A digital twin for hospital antimicrobial resistance forecasting and constrained intervention optimisation

Hospital antimicrobial resistance (AMR) emanates from an array of complex interactions between patient turnover, heterogeneous patient--staff contact patterns, antibiotic-driven within-host selection, and imperfect surveillance. We present a hospital AMR digital twin that combines mechanistic simulation with temporal graph learning to forecast resistance emergence from evolving daily contact networks and enable support intervention planning. Our approach is twofold: graph neural networks and transformers to model predictions and mathematical programming optimization to provide decision support. The main predictive task asks whether future spread of resistant infections is more likely to be driven by endogenous hospital transmission and selection or from importation on admission. We evaluated this task under both fully observed and partially observed settings, using baseline benchmarks together with ablations, surveillance perturbations, and distribution-shift stress tests. Under canonical conditions, the model achieved very strong predictive performance, especially when ground-truth system states were available, and remained informative under partial observation. Ablations showed that contact-weight information was relatively robust, whereas compressed node-feature representations weakened performance more noticeably when observations were incomplete. Surveillance stress tests further showed that delayed or less frequent reporting can be tolerated in some settings, but threshold calibration becomes fragile under more severe observation changes. Across broader epidemiological and surveillance shifts, the ground-truth model generally preserved strong ranking ability, while partial-observation performance was less stable. When models were trained directly in the shifted regime, performance improved compared with zero-shot transfer, indicating that the digital twin can adapt to new and previously unseen operating conditions but that portability across regimes can improve, particularly when only partial surveillance data are available. We also evaluated intervention-conditioned forecasting by branching hospital states into a small library of screening and isolation policies under a shock-and-superspreader regime. The learned models supported useful within-state action ranking and frequently identified policies that improved on the baseline containment protocols or avoided worsening outcomes. The same digital twin can also support constrained intervention selection, although reliable deployment will require careful calibration, improved robustness under partial observation, and broader policy libraries.