SARS-CoV-2: How Science has Advanced in the Era of the COVID-19 Pandemic
Bibliographic record
Abstract
Background : The SARS-CoV-2 (Severe Acute Respiratory Syndrom Corona Virus) virus causes COVID-19 (Corona Virus Disease) disease, which was first diagnosed in late December 2019 among a few people with unknown respiratory illness in Wuhan city, Hubei province, China. Presumably this virus jumped from a natural host to human, and that occurred in one of the open food markets in Wuhan city, spreading very quickly to neighbouring provinces, neighbouring countries and eventually different continents. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January 2020 and a pandemic on 11 March 2020. As of writing, this virus has infected close to 185 million people and killed over 3.97 million people globally. People from all colours and tribes have fallen victim to this virus and the world is struggling to restore pre-pandemic life, which seems far away. While this virus hijacked the freedom of human beings in so many ways, on the other hand SARS CoV-2 also forced us to invent new skills and technology not only to defeat it but also to propel ourselves forward. For instance, diagnostic tests for SARS CoV-2 became available in weeks instead of years, vaccines were produced using newer as well as traditional technology from scratch in a matter of months rather than 10-14 years, a variety of online platforms were adopted and widely used in the past year. While the speed at which science progressed has reached new dimensions, we have experienced many unintended consequences as well since we were forced to focus on SARS CoV-2. In this article a brief update on the history, origin, characteristics of this virus, its epidemiology, transmission, laboratory diagnoses, whole genome sequencing will be given, highlighting the scientific gains driven by the pandemic such as the development of new drugs and repurposing of old drugs, vaccines, prevention measures and infection control.
 Methodology : Title, abstract and text of relevant scientific articles were retireved from PubMed, Goole Scholar and WHO websites from 1974 to 2021.
 Conclusion : Finally, unintended consequences, post COVID-19 issues, myths and superstitions and adoption of technological development and new innovations will be discussed.
 IAHS Medical Journal Vol 4(2), June 2021; 63-73
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 teacher head, 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".