Airborne Transmission of Bordetella pertussis Demonstrated in a Baboon Model of Whooping Cough
Bibliographic record
Abstract
(See the brief report by Warfel et al, on pages 902–6.) Pertussis is a vaccine-preventable respiratory disease caused by Bordetella pertussis [1]. Globally, it is estimated that pertussis vaccines prevented approximately 700 000 deaths in 2008, attesting to the ostensible success of the vaccines [2]. However, despite vaccine coverage approaching 90% [3], 16 million cases of pertussis occur annually [2], resulting in 195 000 deaths in children <5 years of age [4]. Most of these cases are in low- and middle-income countries. Although the morbidity and mortality numbers are lower in high-income countries, pertussis remains endemic in the face of high vaccination coverage [5, 6]. For example, in the United States, despite a 92.2% reduction in the number of cases and a 99.3% reduction in deaths since the introduction of pertussis vaccines in the 1940s [7], B. pertussis continues to circulate [8], causing outbreaks such as the 2010 epidemic in California, which resulted in 7824 cases and 10 deaths in infants [9, 10]. Outbreaks occur every 2–5 years, and this cyclical nature of pertussis, which has not changed since the introduction of vaccines [8], is attributed to the immune status of the population. Immunity lasts 4–20 years after infection and 4–12 years after vaccination [11]. When immunity is high as a consequence of natural infection or vaccination, the number of cases dips; when immunity wanes, the number of cases rises. Although other factors may play a role [8, 10, 12, 13, 14], the epidemiology of pertussis is ultimately framed by the transmission dynamics of B. pertussis, which is influenced by waning immunity and the ability of the immune status of the population to be boosted either by vaccination or by natural infection [11, 15, 16, 17].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".