The speed of vaccination rollout and the risk of pathogen adaptation
Bibliographic record
Abstract
Vaccination is expected to reduce disease prevalence and to halt the spread of epidemics. Pathogen adaptation, however, may erode the efficacy of vaccination and limit our ability to control disease spread. Here, we examine the influence of the rate of vaccination of the host population on the overall risk of pathogen adaptation to vaccination. We extend the framework of evolutionary epidemiology theory to account for demographic stochasticity in the different steps leading to the adaptation to vaccination: (i) the introduction of a vaccine-escape variant by mutation from an endemic wild-type pathogen, (ii) the invasion of this vaccine-escape variant in spite of the risk of early extinction, (iii) the spread and fixation of the vaccine-escape variant in the pathogen population. We introduce a novel and versatile hybrid analytical-numerical method that allows fast computation of the probabilities associated with these steps. Using it, we show that increasing the rate of vaccination can reduce both the number of cases and the likelihood of pathogen adaptation. Our work clarifies the influence of vaccination policies-a major ecological perturbation of the environment of a pathogen-on different steps of pathogen adaptation. The model provides a useful theoretical framework to account for the interplay between epidemiology, selection and genetic drift and to anticipate the effects of public-health interventions on pathogen evolution.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".