PREDICTION OF COVID – 19 EPIDEMIOLOGY USING THE DATA FROM SOCIAL MEDIA - A DESCRIPTIVE BIBLIOGRAPHIC ANALYSIS.:
Bibliographic record
Abstract
Background: The COVID – 19 pandemics has led to many kinds of basic and social research. This has served as a platform for the scientists to extensively mine the social media data and to identify and predict the potential disease hot-spots. Methods: Google scholar and Pubmed search were used to identify the articles which has used social media data to project the hot-spots. Search period window was one month and only open access / free to access articles were included for the study. Standardised search terms were used by a team of two researchers. Results: A total of 15 articles were selected and screened for the inclusion criteria. Then 12 articles which met the inclusion criteria were selected for this study. The highest read and cited articles were published from USA and China respectively. Even though Canada has been acknowledged as the country with highest social media usage the research with such data has to be given some impetus. Conclusion: Usage of social media data for predicting caseloads can significantly reduce the morbidity and mortality due to COVID – 19 which is relevant in these times of minimal digital divide around the globe.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.058 | 0.046 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".