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Synergistic epidemic spreading in correlated networks
Shogo Mizutaka1, Kizashi Mori2, Takehisa Hasegawa2
1Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 924-1292, Japan.
Physical Review. E
|October 21, 2022
Summary
Degree correlation impacts disease spread in synergistic SIS models. Positive correlation lowers epidemic thresholds, while negative correlation raises them, with transitions often independent of correlation strength.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Biology
Background:
- Understanding disease dynamics on complex networks is crucial.
- Degree correlation, a network property, influences epidemic spread.
- Synergistic effects in transmission can alter disease dynamics.
Purpose of the Study:
- To investigate the impact of degree correlation on a susceptible-infected-susceptible (SIS) model with synergistic effects.
- To analyze the epidemic threshold and transition behavior in relation to network structure.
- To validate theoretical models with simulations.
Main Methods:
- Mean-field treatment of a synergistic SIS model on a bimodal network.
- Analysis of tunable degree correlation.
- Development and application of approximate master equations.
- Monte Carlo simulations for validation.
Main Results:
- A discontinuous transition in epidemic spread was identified, largely independent of degree correlation strength.
- Positive degree correlation reduces the epidemic threshold, while negative correlation increases it.
- Strong positive degree correlation predicts two discontinuous jumps in infected density.
- Approximate master equations quantitatively agree with mean-field predictions and simulations.
Conclusions:
- Degree correlation significantly modulates epidemic thresholds in synergistic SIS models.
- The mean-field approach, supported by master equations and simulations, accurately captures these dynamics.
- Network structure plays a critical role in the transmission patterns of synergistic infectious diseases.
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