Bibliographic record
Abstract
Rationale - From the year 1 anno Domini until 1855 with the third Plague, major pandemics occurred on average every 348 years. Since then, they have occurred on average every 33 years, with Coronavirus disease 2019 (COVID-19) now underway.Even though current technologies have greatly improved the way of life of human beings, COVID-19, with more than 700 000 000 cases and 6 950 000 deaths worldwide by the end of 2023, reminds us that much remains to be done. Objective - Given the frequency and duration of recent pandemics, it might be wise to start thinking about preventative methods to minimize the impact of future pandemics. This report looks back at 18 months of COVID-19, from March 2020 to August 2021, with the aim of highlighting potential solutions that could prove practical, or even essential, for the future. Material - COVID-19 data, including case and death reports, were extracted daily from the Worldometer platform to build a database for macroscopic analysis of the spread of the virus around the world. Demographic data were integrated into the COVID-19 database for a better understanding of the spatial spread of the SARS-CoV-2 virus in cities/municipalities. Method - Without loss of generality, we only analyzed data from the top 30 (out of 200 and above) countries ranked by total number of COVID-19 cases. Statistics (regression, t-test (p < 0.05), correlation, mean ± std, etc.) were carried out with Excel software. Spectral analysis, using Matlab software, was also used to try to better understand the temporal spread of COVID-19. Results - A good linear correlation was observed between the number of cases and the respective number of deaths depending on the country, i.e. y = 0.0121x + 19559 with R² = 0.8042. The analysis then focused mainly on the number of cases.This study showed that COVID-19 mainly affects G20 countries. The most interesting result is that cities/municipalities with high population density are a powerful activator of the spread of the virus. The current demographic context seems to be becoming a societal problem that must be addressed adequately.Spectral analysis highlighted that the very first months of spread of COVID-19 were the most notable with a strong expansion of the SARS-CoV-2 virus. On the other hand, the following six months showed a certain stability due mainly to multiple preventive measures such as confinement, closure of non-essential services, wearing of masks, distancing of 2 meters, etc. Discussion - Analysis of case and death data showed that COVID-19 mainly affects G20 countries. Nevertheless, the most interesting result of this study is that cities and municipal areas with population densities of several thousand inhabitants per square kilometer largely favored the spread of the SARS-CoV-2 virus. It is believed that such a demographic context is becoming a societal problem that developed countries around the world will sooner or later face and therefore needs to be adequately addressed. Conclusion - COVID-19 has made us understand that it is time to act both preventatively and curatively. Phenomenological insights suggest that the next pandemic could occur in less than 50 years. It may be time to launch new societal projects aimed at relieving congestion in densely populated regions.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".