The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading

Marco Cremonini,Samira Maghool

doi:10.1038/s41598-021-04589-7

Abstract

In network models of propagation processes, the individual, microscopic level perspective is the norm, with aggregations studied as possible outcomes. On the contrary, we adopted a mesoscale perspective with groups as the core element and in this sense we present a novel agent-group dynamic model of propagation in networks. In particular, we focus on ephemeral groups that dynamically form, create new links, and dissolve. The experiments simulated 160 model configurations and produced results describing cases of consecutive and non-consecutive dynamic grouping, bounded or unbounded in the number of repetitions. Results revealed the existence of complex dynamics and multiple behaviors. An efficiency metric is introduced to compare the different cases. A Null Model analysis disclosed a pattern in the difference between the group and random models, varying with the size of groups. Our findings indicate that a mesoscopic construct like the ephemeral group, based on assumptions about social behavior and absent any microscopic level change, could produce and describe complex propagation dynamics. A conclusion is that agent-group dynamic models may represent a powerful approach for modelers and a promising new direction for future research in models of coevolution between propagation and behavior in society.

Highlights

In network models of propagation processes, the individual, microscopic level perspective is the norm, with aggregations studied as possible outcomes
A detail to highlight is that we studied the second propagation dynamic as the result of a continuous propagation process that produces a first dynamic, when a threshold on the number of spreading agents (I) is reached in the declining phase, it triggers the dynamic grouping mechanism and a flex is produced, starting the second dynamic
The choice of these values has been driven by the extremes: on the one side we wish to test the case of a single large group ( #G = 1 ), which mimics a large gathering of loosely connected agents, and on the other the case of a multitude of small groups ( #G = 1000 ), tightly connected by dynamic links but in large majority inactive because without spreading agents

Summary

Introduction

In network models of propagation processes, the individual, microscopic level perspective is the norm, with aggregations studied as possible outcomes. The model takes inspiration from what we learned during the COVID-19 pandemic: increased urban mobility and gatherings have been observed not just as a consequence of the decision of health authorities to remove social restrictions; instead the phenomena has been documented starting right after the peak of the infection and increasing while approaching the lifting of social r estrictions[2] This spontaneous behavior, seemingly anticipating the decisions of health authorities, has effects that make the epidemic dynamics in the declining part a co-evolutionary process with a rich behavioral component still largely unaccounted in research. This work moves in the direction of integrating an instance of collective behavior into agent-based network models of epidemics, with the specific focus on ephemeral groups, as we have dubbed the particular mesoscale structure of our interest. In addition and of particular relevance is the Null Model analysis evaluating our ephemeral groups model with respect to two random models and a special case considering a non-overlapping version of our model

Methods

Results

Conclusion