Data Visualization and Descriptive Analysis for Understanding Epidemiological Characteristics of COVID-19: A Case Study of a Dataset from January 22, 2020 to March 29, 2020
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
COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) that was reported to spread in people in December 2019. Understanding epidemiological features of COVID-19 is important for the ongoing global efforts to contain the virus. As a complement to the available work, in this article we analyze the Kaggle novel coronavirus dataset of 3397 patients dated from January 22, 2020 to March 29, 2020. We employ semiparametric and nonparametric survival models as well as text mining and data visualization techniques to examine the clinical manifestations and epidemiological features of COVID-19. Our analysis shows that: (i) the median incubation time is about 5 days and older people tend to have a longer incubation period; (ii) the median time for infected people to recover is about 20 days, and the recovery time is significantly associated with age but not gender; (iii) the fatality rate is higher for older infected patients than for younger patients
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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 | 0.011 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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 it