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13:56
A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
Published on: July 18, 2013
Impacts of clustering on interacting epidemics
Bing Wang1, Lang Cao, Hideyuki Suzuki
1Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Tokyo 153-8505, Japan. bingbignmath@gmail.com
Journal of Theoretical Biology
|May 5, 2012
Summary
This study models two interacting diseases spreading in community networks using a random clique model. Disease spread dynamics are sensitive to network clustering, impacting epidemic prevalence.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Biology
Background:
- Community structures significantly influence epidemic spread in real-world networks.
- Understanding disease dynamics within these structures is crucial for public health.
Purpose of the Study:
- To explore the spread of two interacting diseases in networks with community structures.
- To propose and analyze a random clique network model for disease transmission.
Main Methods:
- Utilized a random clique network model with varying clique orders.
- Applied bond percolation theory, adapting it for clique-type dependent occupation probabilities.
- Investigated susceptible-infected-recovered (SIR) dynamics for two competing diseases.
Main Results:
- Derived statistical properties of the proposed network model.
- Demonstrated hypersensitivity of disease clustering effects to clique cohesiveness and concentration.
- Identified coexistence/bistability regions revealing network structure's impact on epidemic prevalence.
Conclusions:
- The random clique network model effectively captures disease spread within community structures.
- Network clustering and composition are critical determinants of epidemic behavior.
- Analysis provides insights into disease prevalence based on network topology.
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