A STUDY ON FORECASTING THE IMPACT OF COVID-19 ON EMERGENCY SERVICE IN A PUBLIC HOSPITAL
Bibliographic record
Abstract
The COVID-19 pandemic has seriously threatened human life all over the world since the first quarter of 2020. Hospitals have fought on the frontlines against this threat. The aim of this study is to predict the number of monthly emergency service patients for a public hospital. In particular, the impact of the COVID-19 pandemic on the number of emergency service patients was examined. While the data set for the period January 2012- June 2021 (114 months) is used in the analyses, two different data sets were created for the Box- Jenkins (B-J) and Gray Prediction approaches. Then, the number of monthly emergency service patients was predicted using the SARIMA model, GM (1,1) and TGM. In the analyses, while examining the long-term trend of the number emergency services patients’ using the SARIMA model, GM (1,1) and TGM were used to focus on the COVID-19 period. The findings suggest that the TGM has the most successful results in terms of evaluation criteria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".