Artificial Intelligence to Model the COVID-19 Country Infection Trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) organized an online repository (available at https://github.com/CSSEGISandData/COVID-19) with world-wide information on the absolute number of new confirmed, recovered, and death cases related to the COVID-19 disease (Coronavirus Disease 2019) caused by the Sars-CoV-2 virus (coronavirus). From the whole dataset, we have focused our analysis on the daily time series summaries, which contain the accumulated numbers of confirmed, death, and recovered cases for each country. Given some countries (e.g., Australia, Canada, and China) were reported at the province/state level, we have aggregated all those into a single time series. Another important modification in this dataset was performed to reorganize the daily records. Instead of using accumulated cases, we calculated the lagged differences between consecutive days. Besides the dataset, we also share our source code designed to cluster time series from different countries with similar behavior. Aiming at reproducing our results, run the source code "tree-clustering.R" Our main contribution is the function "calc.dend.dists" available in "distances-dendrogram.R" . For more information, visit our project https://tsviz.icmc.usp.br/covid19
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.028 |
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 it