VIRIS: Simulating indoor airborne transmission combining architectural design and people movement
- URL: http://arxiv.org/abs/2408.11772v1
- Date: Wed, 21 Aug 2024 16:54:22 GMT
- Title: VIRIS: Simulating indoor airborne transmission combining architectural design and people movement
- Abstract summary: A Viral Infection Risk Indoor Simulator (VIRIS) has been developed to quickly assess and compare mitigations for airborne disease spread.
This agent-based simulator combines people movement in an indoor space, viral transmission modelling and detailed architectural design.
- Score: 0.3749861135832073
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A Viral Infection Risk Indoor Simulator (VIRIS) has been developed to quickly assess and compare mitigations for airborne disease spread. This agent-based simulator combines people movement in an indoor space, viral transmission modelling and detailed architectural design, and it is powered by topologicpy, an open-source Python library. VIRIS generates very fast predictions of the viral concentration and the spatiotemporal infection risk for individuals as they move through a given space. The simulator is validated with data from a courtroom superspreader event. A sensitivity study for unknown parameter values is also performed. We compare several non-pharmaceutical interventions (NPIs) issued in UK government guidance, for two indoor settings: a care home and a supermarket. Additionally, we have developed the user-friendly VIRIS web app that allows quick exploration of diverse scenarios of interest and visualisation, allowing policymakers, architects and space managers to easily design or assess infection risk in an indoor space.
Related papers
- CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior [4.272788079372737]
The risk of indoor airborne transmission among co-located individuals is generally non-uniform.<n>We present CompARE, an integrated risk assessment framework for indoor airborne disease transmission.
arXiv Detail & Related papers (2025-11-26T12:53:30Z) - AeroSafe: Mobile Indoor Air Purification using Aerosol Residence Time Analysis and Robotic Cough Emulator Testbed [3.2207003694189997]
This paper introduces AeroSafe, a novel approach aimed at enhancing the efficacy of indoor air purification systems.<n>It includes a robotic cough emulator testbed and a digital-twins-based aerosol residence time analysis.<n>Results demonstrate the model's ability to predict aerosol concentration dynamics with a mean residence time prediction error within 35 seconds.
arXiv Detail & Related papers (2025-08-04T23:11:37Z) - HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions [76.42274173122328]
We present HAICOSYSTEM, a framework examining AI agent safety within diverse and complex social interactions.
We run 1840 simulations based on 92 scenarios across seven domains (e.g., healthcare, finance, education)
Our experiments show that state-of-the-art LLMs, both proprietary and open-sourced, exhibit safety risks in over 50% cases.
arXiv Detail & Related papers (2024-09-24T19:47:21Z) - 3D Topological Modeling and Multi-Agent Movement Simulation for Viral Infection Risk Analysis [0.3749861135832073]
This paper integrates computer-aided modeling, multi-agent movement simulation, and airborne viral transmission modeling.
Topologicpy spatial design and analysis software is used to model indoor environments, connect spaces, and construct a navigation graph.
arXiv Detail & Related papers (2024-08-29T10:22:51Z) - Protect Your Score: Contact Tracing With Differential Privacy Guarantees [68.53998103087508]
We argue that privacy concerns currently hold deployment back.
We propose a contact tracing algorithm with differential privacy guarantees against this attack.
Especially for realistic test scenarios, we achieve a two to ten-fold reduction in the infection rate of the virus.
arXiv Detail & Related papers (2023-12-18T11:16:33Z) - UniSim: A Neural Closed-Loop Sensor Simulator [76.79818601389992]
We present UniSim, a neural sensor simulator that takes a single recorded log captured by a sensor-equipped vehicle.
UniSim builds neural feature grids to reconstruct both the static background and dynamic actors in the scene.
We incorporate learnable priors for dynamic objects, and leverage a convolutional network to complete unseen regions.
arXiv Detail & Related papers (2023-08-03T17:56:06Z) - DriveGAN: Towards a Controllable High-Quality Neural Simulation [147.6822288981004]
We introduce a novel high-quality neural simulator referred to as DriveGAN.
DriveGAN achieves controllability by disentangling different components without supervision.
We train DriveGAN on multiple datasets, including 160 hours of real-world driving data.
arXiv Detail & Related papers (2021-04-30T15:30:05Z) - Towards Accurate Spatiotemporal COVID-19 Risk Scores using High
Resolution Real-World Mobility Data [15.302926747159557]
We develop a Hawkes process-based technique to assign relatively fine-grain spatial and temporal risk scores.
We focus on developing risk scores based on location density and mobility behaviour.
Our results show that fine-grain risk scores based on high-resolution mobility data can provide useful insights and facilitate safe re-opening.
arXiv Detail & Related papers (2020-12-14T06:31:28Z) - An Autonomous Approach to Measure Social Distances and Hygienic
Practices during COVID-19 Pandemic in Public Open Spaces [5.356127650643356]
Coronavirus has been spreading around the world since the end of 2019.
Most states have issued state-at-home executive orders, however, parks and other public open spaces have largely remained open.
This work provides a scalable sensing approach to detect physical activities within public open spaces.
arXiv Detail & Related papers (2020-11-14T19:35:09Z) - Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
We develop Bayesian inference methods to estimate the risk that an individual is infected.
We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic.
Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
arXiv Detail & Related papers (2020-09-20T12:24:45Z) - Inter-Homines: Distance-Based Risk Estimation for Human Safety [44.266630835933434]
Our system evaluates in real-time the contagion risk in a monitored area by analyzing video streams.
It is able to locate people in 3D space, calculate distances and predict risk levels.
Inter-Ho-mines works both indoor and outdoor, in public and private crowded areas.
arXiv Detail & Related papers (2020-07-20T16:32:27Z) - COVI White Paper [67.04578448931741]
Contact tracing is an essential tool to change the course of the Covid-19 pandemic.
We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
arXiv Detail & Related papers (2020-05-18T07:40:49Z) - How to restart? An agent-based simulation model towards the definition
of strategies for COVID-19 "second phase" in public buildings [0.0]
This work provides an Agent-Based Model to estimate the virus spreading in the closed built environment.
The model is calibrated on experimental data and then applied to a relevant case-study.
arXiv Detail & Related papers (2020-04-27T16:40:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.