Big Data Intelligence Solution for Health Analytics of COVID-19 Data with Spatial Hierarchy
Bibliographic record
Abstract
In the current era of big data, 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 intelligence and computing. In this paper, we propose a big data intelligence solution for health analytics with spatial hierarchy. In particular, we focus on analyzing coronavirus disease 2019 (COVID-19) epidemiological data at different spatial granularity levels. Since its outbreak, there have been cumulatively millions of COVID-19 cases observed in various spatial locations around the world. Our solution focuses on analyzing and mining valuable information and useful knowledge (e.g., distribution, frequency, patterns) of health-related states and characteristics in populations in various spatial locations in a top-down fashion along the spatial hierarchy. To reduce redundancy, our solution discovers and returns to users (e.g., researcher, civilian) new information and knowledge not found at previous spatial hierarchical levels. The discovered information and knowledge helps the users to understand the disease better, and thus take an active role to fight, control, and/or combat the disease. Evaluation of our big data intelligence solution on real-life COVID-19 data demonstrates its practicality in health analytics of the data with spatial hierarchy and in revealing new knowledge about COVID-19 cases at different spatial granularity levels. The solution is expected to be adaptable to health analytics of other diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".