Impact of Vaccination on COVID-19 Spread in Real Time: Visualization and Analysis Tool
Bibliographic record
Abstract
Coronaviruses have been around for years, they are a large family of viruses that can create a variety of anomaly in humans and even in animals, the first symptoms are summed up by a simple cold with fever but it can spread to very serious respiratory problems. This disease has caused a global crisis on all levels; it's a very big challenge that we have lived it since the Second World War. The challenging problem of COVID-19 data science is considered in this paper, where we propose a new data warhouse, that best meets the needs of scientists. The proposed data warhouse as of February 24, 2020, is based on heterogeneous data provided by Our World in Data GitHub and Kaggle database, which are collected daily from Our World in Data COVID-19. Furthermore, this data warehouse is used to feed dashboards in real time that helps the decision-makers to strengthening of the coronavirus screening network, track the spread of the virus before and after vaccination around the world to fight against this dangerous disease.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".