Evidence map›Paper›PMID 35859100›Full record

ArticleScientific reports2022

Modelling the interplay of SARS-CoV-2 variants in the United Kingdom.

N L Barreiro, T Govezensky, C I Ventura, M Núñez, P G Bolcatto, R A Barrio

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 14% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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

6 authors at 4 institutions in 2 countries.

N L BarreiroInstituto de Investigaciones Científicas y Técnicas para la Defensa (CITEDEF), 1603, Buenos Aires, Argentina.
T GovezenskyInstituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, 04510, Mexico, Mexico. tzipe@unam.mx.
C I Ventura(CONICET) Centro Atómico Bariloche-CNEA, Universidad Nacional de Río Negro, 8400, Bariloche, Argentina.
M NúñezConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
P G BolcattoInstituto de Investigaciones Científicas y Técnicas para la Defensa (CITEDEF), 1603, Buenos Aires, Argentina.
R A BarrioInstituto de Física, Universidad Nacional Autónoma de México, Apartado Postal 20-365, 04510, Mexico, Mexico.
Institute of Scientific and Technical Research for Defense · ARUniversidad Nacional Autónoma de México · MXBariloche Atomic Centre · ARNational University of Río Negro · AR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many COVID-19 vaccines are proving to be highly effective to prevent severe disease and to diminish infections. Their uneven geographical distribution favors the appearance of new variants of concern, as the highly transmissible Delta variant, affecting particularly non-vaccinated people. It is important to device reliable models to analyze the spread of the different variants. A key factor is to consider the effects of vaccination as well as other measures used to contain the pandemic like social behaviour. The stochastic geographical model presented here, fulfills these requirements. It is based on an extended compartmental model that includes various strains and vaccination strategies, allowing to study the emergence and dynamics of the new COVID-19 variants. The model conveniently separates the parameters related to the disease from the ones related to social behavior and mobility restrictions. We applied the model to the United Kingdom by using available data to fit the recurrence of the currently prevalent variants. Our computer simulations allow to describe the appearance of periodic waves and the features that determine the prevalence of certain variants. They also provide useful predictions to help planning future vaccination boosters. We stress that the model could be applied to any other country of interest.

Indexed as

COVID-19SARS-CoV-2COVID-19 VaccinesHumansVaccinationCOVID-19 Vaccines

Identifiers

PMID35859100
PMCPMC9296900
OpenAlexW4286502351

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.