Examining job tenure and lost-time claim rates in Ontario, Canada, over a 10-year period, 1999–2008
Bibliographic record
Abstract
<h3>Abstract</h3> <h3>Background</h3> In an effort to contain the COVID-19 epidemic, many governments across the world have enforced lockdown or social distancing measures. Several outbreak models have been developed to investigate the effects of different public health strategies for COVID-19, but they have not been developed for Pakistan and other South East Asian countries, where a large proportion of global population resides. <h3>Methods</h3> We developed a stochastic individual contact model by extending the widely-used Susceptible-Infectious-Recovered (SIR) compartment model with additional compartments to model both anticipated mitigating effects of public health intervention strategies for Pakistan. We estimated the projected spread, number of hospitalizations, and case fatalities under no intervention and four increasingly stringent public health strategies of social distancing and self-isolation at the national and provincial levels of Pakistan. <h3>Results</h3> Our analysis shows that without any public health interventions the expected number of cumulative case fatalities is 671,596 in Pakistan with the virus is expected to peak in terms of the number of required ICU-hospitalizations at 198,593 persons by the end of the June 2020. The estimated total numbers of cumulative case fatalities are lower for other public health strategies with strict social distancing showing the lowest number of deaths at 1,588 (Self-isolation: n=341,359; Flexible social distancing strategy: n=3,995; and Exit strategy: n=28,214). The lowest number of required ICU-hospitalization is also estimated for strict social distancing strategy (n=266 persons at the end of May 2020). Generally, the simulated effects of the different public health strategies at the provincial-level were similar to the national-level with strict social distancing showing the fewest number of case fatalities and ICU-hospitalizations. <h3>Conclusion</h3> Our results indicate that case fatalities and ICU-hospitalizations for Pakistan will be high without any public health interventions. While strict social distancing can potentially prevent a large number of deaths and ICU-hospitalizations, the government faces an important dilemma of potentially severe economic downfall. Consideration of a temporary strict social distancing strategy with gradual return of the lower-risk Pakistani population, as simulated in our exit strategy scenario, may an effective compromise between public health and economy of Pakistani population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".