Evidence map›Paper›PMID 35737177›Full record

ArticleJournal of mathematical biology2022

Bistability in deterministic and stochastic SLIAR-type models with imperfect and waning vaccine protection.

Julien Arino, Evan Milliken

Abstract read
In one paragraph

Article in Journal of mathematical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

2 authors.

Julien ArinoDepartment of Mathematics & Data Science Nexus, University of Manitoba, Winnipeg, Manitoba, Canada.ORCID 0000-0001-6409-5027
Evan MillikenDepartment of Mathematics, University of Louisville, Louisville, Kentucky, United States. Evan.Milliken@louisville.edu.ORCID 0000-0003-1268-9020

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Various vaccines have been approved for use to combat COVID-19 that offer imperfect immunity and could furthermore wane over time. We analyze the effect of vaccination in an SLIARS model with demography by adding a compartment for vaccinated individuals and considering disease-induced death, imperfect and waning vaccination protection as well as waning infections-acquired immunity. When analyzed as systems of ordinary differential equations, the model is proven to admit a backward bifurcation. A continuous time Markov chain (CTMC) version of the model is simulated numerically and compared to the results of branching process approximations. While the CTMC model detects the presence of the backward bifurcation, the branching process approximation does not. The special case of an SVIRS model is shown to have the same properties.

Indexed as

COVID-19VaccinesHumansMarkov ChainsModels, BiologicalVaccinationVaccinesBackward bifurcationBranching process approximationContinuous time Markov chainEpidemic modelWaning immunity

Identifiers

PMID35737177
PMCPMC9219406

What OpenQuestion holds

Textmetadata
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.