A System for Critical Facility and Resource Optimization in Disaster Management and Planning

Disruptions to medical infrastructure during disasters pose significant risks to critically ill patients with advanced chronic kidney disease or end-stage renal disease. To enhance patient access to dialysis treatment under such conditions, it is crucial to assess the vulnerabilities of critical car...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Tung, Emmanuel, Mostafavi, Ali, Li, Maoxu, Li, Sophie, Rasheed, Zeeshan, Shafique, Khurram
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Tung, Emmanuel
Mostafavi, Ali
Li, Maoxu
Li, Sophie
Rasheed, Zeeshan
Shafique, Khurram
description Disruptions to medical infrastructure during disasters pose significant risks to critically ill patients with advanced chronic kidney disease or end-stage renal disease. To enhance patient access to dialysis treatment under such conditions, it is crucial to assess the vulnerabilities of critical care facilities to hazardous events. This study proposes optimization models for patient reallocation and the strategic placement of temporary medical facilities to bolster the resilience of the critical care system, with a focus on equitable outcomes. Utilizing human mobility data from Texas, we evaluate patient access to critical care and dialysis centers under simulated hazard scenarios. The proposed bio-inspired optimization model, based on the Ant Colony optimization method, efficiently reallocates patients to mitigate disrupted access to dialysis facilities. The model outputs offer valuable insights into patient and hospital preparedness for disasters. Overall, the study presents a data-driven, analytics-based decision support tool designed to proactively mitigate potential disruptions in access to critical care facilities during disasters, tailored to the needs of health officials, emergency managers, and hospital system administrators in both the private and public sectors.
doi_str_mv 10.48550/arxiv.2410.02956
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2410_02956</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2410_02956</sourcerecordid><originalsourceid>FETCH-arxiv_primary_2410_029563</originalsourceid><addsrcrecordid>eNqFjrEKwjAURbM4iPoBTr4fsNbaio5SLS6iqKNQHjUtD5KXkkSxfr21uDtdONwDR4jxPAziVZKEM7QvegZR3IIwWifLvrht4NI4LzWUxkJqyVOBCjIsSJFvAPkOZ-nMwxYSjrUnTW_0ZBiIYUsOW9fCARkrqSX7TjgpZCauhqJXonJy9NuBmGS7a7qfdh15bUmjbfJvT971LP4_PhVRQNM</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A System for Critical Facility and Resource Optimization in Disaster Management and Planning</title><source>arXiv.org</source><creator>Tung, Emmanuel ; Mostafavi, Ali ; Li, Maoxu ; Li, Sophie ; Rasheed, Zeeshan ; Shafique, Khurram</creator><creatorcontrib>Tung, Emmanuel ; Mostafavi, Ali ; Li, Maoxu ; Li, Sophie ; Rasheed, Zeeshan ; Shafique, Khurram</creatorcontrib><description>Disruptions to medical infrastructure during disasters pose significant risks to critically ill patients with advanced chronic kidney disease or end-stage renal disease. To enhance patient access to dialysis treatment under such conditions, it is crucial to assess the vulnerabilities of critical care facilities to hazardous events. This study proposes optimization models for patient reallocation and the strategic placement of temporary medical facilities to bolster the resilience of the critical care system, with a focus on equitable outcomes. Utilizing human mobility data from Texas, we evaluate patient access to critical care and dialysis centers under simulated hazard scenarios. The proposed bio-inspired optimization model, based on the Ant Colony optimization method, efficiently reallocates patients to mitigate disrupted access to dialysis facilities. The model outputs offer valuable insights into patient and hospital preparedness for disasters. Overall, the study presents a data-driven, analytics-based decision support tool designed to proactively mitigate potential disruptions in access to critical care facilities during disasters, tailored to the needs of health officials, emergency managers, and hospital system administrators in both the private and public sectors.</description><identifier>DOI: 10.48550/arxiv.2410.02956</identifier><language>eng</language><subject>Computer Science - Neural and Evolutionary Computing</subject><creationdate>2024-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2410.02956$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2410.02956$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Tung, Emmanuel</creatorcontrib><creatorcontrib>Mostafavi, Ali</creatorcontrib><creatorcontrib>Li, Maoxu</creatorcontrib><creatorcontrib>Li, Sophie</creatorcontrib><creatorcontrib>Rasheed, Zeeshan</creatorcontrib><creatorcontrib>Shafique, Khurram</creatorcontrib><title>A System for Critical Facility and Resource Optimization in Disaster Management and Planning</title><description>Disruptions to medical infrastructure during disasters pose significant risks to critically ill patients with advanced chronic kidney disease or end-stage renal disease. To enhance patient access to dialysis treatment under such conditions, it is crucial to assess the vulnerabilities of critical care facilities to hazardous events. This study proposes optimization models for patient reallocation and the strategic placement of temporary medical facilities to bolster the resilience of the critical care system, with a focus on equitable outcomes. Utilizing human mobility data from Texas, we evaluate patient access to critical care and dialysis centers under simulated hazard scenarios. The proposed bio-inspired optimization model, based on the Ant Colony optimization method, efficiently reallocates patients to mitigate disrupted access to dialysis facilities. The model outputs offer valuable insights into patient and hospital preparedness for disasters. Overall, the study presents a data-driven, analytics-based decision support tool designed to proactively mitigate potential disruptions in access to critical care facilities during disasters, tailored to the needs of health officials, emergency managers, and hospital system administrators in both the private and public sectors.</description><subject>Computer Science - Neural and Evolutionary Computing</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFjrEKwjAURbM4iPoBTr4fsNbaio5SLS6iqKNQHjUtD5KXkkSxfr21uDtdONwDR4jxPAziVZKEM7QvegZR3IIwWifLvrht4NI4LzWUxkJqyVOBCjIsSJFvAPkOZ-nMwxYSjrUnTW_0ZBiIYUsOW9fCARkrqSX7TjgpZCauhqJXonJy9NuBmGS7a7qfdh15bUmjbfJvT971LP4_PhVRQNM</recordid><startdate>20241003</startdate><enddate>20241003</enddate><creator>Tung, Emmanuel</creator><creator>Mostafavi, Ali</creator><creator>Li, Maoxu</creator><creator>Li, Sophie</creator><creator>Rasheed, Zeeshan</creator><creator>Shafique, Khurram</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20241003</creationdate><title>A System for Critical Facility and Resource Optimization in Disaster Management and Planning</title><author>Tung, Emmanuel ; Mostafavi, Ali ; Li, Maoxu ; Li, Sophie ; Rasheed, Zeeshan ; Shafique, Khurram</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2410_029563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Neural and Evolutionary Computing</topic><toplevel>online_resources</toplevel><creatorcontrib>Tung, Emmanuel</creatorcontrib><creatorcontrib>Mostafavi, Ali</creatorcontrib><creatorcontrib>Li, Maoxu</creatorcontrib><creatorcontrib>Li, Sophie</creatorcontrib><creatorcontrib>Rasheed, Zeeshan</creatorcontrib><creatorcontrib>Shafique, Khurram</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tung, Emmanuel</au><au>Mostafavi, Ali</au><au>Li, Maoxu</au><au>Li, Sophie</au><au>Rasheed, Zeeshan</au><au>Shafique, Khurram</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A System for Critical Facility and Resource Optimization in Disaster Management and Planning</atitle><date>2024-10-03</date><risdate>2024</risdate><abstract>Disruptions to medical infrastructure during disasters pose significant risks to critically ill patients with advanced chronic kidney disease or end-stage renal disease. To enhance patient access to dialysis treatment under such conditions, it is crucial to assess the vulnerabilities of critical care facilities to hazardous events. This study proposes optimization models for patient reallocation and the strategic placement of temporary medical facilities to bolster the resilience of the critical care system, with a focus on equitable outcomes. Utilizing human mobility data from Texas, we evaluate patient access to critical care and dialysis centers under simulated hazard scenarios. The proposed bio-inspired optimization model, based on the Ant Colony optimization method, efficiently reallocates patients to mitigate disrupted access to dialysis facilities. The model outputs offer valuable insights into patient and hospital preparedness for disasters. Overall, the study presents a data-driven, analytics-based decision support tool designed to proactively mitigate potential disruptions in access to critical care facilities during disasters, tailored to the needs of health officials, emergency managers, and hospital system administrators in both the private and public sectors.</abstract><doi>10.48550/arxiv.2410.02956</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.2410.02956
ispartof
issn
language eng
recordid cdi_arxiv_primary_2410_02956
source arXiv.org
subjects Computer Science - Neural and Evolutionary Computing
title A System for Critical Facility and Resource Optimization in Disaster Management and Planning
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T16%3A21%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20System%20for%20Critical%20Facility%20and%20Resource%20Optimization%20in%20Disaster%20Management%20and%20Planning&rft.au=Tung,%20Emmanuel&rft.date=2024-10-03&rft_id=info:doi/10.48550/arxiv.2410.02956&rft_dat=%3Carxiv_GOX%3E2410_02956%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true