Sep 18 2025
A groundbreaking study by researchers from the Mindanao State University–Iligan Institute of Technology (91¾«Æ·ºÚÁϳԹÏ-IIT) has been published in PLOS ONE, offering fresh insights into the alarming rise of HIV/AIDS cases through the use of advanced computer simulations.
The paper, titled “Simulating HIV Transmission Dynamics: An Agent-Based Approach Using NetLogo,” by
Sophia Nicolette C. Amasa, Trisha Mae P. Beleta, Shemaiah L. Montilla, and Prof. Orven E. Llanto introduce an enhanced agent-based model (ABM) that captures the complex interplay of human behavior, drug use, condom use, testing frequency, and treatment adherence in the spread of HIV. Unlike traditional models that focus narrowly on specific subgroups, this simulation integrates multiple factors and subpopulations, producing results that closely match national HIV infection data in the Philippines between 2010 and 2018.
Rising Urgency Amid Alarming Trends
According to the HIV and AIDS Registry of the Philippines, over 109,000 confirmed cases were recorded as of December 2022, with men who have sex with men (MSM) comprising 82% of new infections. Globally, UNAIDS reports nearly 39 million people living with HIV in 2022, with 70% of cases concentrated in vulnerable groups. The Philippine epidemic is one of the fastest-growing in the Asia-Pacific region, making this research both urgent and highly relevant.
Key Findings
This model demonstrated that extended relationship commitments, consistent condom use, regular HIV testing, and strict adherence to treatment significantly reduce transmission rates. It also highlighted cyclical patterns of infection, with new cases rising and falling every two to three years, while showing an overall long-term decline when preventive measures are in place.
Notably, the study identified heightened risks among people who inject drugs (PWID), underscoring the critical role of needle-sharing in fueling infections. The model’s capacity to simulate such dynamics provides policymakers with a powerful tool for designing targeted interventions.
A Call for Evidence-based Interventions
Prof. Orven E. Llantos emphasized that the model is not only a tool for prediction but also a framework for designing more effective public health strategies. “Our findings show that HIV transmission is not driven by a single factor but by a complex web of behaviors and interventions. Understanding these interactions allows us to design better prevention programs that respond to the realities of diverse communities,” he noted.
The authors stress that while the simulation offers robust insights for short-term projections, it also reveals gaps that require urgent policy attention—particularly in increasing testing coverage, promoting consistent condom use, and addressing the vulnerabilities of high-risk groups.
Global and Local Impact
The publication comes at a critical time as governments and health organizations intensify their fight against HIV/AIDS amid resurging infection rates worldwide. By combining behavioral science, public health data, and computational modeling, the 91¾«Æ·ºÚÁϳԹÏ-IIT study positions the Philippines at the forefront of using artificial intelligence-inspired tools to combat one of the world’s most pressing health challenges.
The full paper and supporting datasets are openly available through PLOS ONE and GitHub, ensuring transparency and accessibility for researchers, policymakers, and public health practitioners worldwide.
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GitHub: