Estimating Optimum Length of Stay in a Hospital to Control the Infection Spread during an Epidemic Using Left-Right Truncated Poisson Model
Bibliographic record
Abstract
Background: The unprecedented havoc of COVID-19 pandemic stressed medical infrastructure of every affected country. The developing countries were more affected as their already inadequate medical resources were strained further. Material and Methods: In order to estimate the time of onset of recovery through the period of hospitalisation stay, the retrospective data on the number of days that 83 COVID-19 patients stayed in a hospital in New Delhi, India was obtained. A Left-Right Truncated Poisson Distribution Model (LRTPD) was developed to estimate the average number of days that patients had to spend in the hospital before the onset of recovery and they were no longer infected. Left truncation is on the ‘u’ left most classes of the random variable and right truncation is after ‘v’ classes. The parametric estimates of the LOS were validated using the Monte-Carlo method. Results and Conclusion: The models suggested that if appropriate truncation limits (both the data-specific and as per expert advice) are used in case of critical medical emergencies, approximately 90 percent of the patients will be able to get hospital admission, without over-burdening the hospital infrastructure. The median recovery onset time/ Length of stay (LOS) obtained using the Kaplan-Meier estimator was consistent with the results of the parametric modeling and simulation. However, the Kaplan-Meier method overestimated the mean LOS as compared to the parametric methods.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".