The QBio group in the Department of Computer Science at the University of Turin, with support from the FLAME GPU team, developed Forge4Flame, a user-friendly interface that makes it easier to harness the full power of FLAME GPU. Specifically, the tool streamlines the design, execution, and analysis of models by automatically generating the necessary FLAME GPU code and incorporating valuable visualisation and post-processing features. This makes the tool more accessible to a broader audience of researchers and public health professionals.
By running large-scale epidemiological models of infectious disease spread within buildings on GPUs, FLAME GPU enables researchers to capture complex interactions in detail and deliver results dramatically faster than traditional CPU-based approaches. This performance opens up new opportunities for exploring dynamic behaviours in epidemiology and beyond. In the COVID-19 case study, FLAME GPU made it possible to model disease spread in an Italian middle school at unprecedented detail and speed, allowing rapid testing of interventions and vaccination strategies. While Forge4FLAME helped to streamline access, the true impact came from FLAME GPU’s ability to combine scale, speed, and accuracy. This brought advanced agent-based modelling within the reach of public health researchers, demonstrating its potential to influence decision-making in real-world crises.
Testimonial
The Quantitative Biology group of the Department of Computer Science from the University of Turin (Italy) collaborated closely with the FLAME GPU developers in the creation of the F4F tool, a graphical interface designed to simplify the modelling process and make agent-based modelling more accessible to a wider audience of researchers and public health professionals. The collaboration was highly synergistic, characterized by virtual meetings, open and constructive discussions. The FLAME GPU team’s expertise, responsiveness, and commitment were instrumental in guiding the technical development and ensuring seamless integration with the FLAME GPU framework. This collaboration not only advanced our project but also led to the joint preparation of a scientific paper. - Simone Pernice, University of Turin (Italy)
More Information
- Baccega, Daniele, et al. “Forge4Flame: An Intuitive Dashboard for Designing GPU Agent-Based Models to Simulate Infectious Disease Spread.” Available at SSRN 5194584. https://doi.org/10.1016/j.simpat.2025.103205* Research Paper