Mathematical models of the interplay between individual vaccinating decisions and disease dynamics: a need for closer integration of models and data
Bibliographic record
Abstract
In non-mandatory vaccination policies, individual choice can be a major driver of vaccine uptake. Choice thereby influences whether public health targets can be achieved. Individual vaccinating decisions can be influenced by perceptions of vaccine risks or infection risks. There is also the potential for non-vaccinators to strategically 'free-ride' on herd immunity provided by vaccinators. This strategic interaction between individuals generates a social dilemma--a conflict between self-interest and what is best for the group as a whole. Game theory and related mathematical approaches that couple mechanistic models of vaccinating decisions with mechanistic models of disease spread can capture this social dilemma and address relevant questions. The past decade has seen significant growth in the theoretical literature developing and analyzing such models. Here, we argue that using these models to address specific public health challenges will require more work that integrates information from empirical studies into the development and validation of such models, as well as more collaboration between mathematical modelers, psychologists, economists and public health experts.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".