Infection risk assessment for socially structured population using stochastic microexposure model.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Predicting infection dynamics in local microenvironments is challenging due to limitations in existing models.
• Models often fail to account for clustered social structures and the unique characteristics of microenvironments.
• Practicable models require consideration of population size, public space details, daily routines, and societal structure.

Purpose of the Study:

• To introduce a novel methodology for predicting infection outbreak dynamics.
• To investigate the impact of population social structure and local constraints on infection spread.
• To develop a more accurate risk assessment model for microenvironments.

Main Methods:

• Utilized the stochastic microexposure model (S-MEM), generalized for clustered populations.
• Applied the methodology to a simulated student community with naturally clustered social structures (classes).
• Analyzed the influence of social network characteristics (number, size, and connections of clusters) on outbreak patterns.

Main Results:

• Social structure significantly impacts infection spread, determining outbreak duration, intensity, and peak patterns.
• The contribution of different microenvironments to overall infection risk changes dynamically throughout an outbreak.
• Outbreak dynamics are highly sensitive to the specific configuration of social clusters and their interconnections.

Conclusions:

• Social structure is a primary factor in infection spread and must be integrated into risk prediction tools.
• Dynamic risk assessment is necessary, as microenvironment contributions evolve over time.
• Adaptive, time-varying infection mitigation policies are more effective than static approaches.
• The generalized S-MEM can model multi-scale social structures and predict evolving microenvironment risks.