Simulating SIR processes on networks using weighted shortest paths

Area of Science:

• Computational epidemiology
• Network science
• Mathematical modeling

Background:

• Simulating Susceptible-Infected-Recovered (SIR) dynamics on complex networks is crucial for understanding epidemic spread.
• Existing methods may face challenges in efficiently handling both Markovian and non-Markovian processes across diverse network structures.

Purpose of the Study:

• To introduce a novel computational framework for simulating SIR processes on networks.
• To leverage weighted shortest paths to represent propagation dynamics for enhanced accuracy and efficiency.
• To apply the framework for source detection and optimizing vaccination strategies.

Main Methods:

• Mapping SIR dynamics to edge weights representing propagation time between nodes.
• Constructing an ensemble of network realizations using Markov Chain Monte Carlo (MCMC) or direct sampling.
• Utilizing weighted shortest path calculations for efficient simulation and analysis.

Main Results:

• The framework successfully simulates SIR dynamics on both Markovian and non-Markovian processes.
• Application to three empirical networks revealed insights into expected propagation times.
• Demonstrated efficiency in source detection and improved time-critical vaccination strategies.

Conclusions:

• The weighted shortest path framework offers a versatile and efficient approach to simulating epidemic dynamics on networks.
• This methodology provides a valuable tool for epidemiological research, public health interventions, and network analysis.