# Concurrency-induced transitions in epidemic dynamics on temporal networks

## TL;DR

This paper investigates how the number of concurrent contacts in dynamic social networks influences epidemic thresholds, revealing that low concurrency suppresses while high concurrency enhances epidemic spread, with analytical and simulation validation.

## Contribution

It provides a theoretical framework for understanding how concurrency affects epidemic thresholds on temporal networks, highlighting a phase transition in epidemic dynamics.

## Key findings

- Low concurrency can suppress epidemics by raising the epidemic threshold.
- High concurrency can promote epidemics by lowering the epidemic threshold.
- The study identifies distinct phases of concurrency-induced transition in epidemic spread.

## Abstract

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the nodes' concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.