ESTIMATING COVID-19 INFECTIONS IN HOSPITAL WORKERS IN THE UNITED STATES
Bibliographic record
Abstract
ABSTRACT Objective We estimated that how many hospital workers in the United States (US) might get infected or die in the COVID-19 pandemic. We also estimated the impact of personal protective equipment (PPE) and age restrictions on these estimates. Methods Our secondary analyses estimated hospital worker infections in the US based on health worker infection and death rates per 100 deaths from COVID-19 in Hubei and Italy. We used Monte Carlo simulations to compute point estimates with 95% confidence intervals for hospital worker infections in the US based on the two scenarios. We computed potential decrease in infections if the PPE were available only to those involved in direct care of COVID-19 patients (∼ 30%) and if workers aged ≥ 60 years are restricted from patient care. Estimates were adjusted for hospital workers per bed in the US compared to China and Italy. Results The hospital worker infections per 100 deaths were 108.2 in Hubei and 94.1 in Italy. Based on Hubei scenario, we estimated that about 53,640 US hospital workers (95% CI: 43,160 to 62,251) might get infected from COVID-19. The Italian scenario suggested 53,097 US hospital worker (95% CI: 37,133 to 69,003) might get infected during the pandemic. Availability of PPE to high-risk workers could reduce counts to 28,100 (95% CI: 23,048 to 33,242) considering Hubei and to 28,354 (95% CI: 19,829 to 36,848) considering Italy. Restricting hospital workers aged ≥ 60 years from direct patient care reduced counts to 1,985 (95% CI: 1,627 to 2,347) considering Hubei and to 2,002 (95% CI: 1,400 to 2,602) considering the Italian scenario. Conclusion We estimated significant burden of illness due to COVID-19 if no strategies are adopted. Making PPE available to all hospital workers and reducing exposure of hospital workers above the age of 60 could have significant reductions in hospital worker infections. VISUAL ABSTRACT Figure 1. Estimated number of COVID-19 related infections among healthcare workers in the United States based on Hubei and Italian scenarios
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".