OUTBreak AI-driven Detection: Enhancing MALDI-TOF outbreak detection with multimodal AI integrating epidemiological and genomic datacore
OUTBRAID · Horizon Europe grant · 2026-07-01–2028-06-30
EC contribution
Total cost
Beneficiaries
About the data
Source: CORDIS (official EU open data), Horizon Europe. Framework HORIZON · call HORIZON-MSCA-2025-PF · scheme HORIZON-TMA-MSCA-PF-EF · topic HORIZON-MSCA-2025-PF-01-01. CORDIS record →
Objective
Hospital-acquired infections (HAIs) from resistant and hypervirulent bacteria—such as Methicillin-resistant Staphylococcus aureus, Vancomycin-resistant Enterococcus faecium, Carbapenemase-producing Pseudomonas aeruginosa, Carbapenemase-producing Acinetobacter baumannii, and Clostridioides difficile—pose a growing threat to patient safety worldwide. Outbreaks often go undetected until severe harm occurs. While whole genome sequencing (WGS) is the gold standard for outbreak tracing, it is costly, slow and not globally accessible. MALDI-TOF mass spectrometry, in contrast, is rapid and widely used, but its outbreak detection potential remains unexploited.The OUTBReak AI-driven Detection (OUTBRAID) project will transform hospital outbreak surveillance by leveraging an unprecedented international dataset—over 1.2 million MALDI-TOF spectra already collected at the IMM Zurich, plus new contributions from 9 countries across Europe, Asia, and Australia, including Low and Median Income Countries (LMICs), each providing at least 100 samples per pathogen for rigorous, external validation.OUTBRAID will develop two breakthrough approaches: (1) diffusion models to generate synthetic MALDI-TOF spectra, ensuring robust, cross-center performance; and (2) multimodal machine learning that combines MALDI-TOF, epidemiological metadata, and WGS to enable automated, high-resolution cluster detection. These models will be benchmarked not only at species level but also at sequence type (ST), cgMLST, and single-nucleotide polymorphism (SNP) resolution—pushing MALDI-TOF as close as possible to the granularity of WGS.All data, models, and code will be open access. OUTBRAID’s participation in workshops and training will reach and train 150+ scientists and clinicians (e.g., ESCMID), ensuring broad impact. By braiding together international data streams and AI, OUTBRAID aims to deliver scalable tools for early outbreak detection, supporting hospitals and public health globally.
Beneficiaries (2)
| Organisation | Country | Role | EC contribution | SME |
|---|---|---|---|---|
| UNIVERSITAT ZURICH | CH | coordinator | €307,959 | |
| MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV | DE | associatedPartner | — |
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