Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic
Authors: Ralph H. Stern
Journal: Annals of Internal Medicine (2020)

Letters20 October 2020Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 PandemicFREERalph H. Stern, PhD, MDRalph H. Stern, PhD, MDUniversity of Michigan, Ann Arbor, Michigan (R.H.S.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L20-1061 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Weissman and colleagues (1) provide a sophisticated interface to an underlying compartmental epidemiologic model. Users should be aware that the assumptions of the standard susceptible, infected, removed (SIR) model are not met during the coronavirus disease 2019 (COVID-19) pandemic. Compartmental models assume that every individual in a compartment has the same characteristics. However, with a 6-day doubling time and a 14-day duration of infectiousness, the cohort structure of the infectious compartment needs to be accounted for. For every patient on day 14 of their infection, there are approximately 4.5 patients on day 1; as such, only approximately 1/33 of the cases in the infectious compartment next transitions to the removed compartment—not the 1/14 used in a standard SIR model. The discrepancy becomes larger for shorter doubling times; for example, only 1/55 of the infectious compartment next transitions for a 4-day doubling time.The cohort structure can be addressed by substituting a first-in, first-out process for the first-order elimination of the standard SIR model. A standard SIR model simulation based on information provided in Weissman and colleagues' article and the COVID-19 Hospital Impact Model for Epidemics (CHIME) website (2) produces dynamics of the COVID-19 pandemic in the Philadelphia region replicating those depicted in the Supplement figures in their article. In addition, the timing and magnitude of the peak of infectious cases (day 61.5; n = 1 095 280) are similar to those calculated there (day 63; n = 1 108 453 after accounting for hospital share). However, when a matched simulation substituting a first-in, first-out SIR model based on delay differential equations is performed, the peak of infectious cases is higher, earlier, and narrower. It takes place on day 51.5, with 2 044 540 infectious cases.Standard SIR models that do not account for cohort structure underestimate the peak of infectious cases of COVID-19 and their timing, a conclusion also reached by Grant in a recent publication (3).References1. Weissman GE, Crane-Droesch A, Chivers C, et al. Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic. Ann Intern Med. 2020;173:21-8. [PMID: 32259197]. doi:10.7326/M20-1260 LinkGoogle Scholar2. University of Pennsylvania. COVID-19 Hospital Impact Model for Epidemics (CHIME). 2020. Accessed at http://penn-chime.phl.io on 8 April 2020. Google Scholar3. Grant A. Dynamics of COVID-19 epidemics: SEIR models underestimate peak infection rates and overestimate epidemic duration. medRxiv. Preprint posted online 12 April 2020. doi:10.1101/2020.04.02.20050674 Google Scholar Comments 0 Comments Sign In to Submit A Comment Author, Article, and Disclosure InformationAuthors: Ralph H. Stern, PhD, MDAffiliations: University of Michigan, Ann Arbor, Michigan (R.H.S.)Disclosures: The author has disclosed no conflicts of interest. The form can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L20-1061. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoLocally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic Gary E. Weissman , Andrew Crane-Droesch , Corey Chivers , ThaiBinh Luong , Asaf Hanish , Michael Z. Levy , Jason Lubken , Michael Becker , Michael E. Draugelis , George L. Anesi , Patrick J. Brennan , Jason D. Christie , C. William Hanson III , Mark E. Mikkelsen , and Scott D. Halpern Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic Gary E. Weissman , Andrew Crane-Droesch , Corey Chivers , Mark E. Mikkelsen , and Scott D. Halpern Metrics Cited byA multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning—A Scoping ReviewA Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 PandemicPrediction of hospital bed capacity during the COVID− 19 pandemicCOVID-19 Community Transmission among Healthcare Workers at a Tertiary Care Cardiac CenterCOVID19-Routes: A Safe Pedestrian Navigation Service 20 October 2020Volume 173, Issue 8 Page: 679-680 Keywords Conflicts of interest COVID-19 Disclosure Epidemiology Infectious diseases ePublished: 20 October 2020 Issue Published: 20 October 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...