Imperial College London


Faculty of MedicineSchool of Public Health

Honorary Lecturer



+44 (0)20 7594 0716vasa.curcin Website




320Reynolds BuildingCharing Cross Campus





Vasa Curcin is a Lecturer at Health Informatics at King's College London and a Visiting Lecturer at Department of Primary Care and Public Health, Imperial College London. His main web page is here.

He was the healthcare theme coordinator at the London e-Science Centre, while based at the Department of Computing.  He was the Scientific Project Manager of EU TRANSFoRm project, and Technical Lead of CLAHRC Northwest London initiative.

His main research is in the area of provenance-based reproducibility, with specific applications in medical informatics and Big Data analytics. His PhD was on the application of process algebras and other formal models to verification and optimization of scientific workflows. 

Other research interests include:

  • Big Data analytics using scientific workflows
  • Translational research across the whole biomedical spectrum
  • Process models for clinical guidelines

The software tools produced in Vasa's projects are being used by a wider scientific and medical community. These include the WISH tool for model-based clinical data collection and reporting, and the provenance infrastructure for heterogeneous software systems. Software is free of charge, and licensing details are available on request.



Wang W, Snell LB, Ferrari D, et al., 2022, Real-world effectiveness of steroids in severe COVID-19: a retrospective cohort study, Bmc Infectious Diseases, Vol:22

Wang W, Rudd AG, Wang Y, et al., 2022, Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study (vol 22, 195, 2022), Bmc Neurology, Vol:22

Meza-Torres B, Delanerolle G, Okusi C, et al., 2022, Differences in Clinical Presentation With Long COVID After Community and Hospital Infection and Associations With All-Cause Mortality: English Sentinel Network Database Study., Jmir Public Health Surveill, Vol:8

Mayor N, Meza-Torres B, Okusi C, et al., 2022, Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis., Jmir Public Health Surveill, Vol:8

de Jong VMT, Rousset RZ, Antonio-Villa NE, et al., 2022, Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis, Bmj-british Medical Journal, Vol:378, ISSN:0959-535X

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