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A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic

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  • Michael Allen
  • Amir Bhanji
  • Jonas Willemsen
  • Steven Dudfield
  • Stuart Logan
  • Thomas Monks

Abstract

This study presents two simulation modelling tools to support the organisation of networks of dialysis services during the COVID-19 pandemic. These tools were developed to support renal services in the South of England (the Wessex region caring for 650 dialysis patients), but are applicable elsewhere. A discrete-event simulation was used to model a worst case spread of COVID-19, to stress-test plans for dialysis provision throughout the COVID-19 outbreak. We investigated the ability of the system to manage the mix of COVID-19 positive and negative patients, the likely effects on patients, outpatient workloads across all units, and inpatient workload at the centralised COVID-positive inpatient unit. A second Monte-Carlo vehicle routing model estimated the feasibility of patient transport plans. If current outpatient capacity is maintained there is sufficient capacity in the South of England to keep COVID-19 negative/recovered and positive patients in separate sessions, but rapid reallocation of patients may be needed. Outpatient COVID-19 cases will spillover to a secondary site while other sites will experience a reduction in workload. The primary site chosen to manage infected patients will experience a significant increase in outpatients and inpatients. At the peak of infection, it is predicted there will be up to 140 COVID-19 positive patients with 40 to 90 of these as inpatients, likely breaching current inpatient capacity. Patient transport services will also come under considerable pressure. If patient transport operates on a policy of one positive patient at a time, and two-way transport is needed, a likely scenario estimates 80 ambulance drive time hours per day (not including fixed drop-off and ambulance cleaning times). Relaxing policies on individual patient transport to 2-4 patients per trip can save 40-60% of drive time. In mixed urban/rural geographies steps may need to be taken to temporarily accommodate renal COVID-19 positive patients closer to treatment facilities.

Suggested Citation

  • Michael Allen & Amir Bhanji & Jonas Willemsen & Steven Dudfield & Stuart Logan & Thomas Monks, 2020. "A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-13, August.
    • Handle: RePEc:plo:pone00:0237628
    DOI: 10.1371/journal.pone.0237628
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    References listed on IDEAS

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    2. Christine S.M. Currie & John W. Fowler & Kathy Kotiadis & Thomas Monks & Bhakti Stephan Onggo & Duncan A. Robertson & Antuela A. Tako, 2020. "How simulation modelling can help reduce the impact of COVID-19," Journal of Simulation, Taylor & Francis Journals, vol. 14(2), pages 83-97, April.
    3. S C Brailsford & P R Harper & B Patel & M Pitt, 2009. "An analysis of the academic literature on simulation and modelling in health care," Journal of Simulation, Taylor & Francis Journals, vol. 3(3), pages 130-140, September.
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    Cited by:

    1. Monks, Thomas & Harper, Alison & Anagnostou, Anastasia & Taylor, Simon J.E., 2022. "Open Science for Computer Simulation," OSF Preprints zpxtm, Center for Open Science.
    2. Shoaib, Mohd & Mustafee, Navonil & Madan, Karan & Ramamohan, Varun, 2023. "Leveraging multi-tier healthcare facility network simulations for capacity planning in a pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).

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