Estimating surge in COVID-19 cases, hospital resources and PPE demand with the interactive and locally-informed COVID-19 Health System Capacity Planning Tool
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic revealed an urgent need for analytic tools to help health system leaders plan for surges in hospital capacity. Our objective was to develop a practical and locally informed Tool to help explore the effects of public health interventions on SARS-CoV-2 transmission and create scenarios to project potential surges in hospital admissions and resource demand. Methods: Our Excel-based Tool uses a modified S(usceptible)-E(xposed)-I(nfected)-R(emoved) model with vaccination to simulate the potential spread of COVID-19 cases in the community and subsequent demand for hospitalizations, intensive care unit beds, ventilators, health care workers, and personal protective equipment. With over 40+ customizable parameters, planners can adapt the Tool to their jurisdiction and changes in the pandemic. Results: We showcase the Tool using data for Ontario, Canada. Using healthcare utilization data to fit hospitalizations and ICU cases, we illustrate how public health interventions influenced the COVID-19 reproduction number and case counts. We also demonstrate the Tool's ability to project a potential epidemic trajectory and subsequent demand for hospital resources. Using local data, we built three planning scenarios for Ontario for a 3-month period. Our worst-case scenario accurately projected the surge in critical care demand that overwhelmed hospital capacity in Ontario during Spring 2021. Conclusions: Our Tool can help different levels of health authorities plan their response to the pandemic. The main differentiators between this Tool and other existing tools include its ease of use, ability to build scenarios, and that it provides immediate outcomes that are ready to share with executive decision makers. The Tool is used by provincial health ministries, public health departments, and hospitals to make operational decisions and communicate possible scenarios to the public. The Tool provides educational value for the healthcare community and can be adapted for existing and emerging diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".