A Bayesian analysis of domestic fire response and fire injury

In this article domestic fire response and fire injury was examined using a Bayesian analysis approach. A Bayesian model was developed to estimate the probability of non-fatal fire injury associated with a given fire response (escape, return to fire, fight the fire) under given circumstances of non-fatal fire injury (age band, gender, smoke alarm presence, type of domestic fire). The Bayesian model was developed using non-fatal fire injury data recorded by Merseyside Fire and Rescue Service between 2011 and 2022. Overall, more domestic fire injuries relating to attempting to fight the fire occurred in properties with a smoke detector (82 % of attempting to fight the fire injuries) compared to properties without a smoke detector (18 % of attempting to fight the fire injuries). Similarly, fire injuries sustained returning to the fire mainly occurred in properties with a smoke detector (75 % of returning to fire injuries) as opposed to properties without a smoke detector (25 % of returning to fire injuries).

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Publication Details

Journal
Fire Safety Journal
Published
2024-09-14
DOI
https://doi.org/10.1016/j.firesaf.2024.104266
Citations
3
Primary Topic
Fire dynamics and safety research
Type
article
Field-Weighted Citation Impact
1.09
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article

A Bayesian analysis of domestic fire response and fire injury

John Fielding, Mark John Taylor, Denis Reilly, Vince Kwasnica
3 citations
Fire Safety Journal
Fire dynamics and safety research
1.09
article

A Bayesian analysis of domestic fire response and fire injury

John Fielding, Mark John Taylor, Denis Reilly, Vince Kwasnica
article en
3 citations

Abstract

In this article domestic fire response and fire injury was examined using a Bayesian analysis approach. A Bayesian model was developed to estimate the probability of non-fatal fire injury associated with a given fire response (escape, return to fire, fight the fire) under given circumstances of non-fatal fire injury (age band, gender, smoke alarm presence, type of domestic fire). The Bayesian model was developed using non-fatal fire injury data recorded by Merseyside Fire and Rescue Service between 2011 and 2022. Overall, more domestic fire injuries relating to attempting to fight the fire occurred in properties with a smoke detector (82 % of attempting to fight the fire injuries) compared to properties without a smoke detector (18 % of attempting to fight the fire injuries). Similarly, fire injuries sustained returning to the fire mainly occurred in properties with a smoke detector (75 % of returning to fire injuries) as opposed to properties without a smoke detector (25 % of returning to fire injuries).

Fire Safety JournalVol. 150
Merseytravel (GB), Liverpool John Moores University (GB)
Openalex Percentile: Top 23%
Fire dynamics and safety research
1.09
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A Bayesian analysis of domestic fire response and fire injury — John Fielding, Mark John Taylor, et al. · Fire Safety Journal (2024) | TGRS Research Map | TGRS