Survey of COVID-19 Prediction Models and Their Limitations
Bibliographic record
Abstract
COVID-19 pandemic has been spreading globally and has been influencing the daily life of human beings in addition to the economies of most countries around the globe. Early and accurate detection of COVID-19 coronavirus is crucial to prevent and control its outbreak using medical treatment and timely quarantine. The daily massive increases in the cases of COVID-19 patients worldwide and the limited solutions of the available diagnosing techniques have resulted in difficulties in pointing out the presence of the disease. Wherefore, the necessity arises to find other alternatives by leveraging the artificial intelligence (AI) models which create intelligent entities that have demonstrated themselves particularly successful due to their spectacular innovations in video processing and image, in addition to their highly accurate projection models. This survey contributes to studying the state of the art of the AI models that have been fighting against the COVID-19, highlighting the limitations that are significant and present noteworthy barriers to struggle with a pandemic, and recommends the trends for the incoming research on the pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".