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

Area of Science:

• Epidemiology
• Network Science
• Sociology

Background:

• Non-pharmaceutical interventions (NPIs) effectively control epidemics but negatively impact social interactions and collaborations.
• Complex idea development and innovation often rely on face-to-face interactions, which are hindered by strict NPIs.
• A balance is needed between epidemic containment and maintaining essential social and collaborative activities.

Purpose of the Study:

• To propose and evaluate a hybrid approach that mitigates epidemic spread while preserving face-to-face interactions.
• To investigate the trade-offs between epidemic control and the diffusion of complex knowledge.
• To develop a flexible model adaptable to varying disease and knowledge diffusion dynamics.

Main Methods:

• Simulated simultaneous spread of a disease and knowledge on a contact network.
• Implemented a two-step population partitioning: spatial clustering into 'social bubbles' and temporal pairing of nodes.
• Tuned clustering levels to assess their impact on epidemic diffusion and knowledge sharing.

Main Results:

• The proposed hybrid approach demonstrates improved trade-offs between epidemic control and complex knowledge diffusion.
• Adjusting spatial ('social bubbles') and temporal clustering effectively balances disease containment and interaction preservation.
• The model's versatility allows for optimization based on specific disease and knowledge characteristics.

Conclusions:

• A hybrid strategy offers a viable alternative to traditional NPIs, minimizing negative social and collaborative consequences.
• Dynamic adjustment of social and temporal clustering is key to optimizing public health interventions.
• This approach provides a framework for managing simultaneous health and societal needs during epidemics.