Efectividad de la limitación de la movilidad en la evolución de la pandemia por Covid-19
Bibliographic record
Abstract
Introduction During the Covid-19 pandemic, non-pharmacological interventions (NPIs) aimed to minimise the spread of the virus as much as possible to avoid the most severe cases and the collapse of health systems. These measures included mobility restrictions in several countries, including Spain. Objective To assess the impact of mobility constraints on incidence, transmission, severe cases and mortality in the evolution of the Covid-19 pandemic. These constraints include: • Mandatory home confinement. • - Recommendation to stay at home. • - Perimeter closures for entry and/or exit from established areas. • - Restriction of night-time mobility (curfew). Methodology Systematic literature review, including documents from official bodies, systematic reviews and meta-analyses. The following reference databases were consulted until October 2021 (free and controlled language): Medline, EMBASE, Cochrane Library, TripDB, Epistemonikos, Royal college of London, COVID-end, COVID-19 Evidence Reviews, WHO, ECDC and CDC. Study selection and quality analysis were performed by two independent researchers. References were filtered firstly by title and abstract and secondly by full text in the Covidence tool using a priori inclusion and exclusion criteria. Synthesis of the results was done qualitatively. The quality of the included studies was assessed using the AMSTAR-II tool. Results The literature search identified 642 studies, of which 38 were excluded as duplicates. Of the 604 potentially relevant studies, 12 studies (10 systematic reviews and 2 official agency papers) were included in the analysis after filtering. One of the official agency papers was from the European Centre for Disease Prevention and Control (ECDC) and the other paper was from the Ontario Agency for Health Promotion and Protection (OHP). The result of the quality assessment with the AMSTAR-II tool of the included systematic reviews was: 3 reviews of moderate quality, 6 reviews of low quality and 1 review of critically low quality. The interventions analysed in the included studies were divided into 2 categories: the first category comprised mandatory home confinement, recommendation to stay at home and curfew, and the second category comprised perimeter blocking of entry and/or exit (local, cross-community, national or international). This division is because the included reviews analysed the measures of mandatory home confinement, advice to stay at home and curfew together without being able to carry out a disaggregated analysis. The included systematic reviews for the evaluation of home confinement, stay-at-home advice and curfew express a decrease in incidence levels, transmission and severe cases following the implementation of mobility limitation interventions compared to the no measure comparator. These conclusions are supported by the quantitative or qualitative results of the studies they include. All reviews also emphasise that to increase the effectiveness of these restrictions it is necessary to combine them with other public health measures. In the systematic reviews included for the assessment of entry and/or exit perimeter closure, most of the studies included in the reviews were found to be modelling studies based on mathematical models. All systematic reviews report a decrease in incidence, transmission and severe case levels following the implementation of travel restriction interventions. The great heterogeneity of travel restrictions applied, such as travel bans, border closures, passenger testing or screening, mandatory quarantine of travellers or optional recommendations for travellers to stay at home, makes data analysis and evaluation of interventions difficult. Conclusions Mobility restrictions in the development of the Covid-19 pandemic were one of the main NPI measures implemented. It can be concluded from the review that there is evidence for a positive impact of NPIs on the development of the COVID-19 pandemic. The heterogeneity of the data from the included studies and their low quality make it difficult to assess the effectiveness of mobility limitations in a disaggregated manner. Despite this, all the included reviews show a decrease in incidence, transmission, hospitalisations and deaths following the application of the measures under study. These measures are more effective when the restrictions were implemented earlier in the pandemic, were applied for a longer period and were more rigorous in their application.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".