COVID-19 Influence: A General Analysis using Machine Learning Methods
Bibliographic record
Abstract
In this paper, we mainly investigate how COVID-19 affects society in multiple ways, such as infected, death, and recovered population, air and ground transportation, and unemployment rate in specific countries. This is important to study since it can help the government create a specific policy to minimize the negative influence of COVID-19 on our society as a whole. Recent works focus on the prevention of COVID-19 and the efficiency of specific immunization. However, the influence of COVID-19 in a specific area of society, such as transportation, has not been paid enough attention to. We first used K-means to find similarities between countries, then we grouped all nations by their continents and used bar plots to visualize how each continent performs. Then, we used matplotlib to visualize the influence of pandemics on air transportation and ground transportation of the U.S. Finally. We create an interactive visualization that investigates the unemployment rate in specific countries, such as China, the United States, Japan, the United Kingdom, and Canada, during the pandemic period. Our methods show all those continents, including Africa, Asia, Australia/Oceania, Europe, North America, and South America, can be divided into five clusters according to the test percentage, and South America represents the highest death percentage. Europe has both the highest test and infected percentage. For both air transportation and inbound ground transportation, revenue and numbers of transportation dropped dramatically at the beginning of April 2020, which is the outbreak of COVID-19 in the U.S. Similarly, the unemployment rate for North America suddenly boost.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".