Explore the research and knowledge base driving our innovations.
Ashleigh Myall joins Diana Samuel to discuss a new machine-learning framework that integrates dynamic patient-contact networks with patient clinical variables and contextual hospital variables to predict hospital-onset COVID-19 infections.
As clinically validated and published in The Lancet Digital Health, our infection acquisition models achieve 0.89 AUC-ROC in predicting COVID-19 in hospitals.
Combined genomics + network modelling identified a real-world outbreak at Imperial College Healthcare NHS Trust.
Tested in Switzerland—our underlying models remained highly accurate even with limited local data to predict viral infections.
NEX now supports >3,000 organisms through intelligent processing algorithms.
17 new users have been onboarded during 2025 so far—actively shaping the future of infection prevention.
Expanded clinical evaluation has demonstrated our ability to detect and accurately predict CRE, MRSA, VRE, and ESBL acquisition in hospitals.
Our system identifies healthcare-associated infections with 99.98% specificity from microbiology data, and device data from patient electronic health records.
Featured in a fireside chat with the Healthcare Infection Society on the future of infection prediction.
Read the team blog to get up to speed with the latest innovations.
Large language models are poised to become valuable allies in infection prevention and control (IPC). The jury will be out for some time but the evidence to date is already hinting at an enticing, brave new world...
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