Optimization models for patient allocation during a pandemic influenza outbreak.

Sun, Lulu

doi:10.18297/etd/1405

Cited by 1 publication

(1 citation statement)

References 28 publications

(39 reference statements)

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“…The author's model also predicts the resource shortage and optimizes the resource allocation when they become available, the allocation is done based on patients' cost to access care, minimizing travel distance and burden. The authors showed through their model that patient and resource allocation can reduce the overall burden during such crisis (Sun 2011). Vaccine distribution is equally important to control the spread of the virus.…”

Section: Literature Reviewmentioning

confidence: 99%

Model for Acceptance of Patients During a Pandemic Using Space Search Optimization

Malik Fuloria,

Sormaz

2023

19th International Scientific Conference on Industrial Systems

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Resource allocation is one of the major problems in a healthcare system. The problem gets worse during a disaster situation like a pandemic. The demand is high, and the resources are limited, this puts a question on which patient should be admitted. This research focuses on increasing the number of patient admissions by deciding who should be admitted based on the patient's severity and length of stay. In the research, the patient has a fixed length of stay and if a patient is rejected, he/she is rejected forever. The research focuses on one critical resource, and it needs to be available to admit the patient. The daily capacity of resources is fixed. The demand during the pandemic is dynamic, so this research solves the problem using space search optimization which is one of the techniques of dynamic programming. The research uses depth-first and breadth-first space search algorithms. The experiments with randomized data were executed to see the effect of available capacity on the number of admitted patients. This research shows that space search algorithms are useful techniques for the optimization of resources and patients during a pandemic.

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Section: Literature Reviewmentioning

confidence: 99%