Model Choice Using the Deviance Information Criterion for Latent Conditional Individual-Level Models of Infectious Disease Spread
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Individual-level models (ILMs) are a class of complex, statistical models that are often fitted within a Bayesian framework, and which can be suitable for modeling infectious disease spread. The deviance information criterion (DIC) is a model comparison tool that is appropriate for complex, Bayesian models, and since its development a number of variants have been proposed, including those for its application to missing data models. Here, we assessed five variants of the DIC and their application to ILMs, in particular a class of infectious disease models known as latent conditional LC-ILMs, which depend on a potentially unknown latent grouping variable for each individual in the population. The effectiveness of the traditionally defined DIC was compared to alternative DIC definitions through a simulation study, to assess which is most applicable for this class of models. Epidemic data was generated under an LC-ILM, to which both a spatial ILM (SILM) and the LC-ILM were fitted. Each variant of the DIC was then calculated for every fitted model, and the DIC values obtained for the LC-ILM were compared to those from the SILM. The results of the simulation study indicate that the DIC can be effective for model comparison within complex Bayesian models; however, the degree to which it is effective is dependent upon the variant of the DIC used and the amount of available information on the latent grouping variable.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it