Pattern dynamics of networked epidemic model with higher-order infections
Jiaojiao Guo1, Xing Li1, Runzi He1
1School of Mathematics, North University of China, Taiyuan 030051, Shanxi, China.
Chaos (Woodbury, N.Y.)
|October 23, 2024
Summary
Networked reaction-diffusion systems can form patterns due to higher-order interactions within nodes. This study reveals how contact heterogeneity influences these patterns and Turing instability, aiding epidemic control.
Area of Science:
- Mathematical Biology
- Network Science
- Complex Systems
Background:
- Current research on reaction-diffusion (RD) systems often neglects contact heterogeneity within nodes.
- Higher-order interactions in reaction terms are crucial for understanding complex pattern formation.
Purpose of the Study:
- To develop a networked RD model incorporating higher-order interactions in simplicial complexes.
- To investigate the impact of contact heterogeneity on Turing instability and pattern formation.
Main Methods:
- Theoretical analysis of the networked RD model.
- Numerical simulations to explore pattern dynamics.
- Analysis of the relationship between network structure and pattern characteristics.
Main Results:
- Higher-order interactions can induce Turing instability in networked RD systems.
- The Turing instability range is quadratically related to the average 2-simplices degree.
- Pattern amplitude shows varied trends with increasing average 2-simplices degree, while infection density consistently rises.
Conclusions:
- Contact heterogeneity within nodes significantly influences pattern formation in networked RD systems.
- Findings provide insights into epidemic dynamics and inform prevention strategies.
- The study highlights the importance of considering complex network structures in RD models.
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