Agent-based model simulation for air quality and pandemic risk assessment in architectural spaces.

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User Guide

archABM is a fast and open source agent-based modelling framework that simulates complex human-building-interaction patterns and estimates indoor air quality across an entire building, while taking into account potential airborne virus concentrations.

This documentation contains a quick-start guide (including installation procedure and basic usage of the simulator), a complete API Reference, as well as a detailed example. archABM is released under the MIT License.

Finally, if you use archABM in a scientific publication, we would appreciate citations.

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Disclaimer

archABM is an evolving research tool designed to familiarize the interested user with factors influencing the potential indoor airborne transmission of viruses (such as SARS-CoV-2) and the generation of carbon-dioxide (CO2) indoors. Calculations of virus and CO2 levels within ArchABM are based on recently published aerosol models [1,2], which however have not been validated in the context of agent-based modeling (ABM) yet. We note that uncertainty in and intrinsic variability of model parameters as well as underlying assumptions concerning model parameters may lead to errors regarding the simulated results. Use of archABM is the sole responsibility of the user. It is being made available without guarantee or warranty of any kind. The authors do not accept any liability from its use.

[1] Peng, Zhe, and Jose L. Jimenez. “Exhaled CO2 as a COVID-19 infection risk proxy for different indoor environments and activities.” Environmental Science & Technology Letters 8.5 (2021): 392-397.

[2] Lelieveld, Jos, et al. “Model calculations of aerosol transmission and infection risk of COVID-19 in indoor environments.” International journal of environmental research and public health 17.21 (2020): 8114.

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Getting Started

Use the following template to run a simulation with archABM:

# Import modules
import json
import pandas as pd
from archABM.engine import Engine

# Read configuration example
from archABM.config import config
# Alternatively, get configuration from "config.json" example

# Create ArchABM simulation engine
simulation = Engine(config)

# Run simulation
results = simulation.run()

# Create dataframes based on the results
df_people = pd.DataFrame(results["results"]["people"])
df_places = pd.DataFrame(results["results"]["places"])

This example uses the default configuration file built-in the library. Alternatively, you can download and modify the config.json file found at the data directory of archABM repository.

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Developers can also use the command-line interface with the main.py file from the source code repository.

$ python main.py config.json

To run an example, use the config.json found at the data directory of archABM repository.

Check the --help option to get more information about the optional parameters:

$ python main.py --help
Usage: main.py [OPTIONS] CONFIG_FILE

  ArchABM simulation helper

Arguments:
  CONFIG_FILE  The name of the configuration file  [required]

Options:
  -i, --interactive     Interactive CLI mode  [default: False]
  -l, --save-log        Save events logs  [default: False]
  -c, --save-config     Save configuration file  [default: True]
  -t, --save-csv        Export results to csv format  [default: True]
  -j, --save-json       Export results to json format  [default: False]
  -o, --return-output   Return results dictionary  [default: False]
  --install-completion  Install completion for the current shell.
  --show-completion     Show completion for the current shell, to copy it or
                        customize the installation.

  --help                Show this message and exit.

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Inputs

In order to run a simulation, information about the event types, people, places, and the aerosol model must be provided to the ArchABM framework.

EventsPlacesPeopleAerosol ModelOptions

Attribute	Description	Type
name	Event name	string
schedule	When an event is permitted to happen, in minutes	list of tuples
duration	Event duration lower and upper bounds, in minutes	integer,integer
number of repetitions	Number of repetitions lower and upper bounds	integer,integer
mask efficiency	Mask efficiency during an event [0-1]	float
collective	Event is invoked by one person but involves many	boolean
allow	Whether such event is allowed in the simulation	boolean

Example config.json

{
    "events": [{
            "activity": "home",
            "schedule": [
                [0, 480],
                [1020, 1440]
            ],
            "repeat_min": 0,
            "repeat_max": null,
            "duration_min": 300,
            "duration_max": 360,
            "mask_efficiency": null,
            "collective": false,
            "shared": false,
            "allow": true
        },
        {
            "activity": "work",
            "schedule": [
                [480, 1020]
            ],
            "repeat_min": 0,
            "repeat_max": null,
            "duration_min": 30,
            "duration_max": 60,
            "mask_efficiency": 0.8,
            "collective": false,
            "shared": true,
            "allow": true
        }
    ],
    "places": [{
            "name": "home",
            "activity": ["home"],
            "building": null,
            "department": null,
            "area": null,
            "height": null,
            "capacity": null,
            "ventilation": null,
            "recirculated_flow_rate": null,
            "allow": true
        },
        {
            "name": "office1",
            "activity": ["work"],
            "building": "building1",
            "department": ["department1"],
            "area": 200.0,
            "height": 3.0,
            "capacity": 50,
            "ventilation": 3,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "office2",
            "activity": ["work"],
            "building": "building1",
            "department": ["department2"],
            "area": 200.0,
            "height": 3.0,
            "capacity": 50,
            "ventilation": 3,
            "recirculated_flow_rate": 0,
            "allow": true
        }
    ],
    "people": [{
            "department": "department1",
            "building": "building1",
            "num_people": 20
        },
        {
            "department": "department2",
            "building": "building1",
            "num_people": 20
        }
    ],
    "options": {
        "movement_buildings": true,
        "movement_department": true,
        "number_runs": 1,
        "save_log": true,
        "save_config": true,
        "save_csv": false,
        "save_json": false,
        "return_output": false,
        "directory": null,
        "ratio_infected": 0.05,
        "model": "Colorado",
        "model_parameters": {
            "Colorado": {
                "pressure": 0.95,
                "temperature": 20,
                "CO2_background": 415,
                "decay_rate": 0.62,
                "deposition_rate": 0.3,
                "hepa_flow_rate": 0.0,
                "filter_efficiency": 0.20,
                "ducts_removal": 0.10,
                "other_removal": 0.00,
                "fraction_immune": 0,
                "breathing_rate": 0.52,
                "CO2_emission_person": 0.005,
                "quanta_exhalation": 25,
                "quanta_enhancement": 1,
                "people_with_masks": 1.00
            }
        }
    }
}

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Outputs

Simulation outputs are stored by default in the results directory. The subfolder with the results of an specific simulation have the date and time of the moment when it was launched as a name in %Y-%m-%d_%H-%M-%S-%f format.

By default, three files are saved after a simulation:

• config.json stores a copy of the input configuration.

• people.csv stores every person’s state along time.

• places.csv stores every places’s state along time.

people.csvplaces.csv

Attribute	Description	Type
run	Simulation run	int
time	Simulation time (minutes)	int
person	Person ID	int
status	Person status (0: susceptible, 1: infective)	bool
place	Place ID	int
event	Event ID	int
CO2_level	Average CO2 level (ppm)	float
quanta_inhaled	Quanta inhaled (quanta)	float

archABM offers the possibility of exporting the results in JSON and CSV format. To export in JSON format, use the --save-json parameter when running archABM. By default, the --save-csv parameter is set to true.

Alternatively, archABM can also be configured to yield more detailed information. The app.log file saves the log of the actions and events occurred during the simulation. To export this file, use the --save-log parameter when running archABM.

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Installation

As the compiled archABM package is hosted on the Python Package Index (PyPI) you can easily install it with pip. To install archABM, run this command in your terminal of choice:

$ pip install archABM

or, alternatively:

$ python -m pip install archABM

If you want to get archABM’s latest version, you can refer to the repository hosted at github:

python -m pip install https://github.com/Vicomtech/ArchABM/archive/main.zip

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Environment Setup

Requirements

archABM builds on simpy, tqdm, jsonschema and typer libraries.

Python 3

To find out which version of python you have, open a terminal window and try the following command:

$ python3 --version
Python 3.6.9

If you have python3 on your machine, then this command should respond with a version number. If you do not have python3 installed, follow these instructions.

Pip

pip is the reference Python package manager. It’s used to install and update packages. In case pip is not installed in your OS, follow these instructions.

Virtual Environment

venv creates a “virtual” isolated Python installation and installs packages into that virtual installation. It is always recommended to use a virtual environment while developing Python applications. To create a virtual environment, go to your project’s directory and run venv.

$ python3 -m venv env

Before you can start installing or using packages in your virtual environment you’ll need to activate it.

$ source env/bin/activate

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Source Code

archABM is developed on GitHub, where the code is always available.

You can either clone the public repository:

$ git clone git://github.com/Vicomtech/ArchABM.git

Or, download the tarball:

$ curl -OL https://github.com/Vicomtech/ArchABM/tarball/main
# optionally, zipball is also available (for Windows users).

Once you have a copy of the source, you can embed it in your own Python package, or install it into your site-packages easily:

$ cd ArchABM
$ python -m pip install .