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

Overview

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

PyPI Status PyPI Version License Actions Top Language Github Issues

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.


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.


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

Getting Started

Use the following template to run a simulation with archABM:

from archABM.engine import Engine
import json
import pandas as pd

# Read config data from JSON
def read_json(file_path):
    with open(str(file_path)) as json_file:
        result = json.load(json_file)
    return result

config_data = read_json("config.json")
# WARNING - for further processing ->
# config_data["options"]["return_output"] = True

# Create ArchABM simulation engine
simulation = Engine(config_data)

# 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"])

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.

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.

Events
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
Places
Attribute Description Type
name Place name string
activity Activity or event occurring at that place string
department Department name string
building Building name string
area Room floor area in square meters float
height Room height in meters. float
capacity Room people capacity. integer
height Room height in meters. float
ventilation Passive ventilation in hours-1 float
recirculated_flow_rate Active ventilation in cubic meters per hour float
allow Whether such place is allowed in the simulation boolean
People
Attribute Description Type
department Department name string
building Building name string
num_people Number of people integer
Aerosol Model
Attribute Description Type
pressure Ambient pressure in atm float
temperature Ambient temperature in Celsius degrees float
CO2_background Background CO2 concentration in ppm float
decay_rate Decay rate of virus in hours-1 float
deposition_rate Deposition to surfaces in hours-1 float
hepa_flow_rate Hepa filter flow rate in cubic meters per hour float
filter_efficiency Air conditioning filter efficiency float
ducts_removal Air ducts removal loss float
other_removal Extraordinary air removal float
fraction_immune Fraction of people immune to the virus float
breathing_rate Mean breathing flow rate in cubic meters per hour float
CO2_emission_person CO2 emission rate at 273K and 1atm float
quanta_exhalation Quanta exhalation rate in quanta per hour float
quanta_enhancement Quanta enhancement due to variants float
people_with_masks Fraction of people using mask float
Options
Attribute Description Type
movement_buildings Allow people enter to other buildings boolean
movement_department Allow people enter to other departments boolean
number_runs Number of simulations runs to execute integer
save_log Save events logs boolean
save_config Save configuration file boolean
save_csv Export the results to csv format boolean
save_json Export the results to json format boolean
return_output Return a dictionary with the results boolean
directory Directory name to save results string
ratio_infected Ratio of infected to total number of people float
model Aerosol model to be used in the simulation string

Example config.json

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": 0.0,
            "collective": false,
            "shared": true,
            "allow": true
        },
        {
            "activity": "meeting",
            "schedule": [
                [540, 960]
            ],
            "repeat_min": 0,
            "repeat_max": 5,
            "duration_min": 20,
            "duration_max": 90,
            "mask_efficiency": 0.0,
            "collective": true,
            "shared": true,
            "allow": true
        },
        {
            "activity": "lunch",
            "schedule": [
                [780, 900]
            ],
            "repeat_min": 1,
            "repeat_max": 1,
            "duration_min": 20,
            "duration_max": 45,
            "mask_efficiency": 0.0,
            "collective": true,
            "shared": true,
            "allow": true
        },
        {
            "activity": "coffee",
            "schedule": [
                [600, 660],
                [900, 960]
            ],
            "repeat_min": 0,
            "repeat_max": 2,
            "duration_min": 5,
            "duration_max": 15,
            "mask_efficiency": 0.0,
            "collective": true,
            "shared": true,
            "allow": true
        },
        {
            "activity": "restroom",
            "schedule": [
                [480, 1020]
            ],
            "repeat_min": 0,
            "repeat_max": 4,
            "duration_min": 3,
            "duration_max": 6,
            "mask_efficiency": 0.0,
            "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": "open_office",
            "activity": "work",
            "building": "building1",
            "department": ["department1", "department2", "department3", "department4"],
            "area": 330.0,
            "height": 2.7,
            "capacity": 60,
            "ventilation": 1.5,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "it_office",
            "activity": "work",
            "building": "building1",
            "department": ["department4"],
            "area": 52.0,
            "height": 2.7,
            "capacity": 10,
            "ventilation": 1.5,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "chief_office_A",
            "activity": "work",
            "building": "building1",
            "department": ["department5", "department6", "department7"],
            "area": 21.0,
            "height": 2.7,
            "capacity": 5,
            "ventilation": 1.5,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "chief_office_B",
            "activity": "work",
            "building": "building1",
            "department": ["department5", "department6", "department7"],
            "area": 21.0,
            "height": 2.7,
            "capacity": 5,
            "ventilation": 1.5,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "chief_office_C",
            "activity": "work",
            "building": "building1",
            "department": ["department5", "department6", "department7"],
            "area": 24.0,
            "height": 2.7,
            "capacity": 5,
            "ventilation": 1.5,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "meeting_A",
            "activity": "meeting",
            "building": "building1",
            "department": ["department1", "department2", "department3", "department5", "department6", "department7"],
            "area": 16.0,
            "height": 2.7,
            "capacity": 6,
            "ventilation": 1.0,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "meeting_B",
            "activity": "meeting",
            "building": "building1",
            "department": ["department1", "department2", "department3", "department5", "department6", "department7"],
            "area": 16.0,
            "height": 2.7,
            "capacity": 6,
            "ventilation": 1.0,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "meeting_C",
            "activity": "meeting",
            "building": "building1",
            "department": ["department1", "department2", "department3", "department5", "department6", "department7"],
            "area": 11.0,
            "height": 2.7,
            "capacity": 4,
            "ventilation": 1.0,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "meeting_D",
            "activity": "meeting",
            "building": "building1",
            "department": null,
            "area": 66.0,
            "height": 2.7,
            "capacity": 24,
            "ventilation": 1.5,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "coffee_A",
            "activity": "coffee",
            "building": "building1",
            "department": null,
            "area": 25.0,
            "height": 2.7,
            "capacity": 10,
            "ventilation": 1.5,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "coffee_B",
            "activity": "coffee",
            "building": "building1",
            "department": null,
            "area": 55.0,
            "height": 2.7,
            "capacity": 20,
            "ventilation": 1.5,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "restroom_A",
            "activity": "restroom",
            "building": "building1",
            "department": null,
            "area": 20.0,
            "height": 2.7,
            "capacity": 4,
            "ventilation": 1.0,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "restroom_B",
            "activity": "restroom",
            "building": "building1",
            "department": ["department1", "department2", "department3", "department4", "department5", "department6"],
            "area": 20.0,
            "height": 2.7,
            "capacity": 4,
            "ventilation": 1.0,
            "recirculated_flow_rate": 0,
            "allow": true
        },
        {
            "name": "lunch",
            "activity": "lunch",
            "building": "building1",
            "department": null,
            "area": 150.0,
            "height": 2.7,
            "capacity": 60,
            "ventilation": 1.5,
            "recirculated_flow_rate": 0,
            "allow": true
        }
    ],
    "people": [{
            "department": "department1",
            "building": "building1",
            "num_people": 16
        },
        {
            "department": "department2",
            "building": "building1",
            "num_people": 16
        },
        {
            "department": "department3",
            "building": "building1",
            "num_people": 16
        },
        {
            "department": "department4",
            "building": "building1",
            "num_people": 7
        },
        {
            "department": "department5",
            "building": "building1",
            "num_people": 2
        },
        {
            "department": "department6",
            "building": "building1",
            "num_people": 2
        },
        {
            "department": "department7",
            "building": "building1",
            "num_people": 1
        }
    ],
    "options": {
        "movement_buildings": true,
        "movement_department": false,
        "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,
                "recirculated_flow_rate": 300,
                "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
            }
        }
    }
}

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.

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.


Citing archABM

If you use ArchABM in your work or project, please cite the following article, published in Building and Environment (DOI...): [Full REF]

@article{
}
Owner
Vicomtech
Applied Research in Visual Computing & Interaction and Artificial Inteligence - Official Github Account - Member of Basque Research & Technology Alliance, BRTA
Vicomtech
[ECCV'20] Convolutional Occupancy Networks

Convolutional Occupancy Networks Paper | Supplementary | Video | Teaser Video | Project Page | Blog Post This repository contains the implementation o

622 Dec 30, 2022
A neuroanatomy-based augmented reality experience powered by computer vision. Features 3D visuals of the Atlas Brain Map slices.

Brain Augmented Reality (AR) A neuroanatomy-based augmented reality experience powered by computer vision that features 3D visuals of the Atlas Brain

Yasmeen Brain 10 Oct 06, 2022
Oriented Object Detection: Oriented RepPoints + Swin Transformer/ReResNet

Oriented RepPoints for Aerial Object Detection The code for the implementation of “Oriented RepPoints + Swin Transformer/ReResNet”. Introduction Based

96 Dec 13, 2022
FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS

FaceAPI AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using

Vladimir Mandic 395 Dec 29, 2022
Conditional Generative Adversarial Networks (CGAN) for Mobility Data Fusion

This code implements the paper, Kim et al. (2021). Imputing Qualitative Attributes for Trip Chains Extracted from Smart Card Data Using a Conditional Generative Adversarial Network. Transportation Re

Eui-Jin Kim 2 Feb 03, 2022
Azion the best solution of Edge Computing in the world.

Azion Edge Function docker action Create or update an Edge Functions on Azion Edge Nodes. The domain name is the key for decision to a create or updat

8 Jul 16, 2022
Generating Videos with Scene Dynamics

Generating Videos with Scene Dynamics This repository contains an implementation of Generating Videos with Scene Dynamics by Carl Vondrick, Hamed Pirs

Carl Vondrick 706 Jan 04, 2023
A foreign language learning aid using a neural network to predict probability of translating foreign words

Langy Langy is a reading-focused foreign language learning aid orientated towards young children. Reading is an activity that every child knows. It is

Shona Lowden 6 Nov 17, 2021
IMBENS: class-imbalanced ensemble learning in Python.

IMBENS: class-imbalanced ensemble learning in Python. Links: [Documentation] [Gallery] [PyPI] [Changelog] [Source] [Download] [知乎/Zhihu] [中文README] [a

Zhining Liu 176 Jan 04, 2023
Machine-in-the-Loop Rewriting for Creative Image Captioning

Machine-in-the-Loop Rewriting for Creative Image Captioning Data Annotated sources of data used in the paper: Data Source URL Mohammed et al. Link Gor

Vishakh P 6 Jul 24, 2022
商品推荐系统

商品top50推荐系统 问题建模 本项目的数据集给出了15万左右的用户以及12万左右的商品, 以及对应的经过脱敏处理的用户特征和经过预处理的商品特征,旨在为用户推荐50个其可能购买的商品。 推荐系统架构方案 本项目采用传统的召回+排序的方案。

107 Dec 29, 2022
UMT is a unified and flexible framework which can handle different input modality combinations, and output video moment retrieval and/or highlight detection results.

Unified Multi-modal Transformers This repository maintains the official implementation of the paper UMT: Unified Multi-modal Transformers for Joint Vi

Applied Research Center (ARC), Tencent PCG 84 Jan 04, 2023
Personal project about genus-0 meshes, spherical harmonics and a cow

How to transform a cow into spherical harmonics ? Spot the cow, from Keenan Crane's blog Context In the field of Deep Learning, training on images or

3 Aug 22, 2022
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Razvan Valentin Marinescu 51 Nov 23, 2022
Training Cifar-10 Classifier Using VGG16

opevcvdl-hw3 This project uses pytorch and Qt to achieve the requirements. Version Python 3.6 opencv-contrib-python 3.4.2.17 Matplotlib 3.1.1 pyqt5 5.

Kenny Cheng 3 Aug 17, 2022
MiniSom is a minimalistic implementation of the Self Organizing Maps

MiniSom Self Organizing Maps MiniSom is a minimalistic and Numpy based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial N

Giuseppe Vettigli 1.2k Jan 03, 2023
Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Sidd Karamcheti 50 Nov 16, 2022
Boostcamp CV Serving For Python

Boostcamp-CV-Serving Prerequisites MySQL GCP Cloud Storage GCP key file Sentry Streamlit Cloud Secrets: .streamlit/secrets.toml #DO NOT SHARE THIS I

Jungwon Seo 19 Feb 22, 2022
Active Offline Policy Selection With Python

Active Offline Policy Selection This is supporting example code for NeurIPS 2021 paper Active Offline Policy Selection by Ksenia Konyushkova*, Yutian

DeepMind 27 Oct 15, 2022
[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints Official implementation for Reducing Footskate in Human Motion Recon

Virginia Tech Vision and Learning Lab 38 Nov 01, 2022