Streamlining Epidemiological Model Generation Using Large Language Models
Bibliographic record
Abstract
Epidemiological modeling is a critical step in understanding and studying diseases. While conventional modeling techniques, like compartmental or agentic models, exist, supporting tools are not necessarily standardized. Consequently, learning a new tool may incur significant effort. In this work, we explore the use of Large Language Models (LLMs) as a means to quickly transition from requirements, in the form of graphical model representations and initial parameters to a software-aided modeling platform with simulation and verification capabilities. Our methodology employs a metamodel-based language specification for prompt engineering, combined with multimodal inputs comprising textual descriptions and epidemiological diagrams. We evaluate LLM performance across three epidemiological models of varying complexity: a simple SIR compartmental model, a COVID-19 pandemic model, and an HIV transmission model. Initial results demonstrate the potential of LLMs to significantly reduce manual modeling effort while maintaining accuracy in structural representation and parameter extraction. The study provides insights into optimal input configurations and identifies key challenges in automated epidemiological model generation.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".