COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care: computer simulation study

Wood, Richard M.; McWilliams, Christopher; Bourdeaux, Christopher; Thomas, Matthew; Vasilakis, Christos

doi:10.1101/2020.04.02.20050898

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…The consequences of surging pandemic care is often not evaluated in prediction or allocation models. For example, Wood et al [48] stated that the act of balancing resources and evaluating the opportunity cost of surging capacity is left as an exercise for decision-makers. Zhang et al [50] included regular care as a baseload factor to the model.…”

Section: Resource Allocation Modellingmentioning

confidence: 99%

Balancing Scarce Hospital Resources During a Pandemic: A Case-Study of COVID-19 at Addenbrooke’s Hospital (UK)

Melman¹,

Parlikad²,

Cameron³

2020

Preprint

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COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke's hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

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Section: Resource Allocation Modellingmentioning

confidence: 99%

Balancing Scarce Hospital Resources During a Pandemic: A Case-Study of COVID-19 at Addenbrooke’s Hospital (UK)

Melman¹,

Parlikad²,

Cameron³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Some of the current models focused on predicting the infection rates in certain populations depending on mitigation measures (10)(11)(12). With this estimation, the fraction of infected patients in need for ICU care can be calculated and compared to existing capacities, leading to a valuation of potential capacity related deaths (13). Other models regarding the length of hospital stay concentrated on time-to-death (14).…”

Section: Introductionmentioning

confidence: 99%

Tackling the Waves of COVID-19: A Planning Model for Intrahospital Resource Allocation

Schmidt

Hauptmann

Kohlenz³

et al. 2021

Front.Health Serv.

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Background: The current pandemic requires hospitals to ensure care not only for the growing number of COVID-19 patients but also regular patients. Hospital resources must be allocated accordingly.Objective: To provide hospitals with a planning model to optimally allocate resources to intensive care units given a certain incidence of COVID-19 cases.Methods: The analysis included 334 cases from four adjacent counties south-west of Munich. From length of stay and type of ward [general ward (NOR), intensive care unit (ICU)] probabilities of case numbers within a hospital at a certain time point were derived. The epidemiological situation was simulated by the effective reproduction number R, the infection rates in mid-August 2020 in the counties, and the German hospitalization rate. Simulation results are compared with real data from 2nd and 3rd wave (September 2020–May 2021).Results: With R = 2, a hospitalization rate of 17%, mitigation measures implemented on day 9 (i.e., 7-day incidence surpassing 50/100,000), the peak occupancy was reached on day 22 (155.1 beds) for the normal ward and on day 25 (44.9 beds) for the intensive care unit. A higher R led to higher occupancy rates. Simulated number of infections and intensive care unit occupancy was concordant in validation with real data obtained from the 2nd and 3rd waves in Germany.Conclusion: Hospitals could expect a peak occupancy of normal ward and intensive care unit within ~5–11 days after infections reached their peak and critical resources could be allocated accordingly. This delay (in particular for the peak of intensive care unit occupancy) might give options for timely preparation of additional intensive care unit resources.

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“…DES of health care systems and performance modeling in health care using DES were extensively reviewed in [5][6][7]. Emerging applications of DES addressing the COVID-19 pandemic hospital planning problems can be found in [8][9][10]. The review of how simulation modeling can help reduce the impact of COVID-19 was presented in [11].…”

Section: Introductionmentioning

confidence: 99%

Hospital beds and personal protective equipment planning for COVID-19 pandemic: A computer simulation approach

Guan

Fishe

et al. 2020

Preprint

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Health care systems are at the frontline to fight the COVID-19 pandemic. An emergent question for each hospital is how many general ward and intensive care unit beds are needed and how much personal protective equipment to be purchased. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a computer simulation approach to evaluating bed utilizations and the corresponding supply needs based on the operational considerations and constraints in individual hospitals. We built a data-driven SEIR model which is adaptive to control policies and can be utilized for regional forecast targeting a specific hospital’s catchment area. The forecast model was integrated into a discrete-event simulation which modeled the patient flow and the interaction with hospital resources. We tested the simulation model outputs against patient census data from UF Health Jacksonville, Jacksonville, FL. Simulation results were consistent with the observation that the hospital has ample bed resources to accommodate the regional COVID patients. After validation, the model was used to predict future bed utilizations given a spectrum of possible scenarios to advise bed planning and stockpiling decisions. Lastly, how to optimally allocate hospital resources to achieve the goal of reducing the case fatality rate while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge.

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COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care: computer simulation study

Cited by 6 publications

References 33 publications

Balancing Scarce Hospital Resources During a Pandemic: A Case-Study of COVID-19 at Addenbrooke’s Hospital (UK)

Balancing Scarce Hospital Resources During a Pandemic: A Case-Study of COVID-19 at Addenbrooke’s Hospital (UK)

Tackling the Waves of COVID-19: A Planning Model for Intrahospital Resource Allocation

Hospital beds and personal protective equipment planning for COVID-19 pandemic: A computer simulation approach

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