A queuing model for ventilator capacity management during the COVID-19 pandemic

Zimmerman, Samantha; Rutherford, Alexander R.; Waall, Alexa van der; Norena, Monica; Dodek, Peter

doi:10.1007/s10729-023-09632-9

Cited by 3 publications

(2 citation statements)

References 65 publications

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“…They proposed a healthcare resource allocation model using a hybrid system dynamics-discrete event simulation model. Zimmerman et al (2023) modelled COVID-19 progression and linked it with ventilator utilisation to propose effective COVID-19 prevention strategies. Gambaro et al (2023) modelled the ICU capacity expansion plan in Italy using the stochastic extension of the susceptible, infected, removed (SIR) model.…”

Section: Literature Reviewmentioning

confidence: 99%

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A study to forecast healthcare capacity dynamics in the wake of the COVID-19 pandemic

Patil,

Shardeo,

Madaan

et al. 2023

IJPDLM

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PurposeThis study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.Design/methodology/approachThis study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.FindingsThe results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.Practical implicationsThe models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.Originality/valueThe study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

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Section: Literature Reviewmentioning

confidence: 99%

“…They proposed a healthcare resource allocation model using a hybrid system dynamics-discrete event simulation model. Zimmerman et al. (2023) modelled COVID-19 progression and linked it with ventilator utilisation to propose effective COVID-19 prevention strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

A study to forecast healthcare capacity dynamics in the wake of the COVID-19 pandemic

Patil,

Shardeo,

Madaan

et al. 2023

IJPDLM

View full text Add to dashboard Cite

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A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation

Ordu,

Demir,

Tofallis

et al. 2023

Healthcare Analytics

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Applying Neural Networks to Predict Ventilator Demand: A Study of Pakistan's Healthcare Sector

Abbasi,

Daniyal,

Abro

et al. 2024

VFAST trans. softw. eng.

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The distribution companies that deal with ventilators in Pakistan face challenges related to inventory control because of inadequate product shelf life, shortages, excess inventory, and unnecessary stock. This study, which focuses on Pakistani ventilator distribution companies, aims to offer a novel approach to sales estimation, avoid unnecessary stock expenditures, and stop clientele loss brought on by ventilator shortages. The results of this study will help determine key elements and standards that Pakistani distributors of ventilators might employ to boost sales. Most ventilator distribution businesses in Pakistan are independent wholesalers that purchase stock from their stores and distribute it to customers. To maximize ventilator distribution firms' sales for various products, this study examined distribution and sales data from 2019 to 2024 for many locations and dates. To create an accurate sales forecasting model for a ventilator distribution company, this research also aims to apply artificial neural networks (ANN) for effective sales prediction. An Artificial Neural Network (ANN) model was trained using a dataset from ventilator distribution businesses and the proposed model produced an accuracy of 90%, which is good for early prediction.

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A queuing model for ventilator capacity management during the COVID-19 pandemic

Cited by 3 publications

References 65 publications

A study to forecast healthcare capacity dynamics in the wake of the COVID-19 pandemic

A study to forecast healthcare capacity dynamics in the wake of the COVID-19 pandemic

A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation

Applying Neural Networks to Predict Ventilator Demand: A Study of Pakistan's Healthcare Sector

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