CAiMIRA is a risk assessment tool developed to model the concentration of viruses in enclosed spaces, in order to inform space-management decisions. It does this by simulating the airborne spread SARS-CoV-2 virus in a finite volume, assuming homogenous mixing for the long-range component and a two-stage jet model for short-range, and estimates the risk of COVID-19 airborne transmission therein. Please see the About page for more details on the methodology, assumptions and limitations of CAiMIRA.

The full CAiMIRA source code can be accessed freely under an Apache 2.0 open source license from our code repository. It includes detailed instructions on how to run your own version of this tool.


CAiMIRA is composed of two applications, the Calculator and the Expert App.


About page for details on methodology, assumptions and limitations of CAiMIRA.


Documentation for CAiMIRA, available here.


Official CAiMIRA GitLab repository.

Reference & Citation

For use of the CAiMIRA model:

  • Henriques A, Mounet N, Aleixo L, Elson P, Devine J, Azzopardi G, Andreini M, Rognlien M, Tarocco N, Tang J. (2022). Modelling airborne transmission of SARS-CoV-2 using CARA: risk assessment for enclosed spaces. Interface Focus 12: 20210076.
  • Download citation
    Short-range expiratory jet model from:
    • Jia W, Wei J, Cheng P, Wang Q, Li Y. (2022). Exposure and respiratory infection risk via the short-range airborne route. Building and Environment 219: 109166.
For use of the CAiMIRA web app:
  • CAiMIRA – CERN Airborne Model for Indoor Risk Assessment tool
  • DOI
    © Copyright 2020-2021 CERN. All rights not expressly granted are reserved.
    Licensed under the Apache License, Version 2.0


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We wish to thank CERN’s HSE Unit, Beams Department, Experimental Physics Department, Information Technology Department, Industry, Procurement and Knowledge Transfer Department and International Relations Sector for their support to the study. Thanks to Doris Forkel-Wirth, Benoit Delille, Walid Fadel, Olga Beltramello, Letizia Di Giulio, Evelyne Dho, Wayne Salter, Benoit Salvant and colleagues from the COVID working group for providing expert advice and extensively testing the model. Finally, we wish to thank Fabienne Landua and the design service for preparing the illustrations and Alessandro Raimondo and Manuela Cirilli from the Knowledge Transfer Group for their continuous support. Our compliments towards the work and research performed by world leading scientists in this domain: Dr. Julian Tang, Prof. Manuel Gameiro, Dr. Linsey Marr, Prof. Lidia Morawska, Prof. Yuguo Li, and others – their scientific contribution was indispensable for this project.