Mathematical Model and Data Analysis to Determine the Number of Confirmed Infections Due to Covid-19 in Spain
Bibliographic record
Abstract
Covid-19 initially started in China, although cases of infection by this virus are currently being identified in Europe since January and February of this year camouflaged within a strong outbreak of influenza that had not been identified before. What is certain is that in about a hundred days it has spread around the world threatening humanity. There seems to be a great need to find a rapid response to the speed at which the virus is spreading. In this work, different mathematical models are studied to accurately determine the speed of propagation or infection of people infected by Covid-19 based on data collected from the evolution of the pandemic in Spain. Several mathematical models are proposed and analyzed, but the model proposed as the most suitable is a fourth degree polynomial regression adjustment that presents an R-square statistic of 99.72% which gives a great adjustment of the model for the calculation of the number of infected confirmed by this virus in Spain. Knowing these data is of vital importance to be able to take and undertake the most urgent health and social measures in an effective and orderly manner. This will have a great repercussion in being able to avoid a high number of possible infections.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".