Forecasting of COVID-19 infections in E7 countries and proposing some policies based on the Stringency Index
Bibliographic record
Abstract
COVID-19 infection data of Emerging 7 (E7) countries, namely Brazil, China, India, Indonesia, Mexico, Russia, and Turkey were described by an empirical model or a special case of this empirical model. Near-future forecasts were also performed. Moreover, the causalities between the Stringency Index’s indicators and total cases in E7 countries in COVID-19 period were examined. Countries were grouped as “stationary,” “transition,” and “exponential” based on the data and model fits. The proposed models produced good fits to the COVID-19 data of E7 countries and it was possible to predict the number of cases in the near future. Some policies to control total cases in E7 countries were also proposed in the final phase of this study based on the findings and forecasting in these countries.
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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) | 0.000 | 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) | 0.000 | 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".