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Modeling epidemic in metapopulation networks with heterogeneous diffusion rates
Mao Xing Liu1, Jie Zhang1, Zheng Guang Li1
1Department of Mathematics, North University of China Taiyuan, Shanxi 030051, P. R. China.
Mathematical Biosciences and Engineering : MBE
|November 9, 2019
Summary
This study models infectious disease spread in cities using metapopulation networks. Higher traffic flow increases disease spread, while traffic heterogeneity raises the outbreak threshold.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Modeling
Background:
- Infectious disease transmission dynamics in urban settings are complex.
- Metapopulation network models are crucial for understanding large-scale epidemic spread.
- Heterogeneity in population movement significantly impacts disease diffusion.
Purpose of the Study:
- To establish an epidemic model in metapopulation networks considering heterogeneous diffusion rates.
- To analyze factors influencing disease diffusion rates and epidemic thresholds.
- To investigate the relationship between traffic flow, network connectivity, and epidemic dynamics.
Main Methods:
- Development of a metapopulation epidemic model incorporating heterogeneous diffusion rates.
- Analysis of disease-free and endemic equilibria for stability.
- Derivation of the epidemic threshold based on model parameters.
- Numerical simulations to validate theoretical findings.
Main Results:
- Disease diffusion rate is positively correlated with traffic development in cities.
- Increased traffic flow leads to a higher number of infected individuals.
- Greater heterogeneity in traffic flow increases the epidemic outbreak threshold.
- Connectivity of cities influences the overall spread dynamics.
Conclusions:
- Traffic development is a critical factor in accelerating infectious disease spread.
- Traffic flow heterogeneity acts as a protective factor against widespread outbreaks.
- Metapopulation network models provide valuable insights into urban epidemic control strategies.
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