Individual-based approach to epidemic processes on arbitrary dynamic contact networks.

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• Epidemic dynamics and contact network changes often occur simultaneously.
• Ignoring these comparable time scales can lead to incomplete understanding of disease spread.

Purpose of the Study:

• To develop an individual-based approximation for the susceptible-infected-recovered (SIR) epidemic model on dynamic networks.
• To provide a computationally efficient framework for analyzing infection dynamics at the individual level.

Main Methods:

• Developed an individual-based approximation for the SIR model.
• Applied the framework to arbitrary dynamic contact networks.
• Validated the approximation against direct numerical simulations.

Main Results:

• The framework accurately approximates epidemic simulation results.
• It captures temporal heterogeneities and correlations in contact sequences.
• The approximation successfully identifies the index individual in real-life network data.

Conclusions:

• The developed approximation offers a computationally efficient and accurate method for studying epidemics on dynamic networks.
• Understanding both network and epidemic time scales is crucial for accurate disease modeling.
• This approach enhances the analysis of outbreak timing, size, and secondary infections.