Temporal clustering of social interactions trades-off disease spreading and knowledge diffusion

TL;DR

This paper introduces a hybrid clustering approach to balance epidemic control with the preservation of face-to-face interactions, enhancing disease mitigation while supporting social and knowledge exchanges.

Contribution

It proposes a novel two-step clustering method that adjusts social and temporal interactions to optimize epidemic control and social knowledge diffusion.

Findings

Hybrid clustering improves epidemic control and knowledge diffusion trade-offs.

Tuning clustering levels adapts to changing disease or diffusion characteristics.

Model offers flexible, optimized social interaction management.

Abstract

Non-pharmaceutical measures such as preventive quarantines, remote working, school and workplace closures, lockdowns, etc. have shown effectivenness 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. 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.