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Using Machine Learning Approaches to Enhance Heatwave Measurement for Vulnerability Assessment and Timely Management of Heat-related Health Services

Authors :
Le Jian
Dimpal Patel
Jing Guo
Jianguo Xiao
Janis Jansz
Grace Yun
Ting Lin
Laura Kirkland
Tim Landrigan
Andrew Robertson
Source :
Asia Pacific Journal of Health Management (2024)
Publication Year :
2024
Publisher :
ACHSM, 2024.

Abstract

Climate change is one of the most critical challenges facing Australia and the global community today. Data from the Australian Bureau of Meteorology (BoM) indicates that Australia has been experiencing rising temperatures, particularly since the late 20th century. The frequency, duration, and intensity of heatwaves are projected to continue increasing . Since national records began in 1910, Australia has warmed by an average of 1.47°C (±0.24°C), with the highest official temperature recorded at 50.7 degrees Celsius in Onslow, Western Australia (WA), on January 13, 2022. Furthermore, a recent unprecedented high temperature of +41.6°C was recorded during winter on August 26, 2024, in Yampi Sound, WA. Among all natural disasters in Australia, heatwave (HW) represents a leading silent killer and pose a significant public health threat. However, innovative methods for assessing vulnerability for HW-related health services remain limited.

Details

Language :
English
ISSN :
18333818 and 22043136
Database :
Directory of Open Access Journals
Journal :
Asia Pacific Journal of Health Management
Publication Type :
Academic Journal
Accession number :
edsdoj.92b197ae6bd5488ba2fc41649ed11c54
Document Type :
article
Full Text :
https://doi.org/10.24083/apjhm.v19i3.4195