Speakers
Description
Sexually transmitted diseases (STDs) and other communicable diseases remain a major global public health challenge due to complex transmission dynamics, heterogeneous contact patterns, and strong social, behavioral, and biological interactions. Mathematical and computational modeling is essential for understanding STD dynamics and evaluating prevention, screening, and intervention strategies.
Advances in surveillance and information technologies have enabled large-scale data collection across health, pathogen, social, environmental, and behavioral domains. Strategic use of these data is critical for disease prevention, health promotion, and reducing disparities. Precision Public Health (PPH) addresses these challenges by applying artificial intelligence and advanced mathematical and statistical methods to assess disease dynamics, exposures, behaviors, and population-level susceptibilities, while promoting evidence-informed policies and targeted, equitable interventions.
This mini-symposium focuses on computational and data-driven modeling approaches for STDs and other communicable diseases, emphasizing public health relevance and AI integration. Contributions include mechanistic transmission models, network and agent-based models, stochastic and multiscale approaches, as well as machine learning and hybrid AI–mechanistic models informed by epidemiological and surveillance data.
