Modeling and Forecasting for the number of cases of the COVID-19 pandemic with the Curve Estimation Models, the Box-Jenkins and Exponential Smoothing Methods
Bibliographic record
Abstract
Objectives: This study aims to provide statistical information summarizing the general structure about the effects and process of infection in all countries of the world in the light of the data obtained and to model the daily change of infection criteria. Methods: The number of COVID 19 epidemic cases of Turkey and the selected G8 countries, Germany, United Kingdom, France, Italy, Russia, Canada, Japan between 1/22/2020 and 3/22/2020 has been estimated and forecasted in this study by using some curve estimation models, Box-Jenkins (ARIMA) and Brown/Holt linear exponential smoothing methods. Results: Japan (Holt Model), Germany (ARIMA (1,4.0)) and France (ARIMA (0,1,3)) provide statistically significant but clinically unqualified results in this data set. UK (Holt Model), Canada (Holt Model), Italy (Holt Model), Turkey (ARIMA (1,4,0)) and Russia?? the results are more reliable. This is specified for the particular model used in this case Turkey. Conclusion: Certainly, more accurate evaluations can be made with more data in future studies. Nevertheless, since this study provides information about the levels at which the number of cases may extend in case that the current situation is not intervened, it can guide countries to take the necessary measures and to intervene it earlier. 
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".