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Record W2971862287 · doi:10.3934/mbe.2019407

Effect of adaptive rewiring delay in an SIS network epidemic model

2019· article· en· W2971862287 on OpenAlexaff
Jing Li, Zhen Jin

Bibliographic record

VenueMathematical Biosciences & Engineering · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLimit cycleLimit (mathematics)Computer scienceState spaceSteady state (chemistry)Parameter spaceNetwork modelControl theory (sociology)Term (time)Process (computing)State (computer science)MathematicsPhysicsStatisticsAlgorithmMathematical analysisArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

In the real world, in order to avoid the infection risk, people tend to cut off the links with their infected neighbors, then look for other susceptible individuals to rewire. However, the rewiring process does not occur immediately, but takes some time. We therefore establish a delayed SIS network model with adaptive rewiring mechanism and analyze the long-term steady states for the system with and without the rewiring delay. We find that with the rewiring time, there are infinite equilibria lie on a line in a high-dimensional state space, which is quite different from normal delayed model. The numerical simulation results show that the system approaches to different steady state on the line under the same initial values and different rewiring delays, and the stable limit cycle can appear with the increase of rewiring delay. These surprising results may provide new insights into the study of delayed network epidemic model.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.261
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes1
Has abstractyes

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