Seasonality and Progression of COVID-19 among Countries With or Without Lock-downs.
Bibliographic record
Abstract
Early predictions by computer simulation of 7 billion infections and 40 million deaths by COVID-19 during 2020 alone if lock-downs and other confining measures were not enforced may have justified restrictive policies mandated by governments of 165 countries. The objective of the present study was to determine differences between the infection and death rate in countries that established early, nation-wide curfews, state-at-home orders, or lock-downs versus countries that did not mandated any lock-downs to deal with the COVID-19 crisis. The analyzed epidemiological data indicates that lock-downs, and other confining measures had no effect on the chances of healthy individuals becoming infected with- ir dying off SARS-CoV-2. The highest incidence of COVID-19 infection progressed from countries in northern latitudes, where it was winter at the beginning of the pandemic, to countries in the southern hemisphere in July 21, 2020 were winter was starting.This trend reversed again during the last quarter of 2020. A considerable (4-fold) increase in COVID-19 infection rate is observed between fall and beginning of winter in countries in the southern hemisphere. This seasonal progression correlates with the variation in the germicidal solar flux received by these countries, suggesting that infectious virus in the environment plays a role in the evolution of COVID-19. In addition, hypotheses are presented that could explain the recurrent new spikes of COVID-19 as well as the mortality of SARS-Co V-2 observed in some developed countries higher than the mortality rate reported in several developing countries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".