Data Fixing by Data Fitting: Estimating the Unreported Cases During the Early COVID-19 Outbreak in Hubei, China
Bibliographic record
Abstract
On February 13, 2020, the Health Commission of Hubei Province changed the definition of confirmed cases, resulting in a reported daily case number that is significantly larger than on other dates. Such abnormal data points pose a challenge in data fitting and parameter estimation. To address this, we derive a simple formula from the classical Kermack-McKendrick model and introduce a new quantity to capture the number of unreported cases hidden in the data. We then use this new formula to fit the inconsistent data and estimate key epidemic parameters. Based on the reported cumulative case numbers until February 21, 2020, we estimate that the unreported case number in Hubei is 60856 (95% CI: [33513, 91206]), while the unreported case number in Wuhan is estimated as 29374 (95% CI: [18205, 40665]). The peak times in Hubei and Wuhan are February 6, 2020, and February 8, 2020, respectively. The basic reproduction numbers are 2.334 (95% CI: [2.053, 2.711]) for Hubei and 2.189 (95%CI: [1.992, 2.448]) for Wuhan.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".