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Simulating HIV transmission dynamics: An agent-based approach using NetLogo
Sophia Nicolette C Amasa1, Trisha Mae P Beleta1, Shemaiah L Montilla1
1Department of Computer Science, College of Computer Studies, MSU-Iligan Institute of Technology, Iligan City, Philippines.
Plos One
|September 11, 2025
Summary
An improved Agent-Based Model (ABM) for HIV transmission dynamics shows that consistent condom use, high testing frequency, and treatment adherence significantly reduce infection rates. The model accurately reflects real-world HIV trends.
Area of Science:
- Epidemiology
- Computational modeling
- Public health
Background:
- Agent-Based Modeling (ABM) is widely used for HIV transmission dynamics.
- Existing ABM studies often lack comprehensive parameterization and subpopulation diversity.
- This limits their ability to capture complex inter-population relationships in HIV epidemics.
Purpose of the Study:
- To develop an enhanced Agent-Based Model (ABM) for simulating HIV epidemic dynamics.
- To explore a wider range of parameters including sexual behaviors, drug use, and treatment adherence.
- To improve the accuracy and scope of ABM in understanding HIV transmission.
Main Methods:
- An improved ABM was developed incorporating diverse parameters.
- The model was calibrated using empirical HIV data from the Philippines (2010-2018).
- Simulation accuracy was validated using Mean Absolute Error (MAE) and Mean Squared Error (MSE).
Main Results:
- The simulation closely matched national HIV infection trends (MAE=3.5, MSE=14.9).
- Key factors reducing HIV transmission include longer commitment duration, consistent condom use, sexual inactivity, frequent testing, and treatment adherence.
- The model predicted cyclical trends in new infections (every 2-3 years) and an overall decline.
Conclusions:
- The enhanced ABM provides a reliable tool for simulating HIV dynamics.
- Findings highlight the significant impact of behavioral and treatment factors on HIV transmission rates.
- This comprehensive model offers insights for targeted public health interventions and future research.
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