Evidence map›Paper›PMID 33152184›Full record

ArticleBiomedica : revista del Instituto Nacional de Salud2020

Pandemic's glossary: The ABC of coronavirus concepts

Felipe Botero-Rodríguez, Oscar Franco, Carlos

Abstract readDictionary
In one paragraph

Article in Biomedica : revista del Instituto Nacional de Salud, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Felipe Botero-RodríguezDepartamento de Epidemiología Clínica y Bioestadística, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, D.C., Colombia. felipe.botero@javeriana.edu.co.
Oscar FrancoInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. oscar.franco@ispm.unibe.ch.
CarlosDepartamento de Epidemiología Clínica y Bioestadística, Departamento de Psiquiatría y Salud Mental, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, D.C., Colombia; Hospital Universitario San Ignacio, Pontificia Universidad Javeriana, Bogotá, D.C., Colombia. cgomez@javeriana.edu.co.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, the world is facing the pandemic generated by SARS-CoV-2. There are no no pharmacological measures for the prevention or treatment of this infection and, so far, it has caused more than 4´880.000 confirmed cases and 322.000 deaths. The different strategies for the control of the disease that have been proposed involve the participation of different actors. Such participation, guided by legal guidelines based on public health measures, include containment, mitigation, physical isolation, and quarantine. As this is a populationbased problem, information plays a primary role; however, the many new terms hat have arisen and their misuse confuse and, therefore, misinform thus limiting citizen participation. For this reason, we conducted a review of the terms used in epidemics and pandemics of infectious diseases, particularly COVID-19. We considered and differentiated the relevant terms to facilitate the understanding of pathogen’s behavior and epidemic and pandemic cycles, as well as the criteria for public health decision-making for the general public. This glossary should facilitate the use of the terms and standardize the information.

Indexed as

BetacoronavirusCoronavirus InfectionsPandemicsPneumonia, ViralCOVID-19HumansSARS-CoV-2coronavirus infectionsmedical subject headingsPandemicsprevention and mitigationSARS virus

Identifiers

PMID33152184
PMCPMC7676831

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.