2020
DOI: 10.1101/2020.03.31.20049239
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A flexible method for optimising sharing of healthcare resources and demand in the context of the COVID-19 pandemic

Abstract: As the number of cases of COVID-19 continues to grow exponentially, local health services are likely to be overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data extracted from UK and Spain. For the UK case, we coarse-grain the NHS system at the level of NHS trusts and, su… Show more

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Cited by 6 publications
(5 citation statements)
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“…Instead, patients should be transferred early and proactively out of emerging hotspots to less affected regions, in order to prevent the non-linear rise in patient mortality as the COVID-19 workload on regional ICUs rises. We base this recommendation on our empirical evidence, yet our conclusion is supported both by early modelling evidence (Alban et al 2020, Lacasa et al 2020), as well as case evidence from regions that have successfully employed a patient transfer strategy (Pett et al 2020).…”
Section: Discussionmentioning
confidence: 76%
“…Instead, patients should be transferred early and proactively out of emerging hotspots to less affected regions, in order to prevent the non-linear rise in patient mortality as the COVID-19 workload on regional ICUs rises. We base this recommendation on our empirical evidence, yet our conclusion is supported both by early modelling evidence (Alban et al 2020, Lacasa et al 2020), as well as case evidence from regions that have successfully employed a patient transfer strategy (Pett et al 2020).…”
Section: Discussionmentioning
confidence: 76%
“…Also, they find that it is essential to ramp up the production of the ventilators to meet the additional requirements of the ventilators that might come up during the peak times of the pandemic. Lacasa et al (2020) come up with an algorithm for optimizing the allocation of the ventilators and ICU beds and validate their algorithm during the peak and declining times of the pandemic based on the data from the United Kingdom and Spain cases. Bertsimas et al (2020) develop a four-step approach, combining descriptive, predictive, and prescriptive analytics and propose an optimization model for the re-allocation of the ventilators throughout the U.S. during the COVID-19 pandemic.…”
Section: Literature Reviewmentioning
confidence: 99%
“…As a real world example of patient allocation schemes during the COVID pandemic, Boudourakis et al (2020) present the successes and challenges associated with patient transfers among New York City hospitals. In the most relevant existing literature, Bai and Zhang (2014), Sun et al (2014) and Lacasa et al (2020) discuss the problem of optimal patient allocation in a pandemic. Lacasa et al (2020) consider the problem of distributing a single resource or demand across a regular geometric graph with healthcare centers as vertices.…”
Section: Related Workmentioning
confidence: 99%
“…In the most relevant existing literature, Bai and Zhang (2014), Sun et al (2014) and Lacasa et al (2020) discuss the problem of optimal patient allocation in a pandemic. Lacasa et al (2020) consider the problem of distributing a single resource or demand across a regular geometric graph with healthcare centers as vertices. They provide solutions as a set of resource transfers using random search optimization.…”
Section: Related Workmentioning
confidence: 99%