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Record W2130325736 · doi:10.5210/ojphi.v4i3.4292

An agent based model for simulating the spread of sexually transmitted infections

2012· article· en· W2130325736 on OpenAlex

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.

Bibliographic record

VenueOnline Journal of Public Health Informatics · 2012
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAgent-based modelPopulationComputer scienceCondomSimulationArtificial intelligenceMedicineImmunologyEnvironmental healthHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

This work uses agent-based modelling (ABM) to simulate sexually transmitted infection (STIs) spread within a population of 1000 agents over a 10-year period, as a preliminary investigation of the suitability of ABM methodology to simulate STI spread. The work contrasts compartmentalized mathematical models that fail to account for individual agents, and ABMs commonly applied to simulate the spread of respiratory infections. The model was developed in C++ using the Boost 1.47.0 libraries for the normal distribution and OpenGL for visualization. Sixteen agent parameters interact individually and in combination to govern agent profiles and behaviours relative to infection probabilities. The simulation results provide qualitative comparisons of STI mitigation strategies, including the impact of condom use, promiscuity, the form of the friend network, and mandatory STI testing. Individual and population-wide impacts were explored, with individual risk being impacted much more dramatically by population-level behaviour changes as compared to individual behaviour changes.

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)0.011
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.567
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.445
GPT teacher head0.501
Teacher spread0.056 · 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