ArticleAnnals of internal medicine2020
Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.
Article in Annals of internal medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 2 of them syntheses that pooled it.
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Who cites it
100 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic literature review on public health and healthcare resources for pandemic preparedness planning.BMC public health · 2024Pooled it
- COVID-19 length of hospital stay: a systematic review and data synthesis.BMC medicine · 2020Pooled it
- Respiratory pathogen epidemiology in the post-pandemic era: a multicenter retrospective study from Saudi Arabia.BMC infectious diseases · 2026Article
- Dynamic demand forecasting and capacity assessment for emergency care system: a hybrid time series and machine learning approach with evidence from Beijing.BMC health services research · 2026Article
- Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.Health care management science · 2026Article
- Infection risk assessment for socially structured population using stochastic microexposure model.Journal of exposure science & environmental epidemiology · 2026Article
- Divergent surveillance needs and resource allocation for COVID-19 and influenza: insights from a community-based syndromic surveillance study in Shanghai (2024-2025).Frontiers in public health · 2026Article
- Optimizing Emergency Response in Hospitals: A Systematic Review of Surge Capacity Planning and Crisis Resource Management.Healthcare (Basel, Switzerland) · 2025Review
- A rubric for assessing conformance to the Ten Rules for credible practice of modeling and simulation in healthcare.PloS one · 2025Article
- Mechanical Ventilator Acquisition Strategy in a Large Private Tertiary Medical Center Using Monte Carlo Simulation.Acta medica Philippina · 2025Article
- When are predictions useful? A new method for evaluating epidemic forecasts.BMC global and public health · 2024Article
- Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model.Infectious Disease Modelling · 2024Article
- Strategic Optimization of Patient Flow and Staffing Schemes during the COVID-19 Pandemic through Operations Management in a Neonatal Intensive Care Unit.Acta medica Philippina · 2024Article
- Hospital capacities and response to COVID-19 pandemic surges in Iran: A quantitative model-based study.Journal of education and health promotion · 2024Article
- A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis.Healthcare analytics (New York, N.Y.) · 2023Article
- Remote patient monitoring for COVID-19 patients: comparisons and framework for reporting.BMC health services research · 2023Review
- A queuing model for ventilator capacity management during the COVID-19 pandemic.Health care management science · 2023Article
- Factors Associated with 28-day Critical Illness Development During the First Wave of COVID-19.Infectious diseases & clinical microbiology · 2023Article
- Health Care Logistics Network Design and Analysis on Pandemic Outbreaks: Insights From COVID-19.Transportation research record · 2023Article
- Simultaneous response to multiple disasters: Integrated planning for pandemics and large-scale earthquakes.International journal of disaster risk reduction : IJDRR · 2023Article
40 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors.
Funding
Abstract
backgroundThe coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.
objectiveTo 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.
designMonte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.
setting3 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.
resultsUsing 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.
conclusionPublicly 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.