RT Journal Article
SR Electronic
T1 Estimated surge in hospital and intensive care admission because of the coronavirus disease 2019 pandemic in the Greater Toronto Area, Canada: a mathematical modelling study
JF CMAJ Open
JO CMAJ
FD Canadian Medical Association
SP E593
OP E604
DO 10.9778/cmajo.20200093
VO 8
IS 3
A1 Sharmistha Mishra
A1 Linwei Wang
A1 Huiting Ma
A1 Kristy C.Y. Yiu
A1 J. Michael Paterson
A1 Eliane Kim
A1 Michael J. Schull
A1 Victoria Pequegnat
A1 Anthea Lee
A1 Lisa Ishiguro
A1 Eric Coomes
A1 Adrienne Chan
A1 Mark Downing
A1 David Landsman
A1 Sharon Straus
A1 Matthew Muller
YR 2020
UL http://www.cmajopen.ca/content/8/3/E593.abstract
AB Background: In pandemics, local hospitals need to anticipate a surge in health care needs. We examined the modelled surge because of the coronavirus disease 2019 (COVID-19) pandemic that was used to inform the early hospital-level response against cases as they transpired.Methods: To estimate hospital-level surge in March and April 2020, we simulated a range of scenarios of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread in the Greater Toronto Area (GTA), Canada, using the best available data at the time. We applied outputs to hospital-specific data to estimate surge over 6 weeks at 2 hospitals (St. Michael’s Hospital and St. Joseph’s Health Centre). We examined multiple scenarios, wherein the default (R0 = 2.4) resembled the early trajectory (to Mar. 25, 2020), and compared the default model projections with observed COVID-19 admissions in each hospital from Mar. 25 to May 6, 2020.Results: For the hospitals to remain below non-ICU bed capacity, the default pessimistic scenario required a reduction in non-COVID-19 inpatient care by 38% and 28%, respectively, with St. Michael’s Hospital requiring 40 new ICU beds and St. Joseph’s Health Centre reducing its ICU beds for non-COVID-19 care by 6%. The absolute difference between default-projected and observed census of inpatients with COVID-19 at each hospital was less than 20 from Mar. 25 to Apr. 11; projected and observed cases diverged widely thereafter. Uncertainty in local epidemiological features was more influential than uncertainty in clinical severity.Interpretation: Scenario-based analyses were reliable in estimating short-term cases, but would require frequent re-analyses. Distribution of the city’s surge was expected to vary across hospitals, and community-level strategies were key to mitigating each hospital’s surge.