A System for Critical Facility and Resource Optimization in Disaster Management and Planning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916422825803776 |
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| author | Tung, Emmanuel Mostafavi, Ali Li, Maoxu Li, Sophie Rasheed, Zeeshan Shafique, Khurram |
| author_facet | Tung, Emmanuel Mostafavi, Ali Li, Maoxu Li, Sophie Rasheed, Zeeshan Shafique, Khurram |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02956 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A System for Critical Facility and Resource Optimization in Disaster Management and Planning Tung, Emmanuel Mostafavi, Ali Li, Maoxu Li, Sophie Rasheed, Zeeshan Shafique, Khurram Neural and Evolutionary Computing 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. |
| title | A System for Critical Facility and Resource Optimization in Disaster Management and Planning |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2410.02956 |