Usability and microbial validation study of an autonomous UV-C disinfection robot.

Authors

Thomas Roberto de Jager, Tijn Olivier Delzenne, Dennis Claessen

Published in

Infection prevention in practice. Volume 8. Issue 4. Pages 100582. Epub Aug 22, 2026.

Abstract

Hospital-acquired infections (HAIs) remain a major challenge in healthcare environments, leading to significant patient morbidity, mortality and increased healthcare costs. Automated ultraviolet C (UV-C) disinfection has been recognized as a potentially effective method to reduce microbial contamination on hard, non-porous, high-touch surfaces, minimizing the errors present in manual disinfection efforts.
To evaluate the microbiological effectiveness of an autonomous moving UV-C disinfection robot (Omnia UV-C v1.0, Ryberg B.V., Delft, The Netherlands) through microbiological validation and usability assessment.
The efficacy of disinfection was assessed by collecting surface swab samples from selected high-touch locations before and after robot disinfection, analysing microbial log reductions of indicator bacteria (Escherichia coli, Bacillus subtilis and Pseudomonas putida). Disinfection doses were measured with analogue dosimeters, and the robot's disinfection reports were analysed to correlate UV-C dose with microbial reduction. Static and dynamic disinfection runs were performed to evaluate the added value of automated movement during disinfection.
The robot achieved microbial log reductions exceeding 4 logs (99.99%) at various locations, meeting established disinfection criteria. Disinfection reports, dosimeter data, and microbial assays showed a strong correlation with dynamic disinfection, providing coverage advantages over static approaches. The disinfection doses measured were consistent with effective microbial decontamination.
The autonomous UV-C disinfection robot demonstrated effective microbial decontamination, highlighting its potential to aid in infection control in healthcare settings. Future work will include broader testing for various pathogens and shadowed areas to optimize disinfection coverage.

PMID:
42831187
Bibliographic data and abstract were imported from PubMed on 05 Oct 2026.

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