What happened
Researchers built a framework where computer simulations of disease spread can detect when their core assumptions are breaking down and automatically adjust their logic to match reality. Most simulations lock in one view of how a disease spreads at the start and never question it, but this one stops, checks its own work, and rewires itself if needed.
Why it matters
In real epidemiology, nobody agrees on how diseases actually spread. For antimicrobial resistance in hospitals, contact patterns matter, but so do environmental persistence and selection pressure, and experts disagree on which dominates. Until now, you had to pick one theory, run the simulation, hope it was right, and ignore what you got wrong. This framework watches the simulation's own predictions fail and automatically explores competing theories to see which one actually matches what's happening. That matters because it means disease models can adapt to local realities instead of enforcing a global guess.