An agent based model for simulating the spread of sexually transmitted infections

Area of Science:

• Computational epidemiology
• Mathematical biology
• Agent-based modeling

Background:

• Compartmental models often oversimplify disease dynamics by not accounting for individual agent behaviors.
• Agent-based models (ABMs) are increasingly used for infectious disease simulation, but their application to sexually transmitted infections (STIs) requires further investigation.
• Existing ABMs are commonly applied to respiratory infections, necessitating adaptation for simulating STI transmission.

Purpose of the Study:

• To preliminarily investigate the suitability of agent-based modeling (ABM) for simulating sexually transmitted infection (STI) spread.
• To contrast the capabilities of ABM with traditional compartmentalized mathematical models in the context of STI transmission.
• To explore the impact of various STI mitigation strategies using a novel ABM framework.

Main Methods:

• Developed an agent-based model in C++ simulating 1000 agents over 10 years.
• Incorporated 16 agent parameters influencing infection probabilities and behaviors.
• Utilized Boost libraries for normal distribution and OpenGL for visualization.

Main Results:

• Simulation results offer qualitative comparisons of STI mitigation strategies, including condom use, promiscuity, social network structure, and mandatory testing.
• Demonstrated that population-level behavior changes have a more dramatic impact on individual STI risk than individual behavior changes.
• Explored both individual and population-wide impacts of simulated interventions.

Conclusions:

• Agent-based modeling is a suitable methodology for simulating STI spread and evaluating mitigation strategies.
• Population-level interventions and social dynamics play a critical role in controlling STI epidemics.
• ABM provides a valuable tool for understanding complex interactions in STI transmission that are often missed by simpler models.