COVID-19 pandemic: Current and future implications on science and society
Bibliographic record
Abstract
In 1 st quarter of 21 st century, with the appearance of novel coronavirus, the world is facing a disastrous pandemic of COVID-19 originated from China.The pandemic intensity differs from country to country and the most affected countries are Italy, Spain, France, UK and USA in terms of the mortality ratio while virus is spreading rapidly in more than 180 countries of Europe, Australia, North and South American, Asia and least affecting the African continents.In Pakistan, there have been total 2,389,827 active infections, 1,446,574 recoveries and 280,697 deaths worldwide to date (10/05/2020) which is more than severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS).The outbreak of SARS, affected 8098 individuals with 774 deaths and 9.7% fatality rate while MERS-CoV has 2494 cases, 858 deaths and 34% fatality rate.The COVID-19 epidemic has established anxious condition all over the world which exhibited the adverse effects on physical and mental health of individuals.This review discusses comparative analysis of confirmed cases, number of deaths and highlighting the impact of COVID-19 on daily life, international trade, business, education, transport and global economy.This article summarizes the present state of information and will enhance our knowledge to understand the COVID-19 distinctive features and improve our preventive measures in future.Thus, during this period of great stress, there is requirement of new interdisciplinary methodology with collaboration of sociologists, scholars, epidemiologists, anthropologists, public health experts and virologists to have a change in our activities and behavior to environment in confronting an emergency.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".