Hospital admission, facility‐based isolation, and social distancing: An SEIR model with constrained medical resources

Chen, Zhiyuan; Kong, Guangwen

doi:10.1111/poms.13702

Cited by 10 publications

(9 citation statements)

References 30 publications

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“…Z. Chen and Kong (2023) and Gonçalves et al (2023) propose deterministic compartmental models, which include hospitalized and critically ill patients.…”

Section: Capacity Demand Modelingmentioning

confidence: 99%

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

Gambaro

Fusai

Sodhi

et al. 2023

Production and Operations Management

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We propose a general modular approach to support decision‐makers' response in the early stages of a pandemic with resource expansion, motivated by the shortage of Covid‐19‐related intensive care units (ICU) capacity in 2020 in Italy. Our approach uses (1) a stochastic extension of an epidemic model for scenarios of projected infections, (2) a capacity load model to translate infections into scenarios of demand for the resources of interest, and (3) an optimization model to allocate this demand to the projected levels of resources based on different values of investment. We demonstrate this approach with the onset of the first and second Covid‐19 waves in three Italian regions, using the data available at that time. For epidemic modeling, we used a parsimonious stochastic susceptible‐infected‐removed model with a robust estimation procedure based on bootstrap resampling, suitable for a noisy and data‐limited environment. For capacity loading, we used a Cox queuing model to translate the projected infections into demand for ICU, using stochastic intensity to capture the variability of the patient arrival process. Finally, we used stochastic dynamic optimization to select the best policy (when and how much to expand) to minimize the expected number of patients denied ICU for any level of investment in capacity expansion and obtain an efficient frontier. The frontier allows a trade‐off between investment in additional resources and the number of patients denied intensive care. Moreover, in the panic‐driven early days of a pandemic, decision‐makers can also obtain the time until which they can postpone action, potentially reducing investment costs without increasing the expected number of denied patients.

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“…Z. Chen and Kong (2023) and Gonçalves et al (2023) propose deterministic compartmental models, which include hospitalized and critically ill patients.…”

Section: Capacity Demand Modelingmentioning

confidence: 99%

“…In the supply‐focused literature, demand is modeled with compartmental or queuing models. Z. Chen and Kong (2023) and Gonçalves et al. (2023) propose deterministic compartmental models, which include hospitalized and critically ill patients.…”

Section: Contribution To the Literaturementioning

confidence: 99%

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

Gambaro

Fusai

Sodhi

et al. 2023

Production and Operations Management

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“…Chen and Kong (2023) develop and test a model to study the impact of medical resources on reducing cumulative deaths and containing the virus while allowing for a capacity constraint on hospital beds. The model adds two state variables to the classic SEIR model to yield a SEIRHQD structure, Susceptible (S) Exposed (E), Infectious (I), Recovered (R), Hospitalized (H), Quarantined (Q), and Death (D).…”

Section: Hospital Operations/capacitymentioning

confidence: 99%

“…We accepted four papers that deal with hospital capacity and operations. These include: “Pausing transplants in the face of a global pandemic: Patient survival implications” (Zhang et al., 2023), “Hospital Admission, Facility‐based Isolation, and Social Distancing: An SEIR Model with Constrained Medical Resources” (Chen & Kong, 2023), “Managing Medical Equipment Capacity with Early Spread of Infection in a Region” (Jain & Rayal, 2023), and “Operations (Management) Warp Speed: Rapid Deployment of Hospital‐Focused Predictive/Prescriptive Analytics for the COVID‐19 Pandemic” (Shi et al., 2023).…”

Section: Hospital Operations/capacitymentioning

confidence: 99%

Managing pandemics: A POM perspective and directions for future research

Anderson

Gupta

Joglekar

et al. 2023

Production and Operations Management

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COVID‐19 pandemic has revealed how unprepared operations and supply chain professionals are for “abnormal” conditions. Understanding how the production and operations management field can affect the trajectory and especially the remediation of pandemics is a critical, but understudied, area of research from descriptive, predictive, and prescriptive perspectives. Fourteen research articles in this special issue have attempted to fill this gap with rigor. We first summarize these articles in six categories, (1) public policies and government interventions, (2) hospital capacity, (3) propagation of pandemics, (4) humanitarian operations, (5) private partnerships, and (6) vaccine production, by drawing out the themes addressed. As we look ahead at pandemics yet to come, we note there is still much research needed and conclude by discussing emergent interest in promising themes for studying pre‐pandemic, during pandemic, and post‐pandemic operations.

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“…Chehrazi et al (2019) investigated an optimal control problem for a susceptible‐infected‐susceptible (SIS) model of an infectious disease with resistance and demonstrated that the optimal prescription policy is of the bang‐bang type with a single switching time. Chen and Kong (2022) studied the effects of different hospital admission policies on the spread of infectious diseases using a modified susceptible‐exposed‐infected‐recovered (SEIR) model under a static social distancing policy. Additionally, (El Housni et al, 2022a; El Housni et al, 2022b) developed new compartmentalized models to study the effects of testing capacity and social distancing measures on the pandemic in New York City and North Carolina.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal interventions of infectious disease

Sun

Liu

2023

Naval Research Logistics

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The recent outbreak of novel coronavirus has highlighted the need for a benefit‐cost framework to guide unconventional public health interventions aimed at reducing close contact between infected and susceptible individuals. In this paper, we propose an optimal control problem for an infectious disease model, wherein the social planner can control the transmission rate by implementing or lifting lockdown measures. The objective is to minimize total costs, which comprise infection costs, as well as fixed and variable costs associated with lockdown measures. We establish conditions concerning model primitives that guarantee the existence of a straightforward optimal policy. The policy specifies two switching points , whereby the social planner institutes a lockdown when the percentage of infected individuals exceeds , and reopens the economy when the percentage of infected individuals drops below . We subsequently extend the model to cases where the social planner may implement multiple lockdown levels. Finally, numerical studies are conducted to gain additional insights into the value of these controls.

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Hospital admission, facility‐based isolation, and social distancing: An SEIR model with constrained medical resources

Cited by 10 publications

References 30 publications

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

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

Managing pandemics: A POM perspective and directions for future research

Optimal interventions of infectious disease

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