P1-S4.19 Stochastic network models
Bibliographic record
Abstract
Background We present a general modelling scheme particularly adapted for the epidemiology of sexually transmitted infections. Emphasis is placed on the complex interaction structure of the population and on the probabilistic nature of the dynamics. Methods We represent the interaction structure of the population with complex network models. Markov Stochastic Processes are used to consider the probabilistic time evolution of both the network structure and the epidemiological state of the population. Idealised epidemiological problems are considered: they are qualitatively inspired by real-world systems but no actual real-world data is fed to the models. All analytical results are systematically validated through Monte Carlo numerical simulations. Results By dedicating compartments to individuals with both similar epidemiological states and similar contact patterns, we observe great agreement between analytical results and Monte Carlo simulations see Abstract P1-S4.19 Figure 1. This is a special case of the general observation that compartmental models perform better when individuals within each compartment are very similar among themselves. Explicit examples are given for contact patterns changing through time as well as for the interactions of different infections in the same population. Abstract P1-S4.19 Figure 1 Epidemiology on networks. Conclusions By specifying the structure of the population through its local features, we successfully model the probabilistic evolution of complex epidemiological systems. Infection stages and/or behavioural groups are considered the same way they are in “classical” compartmental models. The generality of the approach facilitates its application to a vast array of epidemiological phenomena.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".