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Mechanisms to decrease the diseases spreading on generalized scale-free networks
Mircea Galiceanu1, Carlos F O Mendes1, Cássio M Maciel1
1Departamento de Física, Universidade Federal do Amazonas, 69077-000 Manaus, AM, Brazil.
Chaos (Woodbury, N.Y.)
|April 3, 2021
Summary
This study models disease spread on scale-free networks. Network topology and contact restrictions significantly impact disease spread, with network connectivity being the most influential factor.
Area of Science:
- Epidemiology
- Network Science
- Complex Systems
Background:
- Epidemiological models are crucial for understanding disease dynamics.
- Scale-free networks offer realistic structures for modeling social interactions.
Purpose of the Study:
- To construct an epidemiological model on a generalized scale-free network.
- To analyze the impact of network topology and spatial restrictions on disease spread.
Main Methods:
- A lattice-based epidemiological model was developed.
- Random walkers simulated infectious individuals on a scale-free network.
- Susceptible individuals' survival was analyzed using network parameters (connectivity γ, min/max degrees Kmin/Kmax).
Main Results:
- Power-law behaviors were observed in susceptible individuals' survival.
- Exponents were strongly influenced by network connectivity (γ), followed by Kmax and Kmin.
- Network topology (γ) and reduced contact numbers (low Kmax) efficiently diminished infected individuals.
Conclusions:
- Network topology and social distancing parameters significantly influence disease spread dynamics.
- Modifying network connectivity (γ) is a highly effective strategy for disease control.
- Reducing individual contact rates (Kmax) also proves efficient in mitigating infections.
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