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
Repository for XLM-T, a framework for evaluating multilingual language models on Twitter data

This is the XLM-T repository, which includes data, code and pre-trained multilingual language models for Twitter. XLM-T - A Multilingual Language Mode

Cardiff NLP 112 Dec 27, 2022
TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

Microsoft 1.3k Dec 30, 2022
With this package, you can generate mixed-integer linear programming (MIP) models of trained artificial neural networks (ANNs) using the rectified linear unit (ReLU) activation function

With this package, you can generate mixed-integer linear programming (MIP) models of trained artificial neural networks (ANNs) using the rectified linear unit (ReLU) activation function. At the momen

ChemEngAI 40 Dec 27, 2022
Project page for the paper Semi-Supervised Raw-to-Raw Mapping 2021.

Project page for the paper Semi-Supervised Raw-to-Raw Mapping 2021.

Mahmoud Afifi 22 Nov 08, 2022
A style-based Quantum Generative Adversarial Network

Style-qGAN A style based Quantum Generative Adversarial Network (style-qGAN) model for Monte Carlo event generation. Tutorial We have prepared a noteb

9 Nov 24, 2022
MAGMA - a GPT-style multimodal model that can understand any combination of images and language

MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning Authors repo (alphabetical) Constantin (CoEich), Mayukh (Mayukh

Aleph Alpha GmbH 331 Jan 03, 2023
Image data augmentation scheduler for albumentations transforms

albu_scheduler Scheduler for albumentations transforms based on PyTorch schedulers interface Usage TransformMultiStepScheduler import albumentations a

19 Aug 04, 2021
Safe Model-Based Reinforcement Learning using Robust Control Barrier Functions

README Repository containing the code for the paper "Safe Model-Based Reinforcement Learning using Robust Control Barrier Functions". Specifically, an

Yousef Emam 13 Nov 24, 2022
The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.

FMFCC-A This project is the description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts. The FMFCC-A dataset is shared through BaiduCl

18 Dec 24, 2022
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018

PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place

Mikaela Uy 294 Dec 12, 2022
Official implementation of Protected Attribute Suppression System, ICCV 2021

Official implementation of Protected Attribute Suppression System, ICCV 2021

Prithviraj Dhar 6 Jan 01, 2023
Implementation of Pix2Seq in PyTorch

pix2seq-pytorch Implementation of Pix2Seq paper Different from the paper image input size 1280 bin size 1280 LambdaLR scheduler used instead of Linear

Tony Shin 9 Dec 15, 2022
A simple interface for editing natural photos with generative neural networks.

Neural Photo Editor A simple interface for editing natural photos with generative neural networks. This repository contains code for the paper "Neural

Andy Brock 2.1k Dec 29, 2022
Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Dominik Klein 189 Dec 21, 2022
Pytorch implementation of the paper SPICE: Semantic Pseudo-labeling for Image Clustering

SPICE: Semantic Pseudo-labeling for Image Clustering By Chuang Niu and Ge Wang This is a Pytorch implementation of the paper. (In updating) SOTA on 5

Chuang Niu 154 Dec 15, 2022
Rax is a Learning-to-Rank library written in JAX

🦖 Rax: Composable Learning to Rank using JAX Rax is a Learning-to-Rank library written in JAX. Rax provides off-the-shelf implementations of ranking

Google 247 Dec 27, 2022
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning

Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning This is the code for implementing the MADDPG algorithm presented in

97 Dec 21, 2022
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers

Official TensorFlow implementation of the unsupervised reconstruction model using zero-Shot Learned Adversarial TransformERs (SLATER). (https://arxiv.

ICON Lab 22 Dec 22, 2022
A Haskell kernel for IPython.

IHaskell You can now try IHaskell directly in your browser at CoCalc or mybinder.org. Alternatively, watch a talk and demo showing off IHaskell featur

Andrew Gibiansky 2.4k Dec 29, 2022
The Illinois repository for Climatehack (https://climatehack.ai/). We won 1st place!

Climatehack This is the repository for Illinois's Climatehack Team. We earned first place on the leaderboard with a final score of 0.87992. An overvie

Jatin Mathur 20 Jun 09, 2022