Evidence map›Paper›PMID 34067128›Full record

ArticleViruses2021

Will SARS-CoV-2 Become Just Another Seasonal Coronavirus?

Alexander B Beams, Rebecca Bateman, Frederick R Adler

Open access · goldAbstract read
In one paragraph

Article in Viruses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.4field-weighted citation impact, top 20% 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, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Global trends in COVID-19.Infectious medicine · 2022
    Review
  7. The adaptation of SARS-CoV-2 to humans.Memorias do Instituto Oswaldo Cruz · 2022
    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

3 authors at 1 institution in 1 country.

Alexander B BeamsDepartment of Mathematics, University of Utah, Salt Lake City, UT 84112, USA.ORCID 0000-0002-6656-0217
Rebecca BatemanUniversity of Utah, Salt Lake City, UT 84112, USA.
Frederick R AdlerDivision of Epidemiology, University of Utah, Salt Lake City, UT 84108, USA.ORCID 0000-0002-9022-3157
University of Utah · US

Funding

CDC HHS Modeling Infectious Diseases in Healthcare Network (MInD - Healthcare)NCEZID CDC HHS U01 CK000585University of Utah COVID-19 Special Emphasis Award from the University of Utah VPR
6 · The paper itself

Abstract

The future prevalence and virulence of SARS-CoV-2 is uncertain. Some emerging pathogens become avirulent as populations approach herd immunity. Although not all viruses follow this path, the fact that the seasonal coronaviruses are benign gives some hope. We develop a general mathematical model to predict when the interplay among three factors, correlation of severity in consecutive infections, population heterogeneity in susceptibility due to age, and reduced severity due to partial immunity, will promote avirulence as SARS-CoV-2 becomes endemic. Each of these components has the potential to limit severe, high-shedding cases over time under the right circumstances, but in combination they can rapidly reduce the frequency of more severe and infectious manifestation of disease over a wide range of conditions. As more reinfections are captured in data over the next several years, these models will help to test if COVID-19 severity is beginning to attenuate in the ways our model predicts, and to predict the disease.

Indexed as

Communicable Disease ControlCOVID-19COVID-19 VaccinesDisease EradicationHumansImmunity, HerdModels, TheoreticalSARS-CoV-2SeasonsCOVID-19 Vaccinesmathematical modelordinary differential equationsSARS-CoV-2SIR model

Identifiers

PMID34067128
PMCPMC8150750
OpenAlexW3157514985

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.