Analyzing COVID-19 Epidemiological Data
Bibliographic record
Abstract
Technological advancements have made it easy and quick to generate and collect huge volumes of varieties of data from wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by big data science and analysis for social good. In this paper, we propose a solution to analyze coronavirus disease 2019 (COVID-19) epidemiological data. In particular, the solution focuses on analyzing valuable information and useful knowledge (e.g., distribution, frequency, patterns) of health-related states and characteristics in populations. Discovered information and knowledge helps users (e.g., researcher, civilian) to understand the disease better, and thus take an active role in fighting, controlling, and/or combating the disease. Evaluation of our solution on real-life data demonstrates its practicality in analyzing COVID-19 epidemiological data and revealing demographic relationships among COVID-19 cases.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".