From 7587a460472127d7e46bdeb6a0e58316594c38bb Mon Sep 17 00:00:00 2001 From: lrdossan Date: Tue, 19 Nov 2024 12:06:38 +0100 Subject: [PATCH] updated readme and removed jinja2 files --- README.md | 5 +- .../cern_caimira/apps/calculator/__init__.py | 9 +- .../cern_caimira/apps/templates/about.html.j2 | 77 ------ .../templates/base/calculator.form.html.j2 | 55 +--- .../apps/templates/base/index.html.j2 | 8 +- .../apps/templates/base/layout.html.j2 | 9 +- .../apps/templates/base/userguide.html.j2 | 253 ------------------ .../apps/templates/cern/userguide.html.j2 | 5 - .../apps/templates/userguide.html.j2 | 2 - cern_caimira/tests/test_webapp.py | 26 -- 10 files changed, 20 insertions(+), 429 deletions(-) delete mode 100644 cern_caimira/src/cern_caimira/apps/templates/about.html.j2 delete mode 100644 cern_caimira/src/cern_caimira/apps/templates/base/userguide.html.j2 delete mode 100644 cern_caimira/src/cern_caimira/apps/templates/cern/userguide.html.j2 delete mode 100644 cern_caimira/src/cern_caimira/apps/templates/userguide.html.j2 diff --git a/README.md b/README.md index a88cf95b..b8857f87 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,9 @@ Contributions are welcome on our [GitHub repository](https://github.com/CERN/CAi ## Authors & License -Developed by CERN's HSE, Beams, and IT departments, in collaboration with WHO. © Copyright 2020-2021 CERN. All rights not expressly granted are reserved. +Developed by CERN's HSE, Beams, and IT departments, in collaboration with WHO. + +© Copyright 2020-2021 CERN. All rights not expressly granted are reserved.
+Licensed under the Apache License, Version 2.0 See the full [license](caimira/LICENSE) for details. diff --git a/cern_caimira/src/cern_caimira/apps/calculator/__init__.py b/cern_caimira/src/cern_caimira/apps/calculator/__init__.py index 6afb4745..15d07bb3 100644 --- a/cern_caimira/src/cern_caimira/apps/calculator/__init__.py +++ b/cern_caimira/src/cern_caimira/apps/calculator/__init__.py @@ -135,6 +135,7 @@ def write_error(self, status_code: int, **kwargs) -> None: get_url = template.globals['get_url'], get_calculator_url = template.globals["get_calculator_url"], active_page='Error', + documentation_url = template.globals["documentation_url"], error_id=error_id, status_code=status_code, datetime=datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"), @@ -278,6 +279,7 @@ def get(self): template = template_environment.get_template( "index.html.j2") report = template.render( + documentation_url = template.globals["documentation_url"], user=self.current_user, get_url = template_environment.globals['get_url'], get_calculator_url = template_environment.globals['get_calculator_url'], @@ -458,12 +460,6 @@ def make_app( (get_root_calculator_url(r'/baseline-model/result'), StaticModel), (get_root_calculator_url(r'/api/arve/v1/(.*)/(.*)'), ArveData), # Generic Pages - (get_root_url(r'/about'), GenericExtraPage, { - 'active_page': 'about', - 'filename': 'about.html.j2'}), - (get_root_calculator_url(r'/user-guide'), GenericExtraPage, { - 'active_page': 'calculator/user-guide', - 'filename': 'userguide.html.j2'}), (get_root_url(r'/expert-app'), GenericExtraPage, { 'active_page': 'expert-app', 'filename': 'expert-app.html.j2'}), @@ -510,6 +506,7 @@ def make_app( ) template_environment.globals['get_url']=get_root_url template_environment.globals['get_calculator_url']=get_root_calculator_url + template_environment.globals['documentation_url']='https://caimira.docs.cern.ch' if debug: tornado.log.enable_pretty_logging() diff --git a/cern_caimira/src/cern_caimira/apps/templates/about.html.j2 b/cern_caimira/src/cern_caimira/apps/templates/about.html.j2 deleted file mode 100644 index c503c1bb..00000000 --- a/cern_caimira/src/cern_caimira/apps/templates/about.html.j2 +++ /dev/null @@ -1,77 +0,0 @@ -{% extends "layout.html.j2" %} - -{% block main %} - -
- -

Airborne Transmission of SARS-CoV-2


-Currently, the existing public health measures point to the importance of proper building and environmental engineering control measures, such as proper Indoor Air Quality (IAQ). -This pandemic clearly raised increased awareness on airborne transmission of respiratory viruses in indoor settings. -Out of the main modes of viral transmission, the airborne route of SARS-CoV-2 seems to have a significant importance to the spread of COVID-19 infections world-wide, hence proper guidance to building engineers or facility managers, on how to prevent on-site transmission, is essential.
-For information on the Airborne Transmission of SARS-CoV-2, feel free to check out the special issue on the Interface Focus journal from Royal Society publishing: Interface Focus: Volume 12, Issue 2 and an CERN HSE Seminar: https://cds.cern.ch/record/2743403.
-

-

What is CAiMIRA?


-CAiMIRA stands for CERN Airborne Model for Indoor Risk Assessment, previously known as CARA - COVID Airborne Risk Assessment, developed in the spring of 2020 to better understand and quantify the risk of long-range airborne spread of SARS-CoV-2 virus in workplaces. -Since then, the model has evolved and now is capable of simulating the short-range component. CAiMIRA comes with different applications that allow more or less flexibility in the input parameters: - - -The mathematical and physical model simulate the airborne spread of 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 methodology, mathematical equations and parameters of the model are published here in a peer-reviewed paper: Modelling airborne transmission of SARS-CoV-2 using CARA: risk assessment for enclosed spaces.

-

The short-range component of the model was adapted from Jia et al. (2022) Exposure and respiratory infection risk via the short-range airborne route .

- -The model used is based on scientific publications relating to airborne transmission of infectious diseases, virology, epidemiology and aerosol science. It can be used to compare the effectiveness of different airborne-related risk mitigation measures. - -The tool helps assess the potential dose of infectious airborne viruses in indoor gatherings, with people seated, standing, moving around, while breathing, speaking or shouting/singing. The model is based on the exponential dose-response of disease transmission, which assumes a fixed value for the average infectious dose. -The methodology of the model is divided into five parts: -
    -
  1. Estimating the emission rate of virions;
    2. -
  2. Estimating the removal rate of virions;
    3. -
  3. Modeling the concentration of virions within a given volume, as a function of time;
    4. -
  4. Absorbed dose of infectious viruses, inhaled during the exposure time;
    5. -
  5. Estimating the probability of a COVID-19 infection (or secondary transmission) and the expected number of new cases arising from the event
    6. -
-
-

What is the aim of CAiMIRA?


-Although the user is able to calculate the infection probability of a stand-alone event with a pre-defined set of protection measures, the main utility of CAiMIRA is to compare the relative impact of different measures and/or combination of measure. For example: - - -
-

Collaboration with the World Health Organization (WHO)


-

The tool has attracted the attention of many international organisations, including the World Health Organization (WHO) and the United Nations Office at Geneva (UNOG). -In June 2021, CERN shared its own approach towards risk assessments for occupational hazards, which was at the time called CARA, to WHO's COVID Expert Panel.

-

As a result, WHO has invited CERN to become a member of a multidisciplinary expert group of international experts called ARIA, which will work to define a standardised algorithm to quantify airborne transmission risk in indoor settings. -This will ensure that the model inculdes not only the science related to aerosol science but also the virological effects, such as host-pathogen interaction.

- -The collaboration takes place within CERNs wide-ranging engagement with other international organisations, promoting shared solutions to societal challenges. -

- -

Main code developers:


-{{ text_blocks['Main Developers'] }} -
-

Other contributions from:


-{{ text_blocks['Code Contributors'] }} -
-

References:


-{{ text_blocks['References'] }} -
-

Acknowledgements:


-
- Click to expand -
- {{ text_blocks['Acknowledgements'] }} -
- -
-
-
- -{% endblock main %} diff --git a/cern_caimira/src/cern_caimira/apps/templates/base/calculator.form.html.j2 b/cern_caimira/src/cern_caimira/apps/templates/base/calculator.form.html.j2 index 8410844c..198a0574 100644 --- a/cern_caimira/src/cern_caimira/apps/templates/base/calculator.form.html.j2 +++ b/cern_caimira/src/cern_caimira/apps/templates/base/calculator.form.html.j2 @@ -774,60 +774,15 @@

- Quick Guide:
- This tool simulates the airborne spread SARS-CoV-2 virus in a finite volume and estimates the risk of COVID-19 infection. It is based on current scientific data and can be used to compare the effectiveness of different mitigation measures.
- Virus data:
- SARS-CoV-2 covers the original "wild type" strain of the virus and three variants of concern (VOC):
- - Modify the default as necessary, according to local area prevalence e.g. for Geneva - or Ain (France).
- Ventilation data:
- - Activity types:
- The type of activity applies to both the infected and exposed persons: - - Activity breaks:
- - Refer to Calculator App user guide - for more detailed explanations on how to use this tool.
+ Quick Guide: Refer to Calculator App quick user guide. +
+ Full Guide: Refer to Calculator App full user guide. +

About

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

Documentation

-
Documentation for CAiMIRA, available here.
+
Documentation for CAiMIRA, available here.

Git

@@ -91,7 +91,7 @@ DOI
© Copyright 2020-2021 CERN. All rights not expressly granted are reserved.
Licensed under the Apache License, Version 2.0
- LICENSE + LICENSE

diff --git a/cern_caimira/src/cern_caimira/apps/templates/base/layout.html.j2 b/cern_caimira/src/cern_caimira/apps/templates/base/layout.html.j2 index 9e743f72..8415b92b 100644 --- a/cern_caimira/src/cern_caimira/apps/templates/base/layout.html.j2 +++ b/cern_caimira/src/cern_caimira/apps/templates/base/layout.html.j2 @@ -44,7 +44,7 @@ @@ -52,11 +52,11 @@
  • Calculator
    • -
  • User Guide
    • +
  • User Guide
    • {% block covid_information%} {% endblock covid_information%} -
  • About
    • +
  • About
    • {% if user.is_authenticated() %}

  • Apache 2.0 licensed open-source + CAiMIRA is Apache 2.0 licensed open-source software developed at CERN. You can find the source code at https://gitlab.cern.ch/caimira/caimira, where we welcome contributions, feature requests and issue reports.

    - diff --git a/cern_caimira/src/cern_caimira/apps/templates/base/userguide.html.j2 b/cern_caimira/src/cern_caimira/apps/templates/base/userguide.html.j2 deleted file mode 100644 index 7c773556..00000000 --- a/cern_caimira/src/cern_caimira/apps/templates/base/userguide.html.j2 +++ /dev/null @@ -1,253 +0,0 @@ -{% extends "layout.html.j2" %} - -{% block main %} - -
    -

    Instructions for use


    -

    This is a guide to help you use the calculator app. - If you are using the expert version of the tool, you should look at the expert notes.

    -

    For more information on the Airborne Transmission of SARS-CoV-2, feel free to check out the HSE Seminar: https://cds.cern.ch/record/2743403

    -

    The methodology, mathematical equations and parameters of the model are described here in the CERN Report: CERN-OPEN-2021-004

    -
    - -
    - -
    - -
    -
    {{ text_blocks['Disclaimer'] }}
    -
    - - -
    -

    How to use this tool


    -

    Simulation Name & Room number

    -
    -

    In order to be able to trace back the simulations in your workplace risk assessments, performed with the tool, you can give each one a unique name - for example "Office use on Tuesday mornings". -The simulation name has no bearing on the calculation.

    -

    A room number is included, if you do not wish to use a formal room number any reference will do - for example "57/2-004"

    -

    Virus Data


    -

    Please choose the correct virus strain or any reported Variant of Concern (VOC) from the list. -Changing this setting alters the properties of the virus which are used for the simulation. -This has a significant effect on the probability of infection. -The choices are:

    -
      -
    • SARS-CoV-2 (nominal strain), covering typical strains and variants which are not of concern from an epidemiologic point of view of the virus;
      • -
    • SARS-CoV-2 (Alpha VOC), first identified in the UK at the end of 2020 which is found to be approximately 1.5x more transmissible compared to the non-VOCs;
      • -
    • SARS-CoV-2 (Beta VOC), first identified in South Africa in May 2020 which is found to be approximately 1.25x more transmissible compared to the non-VOCs;
      • -
    • SARS-CoV-2 (Gamma VOC), first identified in Brazil in January 2021 which is found to be approximately 2.2x more transmissible compared to the non-VOCs.
      • -
    • SARS-CoV-2 (Delta VOC), first identified in India towards the end of 2020 which is found to be approximately 60% more transmissible compared to the ALPHA VOC.
      • -
    • SARS-CoV-2 (Omicron VOC), first identified in South Africa in November 2021 which is found to be at least 2.53x more transmissible compared to the DELTA VOC.
      • -
    -

    The user can modify the selected variant from the default, according to the prevalence of the different variants in the local area. Access to this information can be found here:

    - -

    N.B. The transmission data for the Gamma variant has been taken from a study data gathered in Manaus, Brazil where the variant was first observed. -The local population in Manaus had very high levels of Covid-19 antibodies (>67%) in recent months. -This factor has been taken into account by the authors of the study, via statistical adjustments to the transmission value (i.e. it has been increased, to account for spread in a population with significant acquired Covid-19 immunity). -However, this value may be revised in the future as more studies of the Gamma VOC transmission in different geographical locations become available.

    -
    -

    Vaccine effectiveness

    -
    -

    The vaccination input corresponds to the vaccine type(s) administrated to the exposed population, assuming every exposed (or the occupant in question) has received the vaccine cocktail selected by the user. -The respective vaccine effectiveness values were extracted from data available in Results of COVID-19 Vaccine Effectiveness Studies: An Ongoing Systematic Review - Updated September 8, 2022, using this script.

    -
    -

    Room Data

    -
    -

    Please enter either the room volume (in m³) or both the floor area (m²) and the room height (m). -{% block room_volume_guide %} -

    -{% endblock room_volume_guide %} -
    -

    Room heating system

    -
    -

    The use of central heating (e.g. radiators) reduces relative humidity of the indoor air, which can decrease the decay rate of viral infectivity. If your space is heated with such water radiators, select 'Yes'. If your space does not have such heating, or they are not in use in the period of the simulation (e.g. summer), select 'No'.

    -
    -

    Ventilation type

    -
    -

    There are three main options:

    -
    -

    Mechanical ventilation

    -
    -

    If the room has mechanical ventilation, suppling fresh air from outside (either a local or centralised system), you should select this option. -In order to make an accurate calculation you will need to know either the flow rate of fresh air supplied in the room or th total number of air changes per hour with fresh air.

    -

    Please bear in mind that any of the two inputs only consider the supply of fresh air. If a portion of air is recirculated, it shall not be accounted for in the inputs.

    -
    -

    Natural ventilation

    -
    -

    Natural ventilation refers to rooms which have openable windows. -There are many possibilities to calculate natural ventilation air flows, for simplification this tool assumes a single-sided natural ventilation scheme which is a conservative approach for the purpose of this tool.

    -

    Please choose the type of window (see illustration below):

    -
      -
    • Sliding or side-hung
      • -
    • Top- or bottom-hung -Window type
      • -
    -

    Please enter the number, height and width and opening distance of the windows (in m). -If there are multiple windows of different sizes, you should take an average.

    -

    The window opening distance (in m) is:

    -
      -
    • In the case of Sliding or Side-Hung option, the length the window is moved open. -Window opening distance example (image of open window and measuring tape): -Window Opening Distance
      • -
    • In case of Top- or Bottom-Hung, the distance between the fixed frame and the movable glazed part when open.
      • -
    -

    Notes: If you are unsure about the opening distance for the window, it is recommended to choose a conservative value (5 cms, 0.05m or 10cms, 0.10m). -If you open the window at different distances throughout the day, choose an average value.

    -

    When using natural ventilation, the circulation of air is simulated as a function of the difference between the temperature inside the room and the outside air temperature. The average outdoor temperature for each hour of the day has been computed for every month of the year based on historical data for Geneva, Switzerland. -It is therefore very important to enter the correct time and date in the event data section. -Finally, you must specify if the windows are open permanently (at all the times), or periodically (in intervals for a certain duration during the course of the day) - e.g. open the window for 10 minutes (duration) every 60 minutes (frequency).

    -
    -

    No ventilation

    -
    -

    This option assumes there is neither Mechanical nor Natural ventilation in the simulation.

    -
    -

    HEPA filtration

    -
    -

    A HEPA filter is a high efficiency particulate matter filter, which removes small airborne particles from the air. -They can be very useful for removing particles with viruses from the air in an enclosed space. -The calculator allows you to simulate the installation of a HEPA air filter within the room. -The recommended airflow rate for the HEPA filter should correspond to a total air exchange rate of 3 - 6 ACH (the higher the better, even beyond 6).

    -
    -

    Event Data

    -
    -

    Here we capture the information about the event being simulated. -First enter the number of occupants in the space, if you have a (small) variation in the number of people, please input the average or consider using the expert tool. -Within the number of people occupying the space, you should specify how many are infected.

    -

    In case one would like to simulate an event happening at a given time and location, where the epidemiological situation is known, the tool allows for an estimation of the probability of on-site transmission, considering the chances that a given person in the event is infected. -The user will need to select Probabilistic event, input the number of inhabitants and the the weekly (7-day rolling average) value of new reported laboratory - ⁠confirmed cases at the event location, as well as the confidence level of these inputs. -The 7-day rolling average consists in the average of the previous 3 days to subsequent 3 days, generally reported by the different public health authorities (e.g. in Switzerland here). -These two inputs need to the related, i.e. the values of reported new cases and the number of inhabitants shall correspond to the a same geographical location. For example:

    -
      -
    • Population of Geneva, CH: 508 000 inhabitants
      • -
    • New lab reported cases in the canton of Geneva: 1000 (7-day rolling avg)
      • -
    -

    The confidence level allows for an ascertainment bias to the data. The user can add the following options:

    -
      -
    • High - mandatory population wide surveillance
      • -
    • Medium - recommended population-wide surveillance
      • -
    • Low - surveillance only for symptomatic patients
      • -
    -

    Depending on the epidemiological situation in the choosen location, the public health surveillance can be more or less active. The confidence level will provide an ascertainment bias to the data collected by the user.

    -

    The higher the incidence rate (i.e. new cases / population) the higher are the chances of having at least one infected occupant participating to the event.


    -

    For general and recurrent layout simply select the Deterministic exposure option. As an example, for a shared office with 4 people, where one person is infected, we enter 4 occupants and 1 infected person.


    -
    -

    Activity type

    -
    -

    There are a few predefined activities in the tool at present.

    -
      -
    • Office = All persons seated, talking occasionally (1/3rd of the time, with normal breathing the other 2/3rds of the time). Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Small meeting = Less than 10 participants. All persons seated, having a conversation (approximately each occupant is 1/N % of the time talking, where N is the number of occupants). Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Large Meeting = 10 or more participants. Similar to a seminar with 'speakers and audience'. Infected occupant(s) is standing and speaking 1/3rd of the time, while the other occupants are seated.
      • -
    • Library = All persons seated, breathing only (not talking), all the time.
      • -
    • Call Centre = All persons seated, all talking simultaneously, all the time. This is a conservative profile, i.e. will show an increased P(i) compared to office/meeting activity. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Control Room (day shift) = All persons seated, all talking 50% of the time. This is a conservative profile, i.e. will show an increased P(i) compared to office/meeting activity. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Control Room (night shift) = All persons seated, all talking 10% of the time. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Lab = Based on a typical lab or technical working area, all persons are doing light activity and talking 50% of the time. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Workshop = Based on a mechanical assembly workshop or equipment installation scenario, all persons are doing moderate activity and talking 50% of the time. This activity is equally applicable to bicycling, or walking on a gradient, in the LHC tunnels. Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    • Conference/Training (speaker infected) = Based on a typical conference/training course scenario. -One individual (the speaker/trainer) is standing and talking, with all other individuals seated and talking quietly (whispering). -In this case it is assumed that the infected person is the speaker/trainer, because this is the worst case in terms of viral shedding.
      • -
    • Conference/Training (attendee infected) = All individuals seated and breathing. -In this case it is assumed that the infected person is not the speaker/trainer.
      • -
    • Gym = All persons are doing heavy exercise and breathing (not talking). Everyone (exposed and infected occupants) is treated the same in this model.
      • -
    -

    Timings

    -

    You should enter the time (hours:minutes) for the start and end of the simulation period (i.e. 8:30 to 17:30 for a typical office day). -It is important to enter the correct times for the simulation, in particular when using natural ventilation. -It is possible to specify a different time for the entry and exit of both the exposed and infected person, however for most cases (where we do not know apriori which of the occupants is infected), it is recommended to set these to the same values as the activity start and end.

    -

    When is the event?


    -

    This is included for completeness in all simulations, however it is of particular relevance to those using natural ventilation because of variations in outside air temperature.

    -

    Only the month is used by the model to retrieve the average outdoor air temperatures for the Geneva region.

    -
    -

    Breaks

    -
    -

    Lunch Break

    -
    -

    You have the option to specify a lunch break. -This will be useful if you plan to simulate a typical full working day. -During the lunch break it is assumed that all occupants will leave the simulated space (to go eat lunch somewhere else - restaurant or break room). -If you plan to eat lunch in the same area where you have been working, you should select 'No' even if a lunch break will be taken, since the risk of infection is related to the occupation of the simulated space. -See 'Split Breaks' if the occupants do not break at the same time.

    -

    It should also be noted that the infection probabilities presented in the report does not take into account any potential exposures during the break times.

    -
    -

    Coffee Breaks

    -
    -

    You have the option to choose 0(No breaks), 2 or 4 coffee breaks during the simulated period. -It is assumed that all occupants vacate the space during the break period. -If coffee breaks are taken in-situ, this option should be set to 'No breaks'.

    -

    When enabled, the breaks are spread equally throughout the day - for example if we simulate the period from 9:00 to 18:00, with a lunch break from 13:00 to 14:00 and considering 2 coffee breaks, the tool will schedule the first coffee break around 11:00 and the second around 16:00. -The exact timing of the breaks within the day is not particularly critical to an accurate simulation, so you do not need to be concerned about major differences if you take a coffee break at 10:00 instead of 11:00. -The variation of coffee breaks can be altered in 5 minute increments up to 30 minutes in length. -Note that this doesn't necessarily have to be a coffee break, it can represent any period where the simulated space is vacated. -See 'Split Breaks' if the occupants do not break at the same time.

    -

    It should also be noted that the infection probabilities presented in the report does not take into account any potential exposures during the break times.

    -
    -

    Split breaks

    -
    -

    You have the option to specify whether the exposed and infected person(s) break at the same time. -If not, then you can input separate breaks. This is particularly different when specifying coffee breaks as they are spread evenly throughout the activity times specified.

    -

    If we take an example where the exposed person(s) activity time is from 9:00 to 18:00 and the infected person(s) is from 10:00 to 17:00, with both having a lunch break from 13:00 to 14:00 and have 2 coffee breaks each, we can have two different results:

    -
      -

    1. Specify the default situation where both exposed and infected persons(s) have their breaks at the same time: in this case the coffee break times are calculated based on the activity time of the exposed - both will have their first coffee break around 11:00 and the second around 16:00.

      -
      2. -

    2. Specify separate breaks for the infected person(s): in this case the coffee breaks will be calculated based on the different activity times (i.e. exposed from 9:00 to 18:00 and infected from 10:00 to 17:00) - the exposed person(s) will have their first coffee break around 11:00 and the second around 16:00, whereas the infected will have their first coffee break around 11:30 and the second around 15:30.

      -
      3. -
    -
    -

    Face Masks

    -
    -

    The model allows for a simulation with either a continuous wearing of face masks throughout the duration of the event, or have the removed at all times - i.e. all occupants (infected and exposed alike) wear or not masks for the duration of the simulation. -Please bear in mind the user inputs shall be aligned with the current applicable public health & safety instructions. -Please check what are the applicable rules, before deciding which assumptions are used for the simulation.

    -

    If you have selected the Conference/Training activity type, this equates to the speaker/trainer and all participants either wearing masks throughout the conference/training (Yes), or removing them when seated/standing at their socially distanced positions within the conference/training room (No). -Please confirm what are the applicable rules, before deciding which assumptions are used for the simulation

    -

    For the time being only the Type 1 surgical and FFP2 masks can be selected.

    -
    -

    Generate Report

    -
    -

    When you have entered all the necessary information, please click on the Generate Report button to execute the model. With the implementation of Monte Carlo simulations, the browser might take a few seconds to react.

    -

    Report


    -

    The report will open in your web browser. -It contains a summary of all the input data, which will allow the simulation to be repeated if required in the future as we improve the model.

    -
    -

    Results

    -
    -

    This part of the report shows the P(I) or probability of one exposed person getting infected. -It is estimated based on the emission rate of virus into the simulated volume, and the amount which is inhaled by exposed individuals. -This probability is valid for the simulation duration - i.e. the start and end time. -If you are using the natural ventilation option, the simulation is only valid for the selected month, because the following or preceding month will have a different average temperature profile. -The expected number of new cases for the simulation is calculated based on the probability of infection, multiplied by the number of exposed occupants.

    -

    The graph shows the variation in the concentration of virions within the simulated volume. -It is determined by:

    -
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    • The presence of the infected person, who emits airborne viruses in the volume.
      • -
    • The emission rate is related to the type of activity of the infected person (sitting, light exercise), their level of vocalisation (breathing, speaking or shouting).
      • -
    • The accumulation of virions in the volume, which is driven, among other factors, by ventilation (if applicable).
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      • In a mechanical ventilation scenario, the removal rate is constant, based on fresh airflow supply in and out of the simulated space.
        • -
      • Under natural ventilation conditions, the effectiveness of ventilation relies upon the hourly temperature difference between the inside and outside air temperature.
        • -
      • A HEPA filter removes virions from the air at a constant rate and is modelled in the same way as mechanical ventilation, however air passed through a HEPA filter is recycled (i.e. it is not fresh air).
        • -
      -
      • -
    -
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    QR code

    -
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    At the end of the report you can find a unique QR code / hyperlink for this report. This provides an automatic way to review the calculator form with the corresponding specified parameters. -This allows for:

    -
      -
    • sharing reports by either scanning or clicking on the QR code to obtain a shareable link.
      • -
    • easily regenerating reports with any new versions of the CAiMIRA model released in the future.
      • -
    -
    -

    Conclusion

    -
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    This tool provides informative comparisons for COVID-19 airborne risk only - see Disclaimer. -If you have any comments on your experience with the app, or feedback for potential improvements, please share them with the development team - send email.

    -
    - -{% endblock main %} diff --git a/cern_caimira/src/cern_caimira/apps/templates/cern/userguide.html.j2 b/cern_caimira/src/cern_caimira/apps/templates/cern/userguide.html.j2 deleted file mode 100644 index 28ab09db..00000000 --- a/cern_caimira/src/cern_caimira/apps/templates/cern/userguide.html.j2 +++ /dev/null @@ -1,5 +0,0 @@ -{% extends "base/userguide.html.j2" %} - -{% block room_volume_guide %} -This information is available via GIS Portal (https://gis.cern.ch/gisportal/).

    -{% endblock room_volume_guide %} \ No newline at end of file diff --git a/cern_caimira/src/cern_caimira/apps/templates/userguide.html.j2 b/cern_caimira/src/cern_caimira/apps/templates/userguide.html.j2 deleted file mode 100644 index 6b34bc4d..00000000 --- a/cern_caimira/src/cern_caimira/apps/templates/userguide.html.j2 +++ /dev/null @@ -1,2 +0,0 @@ -{# The main calculator report, this template is intended to be implemented by themes #} -{% extends "base/userguide.html.j2" %} \ No newline at end of file diff --git a/cern_caimira/tests/test_webapp.py b/cern_caimira/tests/test_webapp.py index 86205682..36b21c8b 100644 --- a/cern_caimira/tests/test_webapp.py +++ b/cern_caimira/tests/test_webapp.py @@ -134,29 +134,3 @@ def test_500(self): response = self.fetch('/') assert response.code == 500 assert 'Unfortunately an error occurred when processing your request' in response.body.decode() - - -class TestCERNGenericPage(tornado.testing.AsyncHTTPTestCase): - def get_app(self): - cern_theme = Path(cern_caimira.apps.calculator.__file__).parent.parent / 'themes' / 'cern' - app = cern_caimira.apps.calculator.make_app(theme_dir=cern_theme) - pages = [ - (r'/calculator/user-guide', cern_caimira.apps.calculator.GenericExtraPage, {'active_page': 'calculator/user-guide', 'filename': 'userguide.html.j2'}), - (r'/about', cern_caimira.apps.calculator.GenericExtraPage, {'active_page': 'about', 'filename': 'about.html.j2'}), - ] - - return tornado.web.Application(pages, **app.settings) - - @tornado.testing.gen_test(timeout=_TIMEOUT) - def test_user_guide(self): - response = yield self.http_client.fetch(self.get_url('/calculator/user-guide')) - self.assertEqual(response.code, 200) - - @tornado.testing.gen_test(timeout=_TIMEOUT) - def test_about(self): - response = yield self.http_client.fetch(self.get_url('/about')) - self.assertEqual(response.code, 200) - - def test_calculator_404(self): - response = self.fetch('/calculator') - assert response.code == 404