diff --git a/cara/apps/calculator/__init__.py b/cara/apps/calculator/__init__.py
index b9572896..e396ea8c 100644
--- a/cara/apps/calculator/__init__.py
+++ b/cara/apps/calculator/__init__.py
@@ -149,12 +149,12 @@ class StaticModel(BaseRequestHandler):
class LandingPage(BaseRequestHandler):
def get(self):
template_environment = self.settings["template_environment"]
- template = self.settings["template_environment"].get_template(
+ template = template_environment.get_template(
"index.html.j2")
report = template.render(
user=self.current_user,
calculator_prefix=self.settings["calculator_prefix"],
- text_blocks=template_environment.globals['common_text']
+ text_blocks=template_environment.globals['common_text'],
)
self.finish(report)
@@ -174,13 +174,15 @@ class AboutPage(BaseRequestHandler):
class CalculatorForm(BaseRequestHandler):
def get(self):
- template = self.settings["template_environment"].get_template(
+ template_environment = self.settings["template_environment"]
+ template = template_environment.get_template(
"calculator.form.html.j2")
report = template.render(
user=self.current_user,
xsrf_form_html=self.xsrf_form_html(),
calculator_prefix=self.settings["calculator_prefix"],
calculator_version=__version__,
+ text_blocks=template_environment.globals['common_text'],
)
self.finish(report)
@@ -199,11 +201,13 @@ class CompressedCalculatorFormInputs(BaseRequestHandler):
class ReadmeHandler(BaseRequestHandler):
def get(self):
- template = self.settings['template_environment'].get_template("userguide.html.j2")
+ template_environment = self.settings["template_environment"]
+ template = template_environment.get_template("userguide.html.j2")
readme = template.render(
active_page="calculator/user-guide",
user=self.current_user,
calculator_prefix=self.settings["calculator_prefix"],
+ text_blocks=template_environment.globals['common_text'],
)
self.finish(readme)
diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py
index e78aba80..f2926b0b 100644
--- a/cara/apps/calculator/report_generator.py
+++ b/cara/apps/calculator/report_generator.py
@@ -12,6 +12,7 @@ import jinja2
import numpy as np
from cara import models
+from cara.apps.calculator import markdown_tools
from ... import monte_carlo as mc
from .model_generator import FormData, _DEFAULT_MC_SAMPLE_SIZE
from ... import dataclass_utils
@@ -354,6 +355,9 @@ class ReportGenerator:
loader=self.jinja_loader,
undefined=jinja2.StrictUndefined,
)
+ env.globals['common_text'] = markdown_tools.extract_rendered_markdown_blocks(
+ env.get_template('common_text.md.j2')
+ )
env.filters['non_zero_percentage'] = non_zero_percentage
env.filters['readable_minutes'] = readable_minutes
env.filters['minutes_to_time'] = minutes_to_time
@@ -364,4 +368,4 @@ class ReportGenerator:
def render(self, context: dict) -> str:
template = self._template_environment().get_template("calculator.report.html.j2")
- return template.render(**context)
+ return template.render(**context, text_blocks=template.globals['common_text'])
diff --git a/cara/apps/templates/base/calculator.form.html.j2 b/cara/apps/templates/base/calculator.form.html.j2
index de9716eb..92d0d42f 100644
--- a/cara/apps/templates/base/calculator.form.html.j2
+++ b/cara/apps/templates/base/calculator.form.html.j2
@@ -614,40 +614,7 @@
-
-
- CARA is a risk assessment tool developed to model the concentration of viruses in enclosed spaces, in order to inform space-management decisions.
-
-
- CARA models the concentration profile of virions in enclosed spaces with clear and intuitive graphs.
- 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 airborne spread SARS-CoV-2 virus in a finite volume, assuming a homogenous mixture and a two-stage exhaled jet model, and estimates the risk of COVID-19 airborne transmission therein.
- The results DO NOT include 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 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 2022.
- 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.
-
-
- 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.
-
+
{{ text_blocks['Disclaimer'] }}
{% block disclaimer %}
-
Disclaimer:
-
-
- CARA is a risk assessment tool developed to model the concentration of viruses in enclosed spaces, in order to inform space-management decisions.
-
-
- CARA models the concentration profile of potential infectious viruses in enclosed spaces with clear and intuitive graphs.
- 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 airborne spread SARS-CoV-2 virus in a finite volume, assuming a homogenous mixture and a two-stage exhaled jet model, and estimates the risk of COVID-19 airborne transmission therein.
- The results DO NOT include 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 2022.
- 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.
-
-
- 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.
-
-
+
Disclaimer:
+ {{ text_blocks['Disclaimer'] }}
{% endblock disclaimer %}
-
-
- CARA is a risk assessment tool developed to model the concentration of viruses in enclosed spaces, in order to inform space-management decisions.
-
-
- CARA models the concentration profile of potential virions in enclosed spaces with clear and intuitive graphs.
- 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 airborne spread SARS-CoV-2 virus in a finite volume, assuming a homogenous mixture and a two-stage exhaled jet model, and estimates the risk of COVID-19 airborne transmission therein.
- The results DO NOT include 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 2022.
- 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.
-
-
- 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.
-
-
+
{{ text_blocks['Disclaimer'] }}
Events with a P(i) between 2% and 10% shall be subject to ALARA principles (see footnote) to minimise the risk before proceeding.
Events with a P(i) exceeding 10% or a number of expected new cases that exceeds 1 may not take place until additional measures are in place and a risk reduction has been performed.
-
+
diff --git a/cara/apps/templates/common_text.md.j2 b/cara/apps/templates/common_text.md.j2
index 5eebb32d..795c07b5 100644
--- a/cara/apps/templates/common_text.md.j2
+++ b/cara/apps/templates/common_text.md.j2
@@ -22,6 +22,41 @@
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.
+## Disclaimer
+
+ CARA is a risk assessment tool developed to model the concentration of viruses in enclosed spaces, in order to inform space-management decisions.
+
+
+ CARA models the concentration profile of virions in enclosed spaces with clear and intuitive graphs.
+ 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 airborne transmission 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.
+
+
+ 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.
+
+
## References
Reference list can be found in the CARA paper: CERN-OPEN-2021-004
