Insights into the dynamics of SARS-CoV-2 pandemicvia Shannon-Fisher causality plane
Bibliographic record
Abstract
Abstract This paper performs a systematic investigation into the temporal evolution of severe acute respiratory disease coronavirus 2 (SARS-CoV-2) pandemic considering 15 diverse countries. Based on the foundations of Information Theory, we apply the Shannon-Fisher causality plane (SFCP), to map the dynamics behavior inherent to SARS-CoV-2 and their respective locations along the (SFCP). Our results show that this dynamics varies widely along the SFCP from the lower-right region, characterized by high entropy and low degree of reliability in relation to the information extracted from the analyzed data set to the top-right region, characterized by the less entropic and high degree of reliability in relation to the information extracted from the analyzed data set. It reveals that we have three different groups of countries in controlling the SARS-CoV-2 pandemic. A country that was proactive in implementing measures such as social distancing, quarantine, orders to stay at home, testing symptomatic and asymptomatic loads and hygienic measures to limit the impacts of SARS-Cov-2 (China) and that today is clearly in the decay phase with the number of cases tending to zero and is no longer in a pandemic situation (efficient). Moderately proactive countries, ie, implemented measures only when the spread of SARS-Cov-2 was already reaching the country (France, Germany, United Kingdom, Spain, Sweden, Italy, Ireland, USA, Austria, and Canada) (moderately efficient) and the reactive countries, which took a long time to implement the measures and/or the infection came later and as a result are not managing to reduce the number of daily cases of SARS-CoV-2 (Russia, Iran, Brazil, and India) (inefficient) and are the new epicenters of the SARS-COV-2 pandemic. Besides, we applied the Bandt Pompe permutation entropy (H) and the Fisher Information (F) to obtain the rank of the most efficient countries to the fight against the SARS-CoV-2. To the best of our knowledge, no researches have been ranking the most proactive countries in the fight against the SARS-CoV-2 dissemination. We truly believe that the empirical results showed in this research draws new perspectives that can collaborate in the formulation of more efficient healthy public policies to combat SARS-CoV-2 spread.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".