Modeling the effects of social distancing on the large-scale spreading of diseases

TL;DR

This paper models how social distancing strategies, both local and global, influence the large-scale spread of infectious diseases, highlighting the importance of human interaction complexity in epidemic outcomes.

Contribution

It introduces a generalized metapopulation model incorporating social distancing mechanisms at the subpopulation level with local and global information inputs.

Findings

Diverse epidemic outcomes depend on social distancing implementation.

No single approach is universally more effective; outcomes vary with interaction complexity.

Model results emphasize the importance of considering human interaction complexity in policy design.

Abstract

To contain the propagation of emerging diseases that are transmissible from human to human, non-pharmaceutical interventions (NPIs) aimed at reducing the interactions between humans are usually implemented. One example of the latter kind of measures is social distancing, which can be either policy-driven or can arise endogenously in the population as a consequence of the fear of infection. However, if NPIs are lifted before the population reaches herd immunity, further re-introductions of the pathogen would lead to secondary infections. Here we study the effects of different social distancing schemes on the large scale spreading of diseases. Specifically, we generalize metapopulation models to include social distancing mechanisms at the subpopulation level and model short- and long-term strategies that are fed with local or global information about the epidemics. We show that different model ingredients might lead to very diverse outcomes in different subpopulations. Our results suggest that there is not a unique answer to the question of whether contention measures are more efficient if implemented and managed locally or globally and that model outcomes depends on how the full complexity of human interactions is taken into account.