A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has created a global crisis and the governments are fighting rigorously to control the spread by imposing intervention measures and increasing the medical facilities. In order to tackle the crisis effectively we need to know the trajectories of number of the people infected (i.e. confirmed cases). Such information is crucial to government agencies for developing effective preparedness plans and strategies. We used a statistical modeling approach – extreme value distributions (EVDs) for projecting the future confirmed cases on a global scale. Using the 69 days data (from January 22, 2020 to March 30, 2020), the EVDs model predicted the number of confirmed cases from March 31, 2020 to April 9, 2020 (validation period) with an absolute percentage error < 15 % and then projected the number of confirmed cases until the end of June 2020. Also, we have quantified the uncertainty in the future projections due to the delay in reporting of the confirmed cases on a global scale. Based on the projections, we found that total confirmed cases would reach around 11.4 million globally by the end of June 2020.The USA may have 2.9 million number of confirmed cases followed by Spain-1.52 million and Italy-1.28 million.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".