Hospital Beds Planning and Admission Control Policies for COVID-19 Pandemic: A Hybrid Computer Simulation Approach

Abstract

Health care systems are at the front line to fight the COVID-19 pandemic. Emergent questions for each hospital are how many general ward and intensive care unit beds are needed, and additionally, how to optimally allocate these resources during demand surge to effectively save lives. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a hybrid computer simulation approach, with a system dynamic model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization and subsequently determine bed allocations. Two control policies, the type-dependent admission control policy and the early stepdown policy, based on patient risk profiling, were proposed to lower the overall death rate of the patient population in need of intensive care. The model was validated using historical patient census data from the

ICU capacity expansion under uncertainty in the early stages of a pandemic

“…The pandemic also motivated some researchers to make modifications: Lu et al (2021), Wood et al (2020), andShi et al (2022) assume a deterministic time-dependent arrival rate linked to the average trend of the epidemic. In particular, Lu et al (2021) The wider healthcare literature also uses queuing models with an infinite number of servers (hence, no queuing) to capture the future demand for patients needing a constrained resource like intensive care. Heemskerk et al (2017Heemskerk et al ( , 2022 also propose an infinite-server queuing system with a mixed Poisson arrival process in a random environment, a special case of a stationary Cox process.…”

“…The demand‐focused stream investigates “flattening the epidemic curve” (Evgeniou et al., 2022; Ferguson et al., 2020; Jain & Rayal, 2023; Perkins & Espana, 2020; Shahmanzari et al., 2022) containment measures such as lockdown or quarantine, given a fixed capacity for the ICU (or other pertinent resource). The supply‐focused stream, in which our paper is positioned, focuses on capacity expansion of ICU (or other resource) (Alban et al., 2020; Lu et al., 2021; Ouyang et al., 2020; Shi et al., 2022; Wood et al., 2020). Unlike our use of three models—epidemic, demand, and optimization—many researchers have used just two of the modules.…”

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

“…There are only 11 (3%) papers concerning hybrid simulation: Six papers were a combination of ABM and DES (Asgary et al, 2020 ; Cimini et al, 2021 ; Possik et al, 2021 ; Qiu et al, 2021 ; Stapelberg et al, 2021 ; Tofighi et al, 2021 ), three papers were a combination of DES and SDM (Kang et al, 2021 ; Lu, Guan, et al, 2021 ; Warde et al, 2021 ) and two papers were an integration of SDM and ABM (Guo, Tong, et al, 2021 ; Mokhtari et al, 2021 ).…”

“…Twenty‐nine papers focused on COVID‐19 epidemic prediction, of which seven tried to estimate the R 0 in different regions (Müller et al, 2021 ; Rypdal et al, 2021 ; Yang et al, 2020 ) and countries (Guo & Xiao, 2020 ; Hoertel, Blachier, Blanco, Olfson, Massetti, Rico, et al, 2020 ; Kolokolnikov & Iron, 2021 ; Krivorotko et al, 2022 ). Most studies made prediction regarding cumulative infections (Hunter & Kelleher, 2021 ; Latkowski & Dunin‐Kȩplicz, 2021 ) and deaths (Ghaffarzadegan & Rahmandad, 2020 ), mortality (Benneyan et al, 2021 ; Lu, Guan, et al, 2021 ), daily testing capacity required (Fiore et al, 2021 ), hospital admissions (Warde et al, 2021 ) and demand for intensive care unit (ICU) beds (Bartz‐Beielstein et al, 2021 ; Garcia‐Vicuña et al, 2021 ; Irvine et al, 2021 ) and so forth as different interventions, such as physical distancing (Aghaei & Lohrasebi, 2021 ), various lockdown (Hoertel, Blachier, Blanco, Olfson, Massetti, Rico, et al, 2020 ; Uansri et al, 2021 ) and vaccination strategy (Suphanchaimat, Nittayasoot, et al, 2021 ; Suphanchaimat, Tuangratananon, et al, 2021 ). The rest predicted the future spread under school reopening (España et al, 2021 ; Rypdal et al, 2021 ; Son & RISEWIDs Team, 2020 ), city reopening (Yin et al, 2021 ), society activities reopening (Cremonini & Maghool, 2020 ) and international borders reopening (Pham et al, 2021 ).…”

Simulation Study of the Impact of COVID-19 Policies on the Efficiency of a Smart Clinic MRI Service

“…Several studies have focused on the influence of COVID-19 on EDs and ICUs [3][4][5][6][7][8][9], while less emphasis has been given to outpatient services [2,10,11]. This is a consequence of the non-urgent services being locked down for extended periods, while EDs and ICUs have faced ongoing emergencies.…”