New Epidemiological Model Suggestions Revealing Size of Epidemics Based on the COVID-19 Pandemic Example: Wavelength Models
Bibliographic record
Abstract
Abstract The main purpose of the study is to introduce the wavelength models developed to measure the size of outbreaks based on the COVID-19 example. In this way, the wavelengths of the outbreaks can be calculated, ensuring that the outbreaks are valid, reliable and easy to follow at the national and international level. Wavelength models consist of approved case, death, recovered case and net wavelength models. Thus, the size of the outbreak can be measured both individually and as a whole. COVID-19 cases of 181 countries were used to demonstrate the application of the models. The prominent findings in the applied wavelength models are as follows: the countries with the highest case wavelength are USA, Italy, Spain and Germany, respectively. However, Italy ranks first in the death wavelength, followed by Spain, the USA and France. On the other hand, China has taken the first place in the recovered case wavelength. This country was followed by Spain and Germany and Italy, respectively. Based on all these wavelength models mentioned, net wavelength lengths are calculated. According to the findings of net wavelengths obtained, Canada ranked first, followed by United Kingdom, USA and Italy, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".