Reducing Mortality from Anthrax Bioterrorism: Strategies for Stockpiling and Dispensing Medical and Pharmaceutical Supplies
Bibliographic record
Abstract
A critical question in planning a response to bioterrorism is how antibiotics and medical supplies should be stockpiled and dispensed. The objective of this work was to evaluate the costs and benefits of alternative strategies for maintaining and dispensing local and regional inventories of antibiotics and medical supplies for responses to anthrax bioterrorism. We modeled the regional and local supply chain for antibiotics and medical supplies as well as local dispensing capacity. We found that mortality was highly dependent on the local dispensing capacity, the number of individuals requiring prophylaxis, adherence to prophylactic antibiotics, and delays in attack detection. For an attack exposing 250,000 people and requiring the prophylaxis of 5 million people, expected mortality fell from 243,000 to 145,000 as the dispensing capacity increased from 14,000 to 420,000 individuals per day. At low dispensing capacities (<14,000 individuals per day), nearly all exposed individuals died, regardless of the rate of adherence to prophylaxis, delays in attack detection, or availability of local inventories. No benefit was achieved by doubling local inventories at low dispensing capacities; however, at higher dispensing capacities, the cost-effectiveness of doubling local inventories fell from 100,000 US dollars to 20,000 US dollars/life year gained as the annual probability of an attack increased from 0.0002 to 0.001. We conclude that because of the reportedly rapid availability of regional inventories, the critical determinant of mortality following anthrax bioterrorism is local dispensing capacity. Bioterrorism preparedness efforts directed at improving local dispensing capacity are required before benefits can be reaped from enhancing local inventories.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".