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Record W3022526271 · doi:10.1142/s021812742050073x

Canard Phenomenon in an SIRS Epidemic Model with Nonlinear Incidence Rate

2020· article· en· W3022526271 on OpenAlexaff
Yingying Zhang, Yicang Zhou, Biao Tang

Bibliographic record

VenueInternational Journal of Bifurcation and Chaos · 2020
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsYork University
Fundersnot available
KeywordsLimit cycleNonlinear systemPhenomenonMathematicsSingular perturbationPerturbation (astronomy)Epidemic modelDivergence (linguistics)Limit (mathematics)Applied mathematicsMathematical analysisPhysicsDemographyQuantum mechanics

Abstract

fetched live from OpenAlex

In this paper, we propose an SIRS epidemic model with a new complex nonlinear incidence rate, which describes the psychological effect of some diseases on the community as the number of infective individuals increases, including linear and nonlinear hazards of infection. The canard phenomenon for the model is analyzed, and its epidemiological meaning is discussed. By using geometrical singular perturbation theory and blow up technique, we investigate the relaxation oscillation of the model with the special fold point [Formula: see text]. The unique existence of the limit cycle is proved. We verify the existence of the canard cycle without head by using singular perturbation theory and analyze the cyclicity of the limit cycle. The detailed formula for slow divergence integral of the model is presented. We also discuss and prove the existence of the canard cycle with head. Numerical simulations are done to demonstrate our theoretical results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.327
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2020
Admission routes1
Has abstractyes

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