ArticleNonlinear dynamics2020
Complete dimensional collapse in the continuum limit of a delayed SEIQR network model with separable distributed infectivity.
Article in Nonlinear dynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed.
- Using simulation modelling and systems science to help contain COVID-19: A systematic review.Systems research and behavioral science · 2022Article
- Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model.Scientific reports · 2021Article
- Complete dimensional collapse in the continuum limit of a delayed SEIQR network model with separable distributed infectivity.Nonlinear dynamics · 2020Article
- COVID-19: data-driven dynamics, statistical and distributed delay models, and observations.Nonlinear dynamics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We take up a recently proposed compartmental SEIQR model with delays, ignore loss of immunity in the context of a fast pandemic, extend the model to a network structured on infectivity and consider the continuum limit of the same with a simple separable interaction model for the infectivities
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Registered trials
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.