Potential magnitude of COVID-19-induced healthcare resource depletion in Ontario, Canada
Barrett, K.; Khan, Y. A.; Mac, S.; Ximenes, R.; Naimark, D. M.; Sander, B.
Show abstract
BackgroundThe global spread of coronavirus disease 2019 (COVID-19) continues in several jurisdictions, causing significant strain to healthcare systems. The purpose of our study is to predict the impact of the COVID-19 pandemic on patient outcomes and the healthcare system in Ontario, Canada. MethodsWe developed an individual-level simulation to model the flow of COVID-19 patients through the Ontario healthcare system. We simulated different combined scenarios of epidemic trajectory and healthcare capacity. Outcomes include numbers of patients needing admission to the ward, Intensive Care Unit (ICU), and requiring ventilation; days to resource depletion; and numbers of patients awaiting resources and deaths associated with limited access to resources. FindingsWe demonstrate that with effective early public health measures system resources need not be depleted. For scenarios considering late or ineffective implementation of physical distancing, health system resources would be depleted within 14-26 days. Resource depletion was also avoided or delayed with aggressive measures to rapidly increase ICU, ventilator, and acute care hospital capacity. InterpretationWe found that without aggressive physical distancing measures the Ontario healthcare system would have been inadequately equipped to manage the expected number of patients with COVID-19, despite the rapid capacity increase. This overall lack of resources would have led to an increase in mortality. By slowing the spread of the disease via ongoing public health measures and having increased healthcare capacity, Ontario may have avoided catastrophic stresses to its health care system.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Preventing a cluster from becoming a new wave in settings with zero community COVID-19 cases 92%
- Modelling testing and response strategies for COVID-19 outbreaks in remote Australian Aboriginal communities 91%
- Testing and Vaccination to Reduce the Impact of COVID-19 in Nursing Homes: An Agent-Based Approach 90%
Similar papers in this journal
- A Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered Model to Predict Demand for COVID-19 Inpatient Care in a Large Healthcare System 92%
- The Impact of Vaccination to Control COVID-19 Burden in the United States: A Simulation Modeling Approach 91%
- Threshold analyses on rates of testing, transmission, and contact for COVID-19 control in a university setting 91%
Similar papers in this journal
- 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 94%
- Efficacy of “stay-at-home” policy and transmission of COVID-19 in Toronto, Canada: a mathematical modeling study 91%
- Analyzing Supply and Demand on a General Internal Medicine Ward: A Cross-Sectional Study 90%
Similar papers in this journal
- Estimating COVID-19 cases and deaths prevented by non-pharmaceutical interventions in 2020-2021, and the impact of individual actions: a retrospective model-based analysis 94%
- Using an Agent-Based Model to Assess K-12 School Reopenings Under Different COVID-19 Spread Scenarios – United States, School Year 2020/21 92%
- Estimating the impact of test-trace-isolate-quarantine systems on SARS-CoV-2 transmission in Australia 91%
Similar papers in this journal
- Mathematical modeling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada 91%
- The mobility gap: estimating mobility levels required to control Canada’s winter COVID-19 surge 89%
- Impact of Population Mixing Between a Vaccinated Majority and Unvaccinated Minority on Disease Dynamics: Implications for SARS-CoV-2 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.