A Study of COVID-19 and Its Impact on Well Being of Society and Business
Bibliographic record
Abstract
Pandemic is the worst situation faced by the world in every century. It not only leads to great human loss but unbearable economic loss also. In order to understand the nature & severity of COVID- 19, emerged in the year 2019 in the Wuhan city of China we have studied the great pandemics occurred in 20th century i.e., influenza outbreak in 1918, 1957 and 1968 1918. The objective of this paper was to understand the severity, mitigating strategies and impact of COVID- 19 on the wellbeing of the society. A review research method was followed to collect the information regarding previous pandemic occurred and the prevailing situation of the society. It is being studied that, alike previous pandemics, this pandemic also leads to great human and economic losses all over the world. Transmission rate was so high that in few weeks it covers a large area under its impact. Similar mathematical model SIR (Susceptible- Infectious- Recovered) of transmission used in 20th century was used to understand the transmission process of COVID- 19. As far as mitigating strategies are concerned, it is being observed that similar strategies like travel restrictions, social distancing, home quarantine, school & workplace closure which were used to control influenza pandemics were used to control the current situation. Recommendations were made on the basis of steps taken by the government in order to help the society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".