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10.9778/cmajo.20200093

http://scihub22266oqcxt.onion/10.9778/cmajo.20200093
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suck abstract from ncbi


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pmid32963024      CMAJ+Open 2020 ; 8 (3): E593-E604
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  • 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 #MMPMID32963024
  • Mishra S; Wang L; Ma H; Yiu KCY; Paterson JM; Kim E; Schull MJ; Pequegnat V; Lee A; Ishiguro L; Coomes E; Chan A; Downing M; Landsman D; Straus S; Muller M
  • CMAJ Open 2020[Jul]; 8 (3): E593-E604 PMID32963024show ga
  • 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 (R(0) = 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.
  • |COVID-19/diagnosis/*epidemiology/transmission/virology[MESH]
  • |Canada/epidemiology[MESH]
  • |Forecasting/methods[MESH]
  • |Health Services Needs and Demand/trends[MESH]
  • |Hospitalization/*statistics & numerical data[MESH]
  • |Hospitals/*statistics & numerical data/supply & distribution[MESH]
  • |Humans[MESH]
  • |Inpatients/statistics & numerical data[MESH]
  • |Intensive Care Units/*statistics & numerical data[MESH]
  • |Models, Theoretical[MESH]
  • |SARS-CoV-2/genetics[MESH]


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