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Record W2086549313 · doi:10.5402/2012/581274

Dynamics of an Infectious Disease Where Media Coverage Influences Transmission

2012· article· en· W2086549313 on OpenAlex
Jean M. Tchuenche, Chris T. Bauch

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

VenueISRN Biomathematics · 2012
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Research and InnovationOntario Ministry of Research, Innovation and ScienceSchlumberger Foundation
KeywordsMedia coverageDiseaseDisease transmissionPsychosocialInfectious disease (medical specialty)Transmission (telecommunications)EpidemiologyIncidence (geometry)PsychologyEconometricsDemographyMedicineComputer scienceMathematicsSociologyTelecommunicationsPsychiatryPathologyVirologyMedia studies

Abstract

fetched live from OpenAlex

There is significant current interest in the application of media/psychosocial effects to problems in epidemiology. News reporting has the potential to reach and to modify the knowledge, attitudes, and behavior of a large proportion of the community. A susceptible-infected-hospitalized-recovered model with vital dynamics, where media coverage of disease incidence and disease prevalence can influence people to reduce their contact rates is formulated. The media function is incorporated into the model using an exponentially decreasing function. Qualitative analysis of the model reveals that the disease-free equilibrium is locally asymptotically stable when a certain threshold is less than unity. Numerical results show the potential short-term beneficial effect of media coverage.

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)0.001
metaresearch head score (Gemma)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.020
GPT teacher head0.300
Teacher spread0.280 · 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