The Impact of Quarantine, Isolation, and Social Distancing on COVID-19 Prevention: A Systematic Review
Bibliographic record
Abstract
Background: The new Corona virus disease (COVID-19) appeared in Wuhan, China in December 2019. Methods, such as quarantine, isolation, and social distancing, if implemented properly, can help prevent the transmission of the disease. This study aimed to examine the effects of quarantine, isolation, and social distancing on the prevention of COVID-19. Methods: In this systematic review, EMBASE (Elsevier, 2018), MEDLINE (National Library of Medicine, 2018), Scopus, ProQuest, Web of Science (Clarivate Analytics, 2018b), and Google Scholar databases were searched for the studies published prior to 10 April 2020. The search and data extraction were conducted by two authors and to check and control the quality of the articles, we used the Newcastle-Ottawa checklist. Results: Based on the inclusion criteria, 24 out of the 768 primarily screened studies were finally assessed. Studies showed that the short-term negative psychological effects of quarantine included frustration, boredom, anger, and confusion. Nonetheless, extending the adult quarantine period to 18-21 days could be effective in preventing the spread of the virus and controlling the disease. Moreover, the decision to control the people’s travels through restrictions on freedom of movement must be balanced regarding the estimated epidemiological impact and the expected economic outcome. Conclusions: Although isolation, quarantine, and social distancing all have challenges, they are very useful methods for controlling the disease, which can be best used by knowing their duration of implementation.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".