A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis
Bibliographic record
Abstract
COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool's predictions and illustrate how it can support managers in their daily decisions concerning the system's capacity and ensure patients the access the resources they require.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".