Analysis of Data on Socio-Demographic and Clinical Factors of the COVID-19 Coronavirus Epidemic in Spain on Cases of Recovered and Death Cases
Bibliographic record
Abstract
Carrying out a study of socio-demographic and clinical factors to determine which of these are more significant and have a greater influence on the speed of the spread of the virus, taking into account the behaviour of people who have died and been recovered in Spain. The objectives of this study have been to analyze the influence of socio-demographic and clinical factors on the speed of propagation of Covid-19, to determine the most relevant factors and to propose studies determining the prevalence of the disease. The Chi-square model supported by the statistical program Statgraphics Centurion xvi has been used to determine the dependence or not of the different variables studied on the speed of propagation of the virus. In relation to the clinical variables, a cluster study has been carried out to see their dependence. Very relevant conclusions have been obtained from the factor of age in the different analyzed bands, as well as from the little influence of the economic position of the people in the speed of propagation of the virus. The high population density and the areas studied are not always indicative of further spread of the disease A linear function has been determined to link the clinical parameters studied that could be used in subsequent prevalence and seroprevalence studies. The fundamental variables in the study of the coronavirus have been indicated according to socio-demographic and clinical factors. We warn about environmental factors to be studied.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".