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Record W4412525973 · doi:10.1186/s13662-025-03972-2

Weak persistence and extinction of a stochastic epidemic model with distributed delay and Ornstein-Uhlenbeck process

2025· article· en· W4412525973 on OpenAlex
Chao Liu, Lora Cheung

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvances in Continuous and Discrete Models · 2025
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsOrnstein–Uhlenbeck processPersistence (discontinuity)Extinction (optical mineralogy)EconometricsProcess (computing)Stochastic processStatistical physicsMathematicsPsychologyEconomicsStatisticsComputer sciencePhysicsGeology

Abstract

fetched live from OpenAlex

A stochastic distributed delay epidemic model with Markovian switching and Allee effect is constructed, where the infectious disease transmission rate follows a mean-reverting Ornstein-Uhlenbeck process. Hybrid dynamic effects of Ornstein-Uhlenbeck process and Lévy jumps on infectious disease transmission are discussed. Stochastically, the ultimate boundedness of the positive solution is investigated. The existence of a unique global positive solution is studied. By constructing appropriate stochastic Lyapunov functionals, sufficient conditions for weak persistence of the infected population are investigated. The existence of a unique ergodic stationary distribution is discussed based on Hasminskii's ergodic theory. Sufficient conditions for the extinction of infectious disease are discussed. Numerical simulations are carried out to show consistency with the theoretical analysis.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.601
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.288
Teacher spread0.273 · 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