, we tune the level of temporal clustering by pairing, for a certain time interval, nodes from specific social bubbles. Our results demonstrate that a hybrid approach can achieve better trade-offs between epidemic control and complex knowledge diffusion. The versatility of our model enables tuning and refining clustering levels to optimally achieve the desired trade-off, based on the potentially changing characteristics of a disease or knowledge diffusion process.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2024-01-03
Language
ISBN
Identifiers
10.1098/rsif.2023.0471