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Updated: Jun 5, 2026

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
Published on: October 29, 2016
Spreading of infection in a two species reaction-diffusion process in networks
Paschalis Korosoglou1, Aristotelis Kittas, Panos Argyrakis
1Department of Physics, University of Thessaloniki, 54124 Thessaloniki, Greece. pkoro@grid.auth.gr
Abstract:
We study the dynamics of the infection of a two mobile species reaction from a single infected agent in a population of healthy agents. Historically, the main focus for infection propagation has been through spreading phenomena, where a random location of the system is initially infected and then propagates by successfully infecting its neighbor sites. Here both the infected and healthy agents are mobile, performing classical random walks. This may be a more realistic picture to such epidemiological models, such as the spread of a virus in communication networks of routers, where data travel in packets, the communication time of stations in ad hoc mobile networks, information spreading (such as rumor spreading) in social networks, etc. We monitor the density of healthy particles ρ(t), which we find in all cases to be an exponential function in the long-time limit in two-dimensional and three-dimensional lattices and Erdős-Rényi (ER) and scale-free (SF) networks. We also investigate the scaling of the crossover time t(c) from short- to long-time exponential behavior, which we find to be a power law in lattices and ER networks. This crossover is shown to be absent in SF networks, where we reveal the role of the connectivity of the network in the infection process. We compare this behavior to ER networks and lattices and highlight the significance of various connectivity patterns, as well as the important differences of this process in the various underlying geometries, revealing a more complex behavior of ρ(t).
Insights
Mobile agents and infection spread were studied. Healthy agent density follows exponential decay, with crossover times varying across network types, highlighting connectivity
Area of Science:
- Epidemiology
- Network Science
- Statistical Physics
Background:
- Traditional infection models focus on static sites.
- Mobile agents offer a more realistic approach to disease and information spread.
Purpose of the Study:
- Investigate infection dynamics with mobile healthy and infected agents.
- Analyze healthy agent density and crossover time across different network structures.
Main Methods:
- Simulated infection spread via random walks of mobile agents.
- Monitored healthy agent density (ρ(t)) over time.
- Analyzed crossover time (t(c)) in various network topologies (lattices, ER, SF networks).
Main Results:
- Healthy agent density exhibits exponential decay in the long-time limit across all studied networks.
- Crossover time scales as a power law in lattices and Erdős-Rényi (ER) networks.
- Scale-free (SF) networks lack a distinct crossover time, emphasizing network connectivity's role.
Conclusions:
- Mobile agent dynamics significantly alter infection spread compared to static models.
- Network topology, particularly connectivity in SF networks, critically influences infection dynamics and crossover behavior.
- Findings provide insights into real-world phenomena like virus propagation in mobile networks and rumor spreading.
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