Variability in a Community-Structured SIS Epidemiological Model

Area of Science:

• Epidemiology
• Mathematical Biology
• Computational Science

Background:

• Studying infectious disease dynamics in populations structured into communities is crucial.
• Stochastic spatial models offer insights into disease transmission patterns.
• Understanding variability in infection levels is key for public health interventions.

Purpose of the Study:

• To analyze an SIS epidemiological model in a spatially structured population.
• To compare a new ODE model with an earlier moment-based model.
• To investigate the impact of localized versus global contact on disease dynamics.

Main Methods:

• Stochastic spatial simulations were employed.
• A system of ordinary differential equations (ODEs) for moments of infectious individuals was developed.
• Results were compared against a prior infinite population size model.

Main Results:

• Localized contact had minimal effect on the equilibrium infection level.
• Localized contact significantly influenced the timing and magnitude of infection variability.
• The new ODE model incorporated population size and variability effects.

Conclusions:

• Spatial structure and contact patterns are critical for understanding disease variability, not just equilibrium.
• The developed ODE model provides a more comprehensive representation of stochastic epidemiological processes.
• Findings highlight the importance of considering contact heterogeneity in disease modeling.