The user can set a number of parameters, including room volume, exposure time, activity type, mask-wearing and ventilation.
The report generated indicates how to avoid exceeding critical concentrations and chains of airborne transmission in spaces such as individual offices, meeting rooms and labs.
The risk assessment tool simulates the long-range airborne spread SARS-CoV-2 virus in a finite volume, assuming a homogenous mixture, and estimates the risk of COVID-19 infection therein.
The results DO NOT include short-range airborne exposure (where the physical distance is a significant factor) nor the other known modes of SARS-CoV-2 transmission.
Hence, the output from this model is only valid when the other recommended public health & safety instructions are observed, such as adequate physical distancing, good hand hygiene and other barrier measures.
The model used is based on scientific publications relating to airborne transmission of infectious diseases, dose-response exposures and aerosol science, as of February 2021.
It can be used to compare the effectiveness of different airborne-related risk mitigation measures.
Note that this model applies a deterministic approach, i.e., it is assumed at least one person is infected and shedding viruses into the simulated volume.
Nonetheless, it is also important to understand that the absolute risk of infection is uncertain, as it will depend on the probability that someone infected attends the event.
The model is most useful for comparing the impact and effectiveness of different mitigation measures such as ventilation, filtration, exposure time, physical activity and
the size of the room, only considering long-range airborne transmission of COVID-19 in indoor settings.
This tool is designed to be informative, allowing the user to adapt different settings and model the relative impact on the estimated infection probabilities.
The objective is to facilitate targeted decision-making and investment through comparisons, rather than a singular determination of absolute risk.
While the SARS-CoV-2 virus is in circulation among the population, the notion of 'zero risk' or 'completely safe scenario' does not exist.
Each event modelled is unique, and the results generated therein are only as accurate as the inputs and assumptions.
Andre Henriques<sup>1</sup>, Luis Aleixo<sup>1</sup>, Marco Andreini<sup>1</sup>, Gabriella Azzopardi<sup>2</sup>, James Devine<sup>3</sup>, Philip Elson<sup>4</sup>, Nicolas Mounet<sup>2</sup>, Markus Kongstein Rognlien<sup>2,6</sup>, Nicola Tarocco<sup>5</sup>
Henriques A, Mounet N, Aleixo L, Elson P, Devine J, Azzopardi G, Andreini M, Rognlien M, Tarocco N, Tang J. (2021). Modelling airborne transmission of SARS-CoV-2 using CARA: risk assessment for enclosed spaces. _Interface Focus 20210076_. https://doi.org/10.1098/rsfs.2021.0076
CARA has not undergone review, approval or certification by competent authorities, and as a result, it cannot be considered as a fully endorsed and reliable tool, namely in the assessment of potential viral emissions from infected hosts to be modelled.
The software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and non-infringement.
In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.
The https://cern.ch/cara application is running on CERN's OpenShift platform. In order to set it up for the first time, we followed the documentation at https://cern.service-now.com/service-portal?id=kb_article&n=KB0004498. In particular we:
* Added the OpenShift application deploy key to the GitLab repository
* Created a Python 3.6 (the highest possible at the time of writing) application in OpenShift
* Configured a generic webhook on OpenShift, and call that from the CI of the GitLab repository
Add the token to GitLab to allow GitLab to access OpenShift and define/change image stream tags. Go to `Settings` -> `CI / CD` -> `Variables` -> click on `Expand` button and create the variable `OPENSHIFT_TEST_DEPLOY_TOKEN`: insert the token `<...test-token...>`.
Create and store the secret. Copy the secret above and add it to the GitLab project under `CI /CD` -> `Variables` with the name `OPENSHIFT_TEST_WEBHOOK_SECRET`.
To get this new user's authentication token go to ``User Management`` -> ``Service Accounts`` -> `gitlab-config-checker` and locate the token in the newly created secret associated with the user (in this case ``gitlab-config-checker-token-XXXX``). Copy the `token` value from `Data`.
Create the various configurations:
```console
$ cd app-config/openshift
$ oc process -f configmap.yaml | oc create -f -
$ oc process -f services.yaml | oc create -f -
$ oc process -f imagestreams.yaml | oc create -f -