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
A Runtime method overload decorator which should behave like a compiled language

strongtyping-pyoverload A Runtime method overload decorator which should behave like a compiled language there is a override decorator from typing whi

20 Oct 31, 2022
[BMVC'21] Official PyTorch Implementation of Grounded Situation Recognition with Transformers

Grounded Situation Recognition with Transformers Paper | Model Checkpoint This is the official PyTorch implementation of Grounded Situation Recognitio

Junhyeong Cho 18 Jul 19, 2022
Official codes: Self-Supervised Learning by Estimating Twin Class Distribution

TWIST: Self-Supervised Learning by Estimating Twin Class Distributions Codes and pretrained models for TWIST: @article{wang2021self, title={Self-Sup

Bytedance Inc. 85 Dec 15, 2022
Like ThreeJS but for Python and based on wgpu

pygfx A render engine, inspired by ThreeJS, but for Python and targeting Vulkan/Metal/DX12 (via wgpu). Introduction This is a Python render engine bui

139 Jan 07, 2023
GeneralOCR is open source Optical Character Recognition based on PyTorch.

Introduction GeneralOCR is open source Optical Character Recognition based on PyTorch. It makes a fidelity and useful tool to implement SOTA models on

57 Dec 29, 2022
Stream images from a connected camera over MQTT, view using Streamlit, record to file and sqlite

mqtt-camera-streamer Summary: Publish frames from a connected camera or MJPEG/RTSP stream to an MQTT topic, and view the feed in a browser on another

Robin Cole 183 Dec 16, 2022
Project ArXiv Citation Network

Project ArXiv Citation Network Overview This project involved the analysis of the ArXiv citation network. Usage The complete code of this project is i

Dennis Núñez-Fernández 5 Oct 20, 2022
A Pytorch Implementation for Compact Bilinear Pooling.

CompactBilinearPooling-Pytorch A Pytorch Implementation for Compact Bilinear Pooling. Adapted from tensorflow_compact_bilinear_pooling Prerequisites I

169 Dec 23, 2022
Implementation of CVPR'21: RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction

RfD-Net [Project Page] [Paper] [Video] RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction Yinyu Nie, Ji Hou, Xiaoguang Han, Matthi

Yinyu Nie 162 Jan 06, 2023
Source code to accompany Defunctland's video "FASTPASS: A Complicated Legacy"

Shapeland Simulator Source code to accompany Defunctland's video "FASTPASS: A Complicated Legacy" Download the video at https://www.youtube.com/watch?

TouringPlans.com 70 Dec 14, 2022
SymPy-powered, Wolfram|Alpha-like answer engine totally in your browser, without backend computation

SymPy Beta SymPy Beta is a fork of SymPy Gamma. The purpose of this project is to run a SymPy-powered, Wolfram|Alpha-like answer engine totally in you

Liumeo 25 Dec 21, 2022
Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).

Deep Text Search - AI Based Text Search & Recommendation System Deep Text Search is an AI-powered multilingual text search and recommendation engine w

19 Sep 29, 2022
Tutorial page of the Climate Hack, the greatest hackathon ever

Tutorial page of the Climate Hack, the greatest hackathon ever

UCL Artificial Intelligence Society 12 Jul 02, 2022
A CROSS-MODAL FUSION NETWORK BASED ON SELF-ATTENTION AND RESIDUAL STRUCTURE FOR MULTIMODAL EMOTION RECOGNITION

CFN-SR A CROSS-MODAL FUSION NETWORK BASED ON SELF-ATTENTION AND RESIDUAL STRUCTURE FOR MULTIMODAL EMOTION RECOGNITION The audio-video based multimodal

skeleton 15 Sep 26, 2022
Official code for UnICORNN (ICML 2021)

UnICORNN (Undamped Independent Controlled Oscillatory RNN) [ICML 2021] This repository contains the implementation to reproduce the numerical experime

Konstantin Rusch 21 Dec 22, 2022
UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation

UNION Automatic Evaluation Metric described in the paper UNION: An UNreferenced MetrIc for Evaluating Open-eNded Story Generation (EMNLP 2020). Please

50 Dec 30, 2022
Transformers based fully on MLPs

Awesome MLP-based Transformers papers An up-to-date list of Transformers based fully on MLPs without attention! Why this repo? After transformers and

Fawaz Sammani 35 Dec 30, 2022
CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches This document describes how to install and use CRISCE (CRItical

Chair of Software Engineering II, Uni Passau 2 Feb 09, 2022
Pytorch-Swin-Unet-V2 - a modified version of Swin Unet based on Swin Transfomer V2

Swin Unet V2 Swin Unet V2 is a modified version of Swin Unet arxiv based on Swin

Chenxu Peng 26 Dec 03, 2022
Research code for Arxiv paper "Camera Motion Agnostic 3D Human Pose Estimation"

GMR(Camera Motion Agnostic 3D Human Pose Estimation) This repo provides the source code of our arXiv paper: Seong Hyun Kim, Sunwon Jeong, Sungbum Park

Seong Hyun Kim 1 Feb 07, 2022