Spatial-Temporal Data Science of COVID-19 Data
Bibliographic record
Abstract
Big data are emerging paradigm that can be applied to huge volume of valuable data, which are often generated or collected at a fast velocity from a wide variety of rich data sources. These data can be of a wide variety of formats and/or type; they can be at different levels of veracity. Embedded in these data is implicit, previously unknown and useful information and knowledge that can be discovered by data science. Healthcare and medical data such as epidemiological data for disease like coronavirus disease 2019 (COVID-19) are examples of big data. Analyzing and mining these data led to discovery of knowledge and information about the disease, which in turn help people to get better understanding of the disease so that they could take parts in preventing or slowing down the spread of the disease, and/or protecting themselves from the disease. Hence, in this paper, we present a data science engine to analyze and mine COVID-19 data. As COVID-19 cases may not evenly distributed among spatial locations and/or evenly distributed throughout the entire period of pandemic, our engine conducts spatial-temporal data science to reveal important information and knowledge about epidemiological characteristics of the disease across different spatial locations and its temporal trends. Evaluation on real-life COVID-19 data demonstrates the effectiveness of our engine in conducting spatial-temporal data science of COVID-19 data.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".