Identification of effective spreaders in contact networks using dynamical influence
Ruaridh A Clark1, Malcolm Macdonald1
1Department of Electronic and Electrical Engineering, University of Strathclyde, George Street, Glasgow, UK.
Applied Network Science
|January 25, 2021
Summary
This study enhances disease spread prediction by incorporating contact time into network analysis. It reveals how network structure influences spreader effectiveness, optimizing disease control strategies in human and ant populations.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Biology
Background:
- Contact networks are crucial for understanding disease transmission dynamics.
- Network structure significantly influences information and disease spread.
- Current metrics for identifying effective spreaders often overlook network structure.
Purpose of the Study:
- To improve eigenvector-based spreader selection by integrating non-linear transmission probabilities.
- To elucidate the relationship between network structure and disease spread dynamics.
- To define effective spreaders based on optimizing infection rates or time.
Main Methods:
- Modified the Laplacian matrix to incorporate contact time and transmission probability.
- Introduced a non-linear relationship between contact duration and disease transmission probability.
- Analyzed simulated and real-world human and ant contact networks.
Main Results:
- Demonstrated that network structure is influential in disease spread, contrary to degree-based metrics.
- Showcased how altered eigenvector centrality highlights influential spreaders.
- Identified a trichotomy in effective spreader definitions: optimizing initial, average, or time to full infection.
Conclusions:
- Eigenvector-based methods, when adapted for non-linear transmission, offer superior spreader identification.
- Network structure plays a vital role in disease spread dynamics.
- Findings can inform targeted interventions for disease control and pathogen spread reduction in various contact networks.
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