EXPOSED : An occupant exposure model for confined spaces to retrofit crowd models during a pandemic

© 2020 The Author(s)..

Crowd models can be used for the simulation of people movement in the built environment. Crowd model outputs have been used for evaluating safety and comfort of pedestrians, inform crowd management and perform forensic investigations. Microscopic crowd models allow the representation of each person and the obtainment of information concerning their location over time and interactions with the physical space/other people. Pandemics such as COVID-19 have posed several questions on safe building usage, given the risk of disease transmission among building occupants. Here we show how crowd modelling can be used to assess occupant exposure in confined spaces. The policies adopted concerning building usage and social distancing during a pandemic can vary greatly, and they are mostly based on the macroscopic analysis of the spread of disease rather than a safety assessment performed at a building level. The proposed model allows the investigation of occupant exposure in buildings based on the analysis of microscopic people movement. Risk assessment is performed by retrofitting crowd models with a universal model for exposure assessment which can account for different types of disease transmissions. This work allows policy makers to perform informed decisions concerning building usage during a pandemic.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:130

Enthalten in:

Safety science - 130(2020) vom: 15. Okt., Seite 104834

Sprache:

Englisch

Beteiligte Personen:

Ronchi, Enrico [VerfasserIn]
Lovreglio, Ruggiero [VerfasserIn]

Links:

Volltext

Themen:

COVID-19
Crowd model
Disease transmission
Journal Article
Occupant exposure
People movement

Anmerkungen:

Date Revised 15.12.2020

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1016/j.ssci.2020.104834

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM31407936X