Prediction of Size of the COVID-19 Pandemic Using Wavelength Models: Cases of Turkey and World
Bibliographic record
Abstract
Abstract The main purpose of the study is to predict the magnitude of the Covid-19 pandemic by using epidemiological wavelength models in Turkey and at international level. Therefore, firstly, the first 36 days of wavelengths based on the number of daily coronavirus cases in Turkey were calculated. In addition, 114 countries were compared in terms of Covid-19 wavelengths considering the cumulative number of the pandemic cases occured at the end of the first 36 days for evaluation on an equal plane. In the last part of the study, the wavelengths of 185 countries were examined comparatively based on the cumulative number of cases at the end of the time frame from the first epidemic case until 2020-04-16 (including that date). According to the findings of wavelength obtained in Turkey, it was observed that case wavelength on 2020-04-11, death and recovered case wavelength on 2020-04-16, and net wavelength on 2020-03-26 reached its peak. China was the country having the highest wavelength of case, death, and recovered case wavelengths in 114 countries at the end of the first 36 days since the first case occurred. In that country, wavelengths of case, death and recovered case were 33.6, 23.5 and 30.7, respectively. The first three countries with the highest net wavelength at the end of the first 36 days were Serbia (36.5), Netherlands (33.5) and Portugal (30.3), respectively. On the other hand, the country having the highest case and death wavelengths among 185 countries in the time interval from the first case until the date of 2020-04-16 (including that date) was the USA, and case and death wavelengths were 39.7 and 30.7, respectively. The country with the highest recovered case wavelength was China (33.3). The first 3 countries with the highest wavelengths are Canada (51.4), England (45.0) and Serbia (39.2), respectively.
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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 | 0.026 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.005 |
| 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".