Towards Rapid Design of Compartmental Models
Bibliographic record
Abstract
In times of crisis, epidemiologists can come under great pressure to model rapidly evolving diseases and to produce analyses about the effects of potential public health interventions. Taking previously developed, tested, and validated model components as the base on which to prototype new infectious disease models can save precious time and effort. However, there is currently no systematic process for quickly navigating a corpus of existing epidemiological models or identifying and reusing their most useful components. In this paper, we propose a vision to accelerate the creation of prototype compartmental models for infectious diseases. We outline a semi-automated process that epidemiologists can use to create prototypes that have been partially completed with reused fragments from existing models. Epidemiologists can thus focus on modelling the novel aspects of an ongoing public health crisis, as opposed to aspects of it that are already more or less well understood in previous work. Our approach comprises five steps in total, including identifying useful components in a corpus of infectious disease models, generating potential candidate prototypes, and organizing them in a formal data structure that allows navigation and exploration by the modellers. We outline 13 challenges ahead and discuss potential solutions based on formal modelling techniques.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".