About: Estimated surge in hospitalization and intensive care due to the novel coronavirus pandemic in the Greater Toronto Area, Canada: a mathematical modeling study with application at two local area hospitals

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Estimated surge in hospitalization and intensive care due to the novel coronavirus pandemic in the Greater Toronto Area, Canada: a mathematical modeling study with application at two local area hospitals Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Estimated surge in hospitalization and intensive care due to the novel coronavirus pandemic in the Greater Toronto Area, Canada: a mathematical modeling study with application at two local area hospitals
Creator	Chan, Adrienne Schull, Michael Mishra, Sharmistha Li, Ka Muller, Matthew Straus, Sharon Wang, Linwei Landsman, David Paterson, J Coomes, Eric Ma, Huiting Downing, Mark Floor, Rd Ishiguro, Lisa Kim, Eliane Lee, Anthea Pequegnat, Victoria Yiu, Kristy
Source	MedRxiv
abstract	Background: A hospital-level pandemic response involves anticipating local surge in healthcare needs. Methods: We developed a mechanistic transmission model to simulate a range of scenarios of COVID-19 spread in the Greater Toronto Area. We estimated healthcare needs against 2019 daily admissions using healthcare administrative data, and applied outputs to hospital-specific data on catchment, capacity, and baseline non-COVID admissions to estimate potential surge by day 90 at two hospitals (St. Michaels Hospital [SMH] and St. Josephs Health Centre [SJHC]). We examined fast/large, default, and slow/small epidemics, wherein the default scenario (R0 2.4) resembled the early trajectory in the GTA. Results: Without further interventions, even a slow/small epidemic exceeded the citys daily ICU capacity for patients without COVID-19. In a pessimistic default scenario, for SMH and SJHC to remain below their non-ICU bed capacity, they would need to reduce non-COVID inpatient care by 70% and 58% respectively. SMH would need to create 86 new ICU beds, while SJHC would need to reduce its ICU beds for non-COVID care by 72%. Uncertainty in local epidemiological features was more influential than uncertainty in clinical severity. If physical distancing reduces contacts by 20%, maximizing the diagnostic capacity or syndromic diagnoses at the community-level could avoid a surge at each hospital. Interpretation: As distribution of the citys surge varies across hospitals over time, efforts are needed to plan and redistribute ICU care to where demand is expected. Hospital-level surge is based on community-level transmission, with community-level strategies key to mitigating each hospitals surge. Keywords: COVID-19, pandemic preparedness, mathematical model, transmission model
has issue date	2020-04-23(xsd:dateTime)
bibo:doi	10.1101/2020.04.20.20073023
has license	medrxiv
sha1sum (hex)	abe2ab7d0026748dc8fb66a757f748df7e431ddd
schema:url	https://doi.org/10.1101/2020.04.20.20073023
resource representing a document's title	Estimated surge in hospitalization and intensive care due to the novel coronavirus pandemic in the Greater Toronto Area, Canada: a mathematical modeling study with application at two local area hospitals
resource representing a document's body	covid:abe2ab7d0026748dc8fb66a757f748df7e431ddd#body_text
is schema:about of	named entity 'hospitals' named entity 'application' named entity 'Canada' named entity 'local transmission' named entity 'health-care' named entity 'ICU' named entity 'COVID-19' named entity 'ICU' named entity 'self-isolate' named entity 'novel-coronavirus' named entity 'Canada' named entity 'GTA' named entity 'ICU' named entity 'GTA' named entity 'scripting language' named entity 'Italy' named entity 'ICU' named entity 'epidemic' named entity 'Italy' named entity 'epidemic' named entity 'Hong Kong' named entity 'ICU' named entity 'April 23' named entity 'epidemic' named entity 'intensive care unit' named entity 'epidemic' named entity 'Lombardy' named entity 'ICU' named entity 'medRxiv' named entity 'ICU' named entity 'GTA' named entity 'acute-care' named entity 'ICU' named entity 'ICU' named entity 'GTA' named entity 'COVID-19 in Canada' named entity 'COVID-19' named entity 'ICU' named entity 'Unity Health Toronto' named entity 'epidemic curve' named entity 'epidemic model' named entity 'ICU' named entity 'Infection' named entity 'St. Michael's Hospital' named entity 'health-care' named entity 'ICU' named entity 'black line' named entity 'COVID-19' named entity 'CC-BY-NC-ND 4.0' named entity 'St. Joseph's Health Centre' named entity 'GTA' named entity 'ICU' named entity 'medRxiv' named entity 'GTA' named entity 'medRxiv' named entity 'medRxiv' named entity 'medRxiv' named entity 'epidemic' named entity 'Canada' named entity 'COVID-19' named entity 'medRxiv' named entity 'ICU' named entity 'GTA' named entity 'GTA' named entity 'per-capita' named entity 'ICU' named entity 'China' named entity 'physical distancing'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software