International Public Health Responses to COVID-19 Outbreak: A Rapid Review.
Bibliographic record
Abstract
BACKGROUND: The outbreak of Coronavirus disease 2019 (COVID-19) has posed a significant threat to many countries. Since the disease does not currently have a particular treatment, there is a compelling need to find substitute means to dominate its expansion. In this rapid review, we aimed to determine some countries' public responses to the COVID-19 epidemic. METHODS: In this study, academic databases, including MEDLINE, Scopus, and Embase, were investigated. The keywords applied in the search strategy besides the names of each country were: "Public Health," "Public Response", "Health Policy", "COVID-19", "Novel Coronavirus," "2019-nCoV", and "SARS-CoV-2". The countries included China, Italy, Iran, Spain, South Korea, Germany, France, United States, Australia, Canada, Japan, and Singapore. RESULTS: The total number of retrieved articles in MEDLINE, Scopus, and Embase in April 2020 was 594, and after removing 259 duplicate articles, 335 papers were screened by the experts. After this investigation, 50 articles, in addition to 12 webpages, were extensively reviewed for the results section. Public health strategies and responses can be divided into four main areas, including monitoring, public education, crowd controlling, and care facilities. CONCLUSION: According to the results of the management decisions of some governments on quarantining, social isolation, screening methods, and flight suspensions due to the severity and anonymity of COVID-19, it is highly assured that these strategies would be the most successful approaches to confront the present pandemic. Governments should put in place timely and strict measures to halt the spread and diminish its unintended deadly consequences.
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 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".