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Record W4409440991 · doi:10.28924/2291-8639-23-2025-92

Stability Properties and Hopf Bifurcation of a Delayed HIV Dynamics Model with Saturation Functional Response, Absorption Effect and Cure Rate

2025· article· en· W4409440991 on OpenAlexvenueno aff
N. S. Rathnayaka, J. K. Wijerathna, B. G. Sampath Aruna Pradeep

Bibliographic record

VenueInternational Journal of Analysis and Applications · 2025
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsnot available
Fundersnot available
KeywordsHopf bifurcationMathematicsFunctional responseSaturation (graph theory)Stability (learning theory)Dynamics (music)Control theory (sociology)Human immunodeficiency virus (HIV)Bifurcation diagramAbsorption (acoustics)Statistical physicsBifurcationMaterials sciencePhysicsMedicineNonlinear systemComputer scienceComposite materialCombinatoricsBiology

Abstract

fetched live from OpenAlex

In this paper, stability properties of an HIV infection model with saturation functional response, logistic proliferation term of susceptible CD4+T cells, cure rate of infected CD4+T cell, virus absorption effect, intracellular delay, and maturation delay are investigated. According to our mathematical analysis, the basic reproduction number R0 of the model completely determines its stability features. Using the characteristic equation of the model, we establish that the infection-free equilibrium point and the infected equilibrium point are locally asymptotically stable when R0 ≤ 1 and R0 > 1, respectively. By means of appropriate Lyapunov functionals and LaSalle’s invariance principle for delay models, if R0 ≤ 1, we study the global asymmetric stability of the infection-free equilibrium point of the model. When R0 > 1, we establish the occurrence of Hopf bifurcations and determine conditions for the permanence of the model. Finally, numerical simulations are also presented to confirm the analytical 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.277
Teacher spread0.262 · 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 designTheoretical or conceptual
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

Citations2
Published2025
Admission routes1
Has abstractyes

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