The decision uncertainty toolkit: Risk measures and visual outputs to support decision making during public health crises
Bibliographic record
Abstract
BACKGROUND: During public health crises such as the COVID-19 pandemic, decision-makers relied on infectious disease models to evaluate policy options. Often, there is a high degree of uncertainty in the evidence base underpinning these models. When there is increased uncertainty, the risk of selecting a policy option that does not align with the intended policy objective also increases; we term this decision risk. Even when models adequately capture uncertainty, the tools used to communicate their outcomes, underlying uncertainty, and associated decision risk have often been insufficient. Our aim is to support infectious disease modellers and decision-makers in interpreting and communicating decision risk when evaluating multiple policy options. METHODS: We developed the Decision Uncertainty Toolkit by adapting methods from health economics and infectious disease modelling to improve the interpretation and communication of uncertainty. Specifically, we developed a quantitative measure of decision risk as well as a suite of risk visualizations. We refined the toolkit contents based on feedback from early dissemination through conferences and workshops. RESULTS: The Decision Uncertainty Toolkit: (i) adapts and extends existing health economics methods for characterization, estimation, and communication of uncertainty to infectious disease modelling, (ii) introduces a novel risk measure that quantitatively captures the downside risk of policy alternatives, (iii) provides visual outputs for dissemination and communication of uncertainty and decision risk, and (iv) includes instructions on how to use the toolkit, standard text descriptions and examples for each component. The use of the toolkit is demonstrated through a hypothetical example. CONCLUSION: The Decision Uncertainty Toolkit improves existing methods for communicating infectious disease model results by providing additional information regarding uncertainty and decision risk associated with policy alternatives. This empowers decision-makers to consider and evaluate decision risk more effectively when making policy decisions. Improved understanding of decision risk can improve outcomes in future public health crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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".