Epidemic spreading in populations of mobile agents with adaptive behavioral response

TL;DR

This paper introduces a model for epidemic spreading among mobile agents that incorporates local behavioral responses, revealing how such responses influence disease prevalence, phase transitions, and network structure in different density regimes.

Contribution

It presents a novel model combining adaptive behavioral responses with mobile agent epidemic dynamics, including a semi-analytic approach and analysis of phase transitions and network metrics.

Findings

Behavioral responses reduce prevalence at low densities.

An abrupt phase transition occurs at higher densities due to clustering.

The model links adaptive networks with mobility-driven epidemic models.

Abstract

Despite the advanced stage of epidemic modeling, there is a major demand for methods to incorporate behavioral responses to the spread of a disease such as social distancing and adoption of prevention methods. Mobility plays an important role in epidemic dynamics and is also affected by behavioral changes, but there are many situations in which real mobility data is incomplete or inaccessible. We present a model for epidemic spreading in temporal networks of mobile agents that incorporates local behavioral responses. Susceptible agents are allowed to move towards the opposite direction of infected agents in their neighborhood. We show that this mechanism considerably decreases the stationary prevalence when the spatial density of agents is low. However, for higher densities, the mechanism causes an abrupt phase transition, where a new bistable phase appears. We develop a semi-analytic approach for the case when the mobility is fast compared to the disease dynamics, and use it to argue that the bistability is caused by the emergence of spatial clusters of susceptible agents. Finally, we characterize the temporal networks formed in the fast mobility regime, showing how the degree distributions and other metrics are affected by the behavioral mechanism. Our work incorporates results previously known from adaptive networks into the population of mobile agents, which can be further developed to be used in mobility-driven models.