Model Choice Using the Deviance Information Criterion for Latent Conditional Individual-Level Models of Infectious Disease Spread
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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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 | 0.037 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".