Evidence map›Paper›PMID 39657791›Full record

ArticleJournal of the Royal Society, Interface2024

Uncertainty in COVID-19 transmission could undermine our ability to predict long COVID.

Alexander B Beams, David J D Earn, Caroline Colijn

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2024. 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.

Alexander B BeamsDepartment of Mathematics, Simon Fraser University, 8888 University Dr W, Burnaby, BC V5A 1S6, Canada.ORCID 0000-0002-6656-0217
David J D EarnDepartment of Mathematics & Statistics, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada.ORCID 0000-0002-7562-1341
Caroline ColijnDepartment of Mathematics, Simon Fraser University, 8888 University Dr W, Burnaby, BC V5A 1S6, Canada.ORCID 0000-0001-6097-6708

Funding

Canadian Institutes of Health Research (CIHR) Grant 2021-2024Natural Sciences and Engineering Research Council of Canada (NSERC) Grant
6 · The paper itself

Abstract

As SARS-CoV-2 has transitioned from a novel pandemic-causing pathogen into an established seasonal respiratory virus, focus has shifted to post-acute sequelae of COVID-19 (PASC, colloquially 'long COVID'). We use compartmental mathematical models simulating emergence of new variants to help identify key sources of uncertainty in PASC trajectories. Some parameters (such as the duration and equilibrium prevalence of infection, as well as the fraction of infections that develop PASC) matter more than others (such as the duration of immunity and secondary vaccine efficacy against PASC). Even if newer variants carry the same risk of PASC as older types, the dynamics of selection can give rise to greater PASC prevalence. However, identifying plausible PASC prevalence trajectories requires accurate knowledge of the transmission potential of COVID-19 variants in the endemic phase. Precise estimates for secondary vaccine efficacy and duration of immunity will not greatly improve forecasts for PASC prevalence. Researchers involved with Living Evidence Synthesis, or other similar initiatives focused on PASC, are well advised to ascertain primary efficacy against infection, duration of infection and prevalence of active infection in order to facilitate predictions.

Indexed as

COVID-19SARS-CoV-2COVID-19 VaccinesHumansModels, TheoreticalPandemicsPost-Acute COVID-19 SyndromeUncertaintyCOVID-19 Vaccinesintegral equationlong COVIDordinary differential equationpost-acute sequelae of COVID-19selectionvaccine efficacy

Identifiers

PMID39657791
PMCPMC11631421

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.