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Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

ABSTRACT: BACKGROUND:The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations. OBJECTIVE:To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated. DESIGN:Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle. SETTING:3 hospitals in an academic health system. PATIENTS:All people living in the greater Philadelphia region. MEASUREMENTS:The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators. RESULTS:Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12?650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators. LIMITATIONS:Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction. CONCLUSION:Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic. PRIMARY FUNDING SOURCE:University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.

SUBMITTER: Weissman GE

PROVIDER: S-EPMC7153364 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

Weissman Gary E GE Crane-Droesch Andrew A Chivers Corey C Luong ThaiBinh T Hanish Asaf A Levy Michael Z MZ Lubken Jason J Becker Michael M Draugelis Michael E ME Anesi George L GL Brennan Patrick J PJ Christie Jason D JD Hanson C William CW Mikkelsen Mark E ME Halpern Scott D SD

Annals of internal medicine 20200407 1

<h4>Background</h4>The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.<h4>Objective</h4>To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.<h4>Design</h4>Monte Carlo simulat ...[more]

PMID: 32259197

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Project description:BackgroundTo calculate hospital surge capacity, achieved via hospital provision interventions implemented for the emergency treatment of coronavirus disease 2019 (COVID-19) and other patients through March to May 2020; to evaluate the conditions for admitting patients for elective surgery under varying admission levels of COVID-19 patients.MethodsWe analysed National Health Service (NHS) datasets and literature reviews to estimate hospital care capacity before the pandemic (pre-pandemic baseline) and to quantify the impact of interventions (cancellation of elective surgery, field hospitals, use of private hospitals, deployment of former medical staff and deployment of newly qualified medical staff) for treatment of adult COVID-19 patients, focusing on general and acute (G&A) and critical care (CC) beds, staff and ventilators.ResultsNHS England would not have had sufficient capacity to treat all COVID-19 and other patients in March and April 2020 without the hospital provision interventions, which alleviated significant shortfalls in CC nurses, CC and G&A beds and CC junior doctors. All elective surgery can be conducted at normal pre-pandemic levels provided the other interventions are sustained, but only if the daily number of COVID-19 patients occupying CC beds is not greater than 1550 in the whole of England. If the other interventions are not maintained, then elective surgery can only be conducted if the number of COVID-19 patients occupying CC beds is not greater than 320. However, there is greater national capacity to treat G&A patients: without interventions, it takes almost 10,000 G&A COVID-19 patients before any G&A elective patients would be unable to be accommodated.ConclusionsUnless COVID-19 hospitalisations drop to low levels, there is a continued need to enhance critical care capacity in England with field hospitals, use of private hospitals or deployment of former and newly qualified medical staff to allow some or all elective surgery to take place.

Project description:INTRODUCTION:The COVID-19 pandemic will test the capacity of health systems worldwide and especially so in low- and middle-income countries. The objective of this study was to assess the surge capacity of the Kenyan of the Kenyan health system in terms of general hospital and ICU beds in the face of the COVID-19 pandemic. METHODS:We assumed that 2% of the Kenyan population get symptomatic infection by SARS-Cov-2 based on modelled estimates for Kenya and determined the health system surge capacity for COVID-19 under three transmission curve scenarios, 6, 12, and 18 months. We estimated four measures of hospital surge capacity namely: 1) hospital bed surge capacity 2) ICU bed surge capacity 3) Hospital bed tipping point, and 5) ICU bed tipping point. We computed this nationally and for all the 47 county governments. RESULTS:The capacity of Kenyan hospitals to absorb increases in caseload due to COVID-19 is constrained by the availability of oxygen, with only 58% of hospital beds in hospitals with oxygen supply. There is substantial variation in hospital bed surge capacity across counties. For example, under the 6 months transmission scenario, the percentage of available general hospital beds that would be taken up by COVID-19 cases varied from 12% Tharaka Nithi county, to 145% in Trans Nzoia county. Kenya faces substantial gaps in ICU beds and ventilator capacity. Only 22 out of the 47 counties have at least 1 ICU unit. Kenya will need an additional 1,511 ICU beds and 1,609 ventilators (6 months transmission curve) to 374 ICU beds and 472 ventilators (18 months transmission curve) to absorb caseloads due to COVID-19. CONCLUSION:Significant gaps exist in Kenya's capacity for hospitals to accommodate a potential surge in caseload due to COVID-19. Alongside efforts to slow and supress the transmission of the infection, the Kenyan government will need to implement adaptive measures and additional investments to expand the hospital surge capacity for COVID-19. Additional investments will however need to be strategically prioritized to focus on strengthening essential services first, such as oxygen availability before higher cost investments such as ICU beds and ventilators.

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Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

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Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

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