Researchers have developed a new artificial intelligence-powered modeling tool capable of accurately predicting how infectious diseases spread in confined environments and identifying more effective strategies for containment. The system, called the AI-GIS Infection Dynamics (AGID) model, combines machine learning with geographic information systems to simulate disease transmission in settings such as hospitals, schools, prisons, and public transportation hubs. Unlike traditional epidemiological models that rely on broad assumptions, AGID uses real-time data on human movement, room layouts, ventilation patterns, and contact frequencies to generate highly detailed simulations. This allows the model to forecast infection hotspots with greater precision and adapt quickly to changing conditions. The researchers tested AGID in simulated scenarios involving tuberculosis, influenza, and SARS-CoV-2 in enclosed spaces. In each case, the model outperformed conventional approaches in predicting outbreak trajectories and evaluating the impact of interventions such as mask mandates, improved ventilation, and targeted quarantines. One of the key strengths of AGID is its ability to incorporate spatial and behavioral data at a granular level, enabling public health officials to design location-specific containment plans. For example, the model can identify high-risk zones within a hospital ward or pinpoint the most effective points for deploying rapid testing and isolation protocols. The team behind the project says the tool could be particularly valuable during emerging outbreaks, where quick, data-driven decisions are critical. It also has potential applications in urban planning, facility design, and emergency preparedness. The researchers emphasize that AGID is not intended to replace human expertise but to support decision-makers with actionable insights. They are currently working on integrating the model into public health dashboards and exploring partnerships with government agencies and health organizations to facilitate real-world deployment.