ArticleEmerging infectious diseases2006
Evaluating detection of an inhalational anthrax outbreak.
Article in Emerging infectious diseases, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Perspective on Improving Environmental Monitoring of Biothreats.Frontiers in bioengineering and biotechnology · 2018Article
- Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock - A review to inform risk assessment studies.Microbial risk analysis · 2016Review
- Article
- Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Rank-based spatial clustering: an algorithm for rapid outbreak detection.Journal of the American Medical Informatics Association : JAMIA · 2011Article
- Comparing early outbreak detection algorithms based on their optimized parameter values.Journal of biomedical informatics · 2010Article
- Real-time public health surveillance for emergency preparedness.American journal of public health · 2009Article
- Mechanistic modeling of emergency events: assessing the impact of hypothetical releases of anthrax.Risk analysis : an official publication of the Society for Risk Analysis · 2008Article
- Decision theoretic analysis of improving epidemic detection.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2007Article
- Application of change point analysis to daily influenza-like illness emergency department visits.Journal of the American Medical Informatics Association : JAMIAArticle
- [The validation of a new population surveillance system that analyzes the daily mortality rates in Montreal].Canadian journal of public health = Revue canadienne de sante publiqueArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Timely detection of an inhalational anthrax outbreak is critical for clinical and public health management. Syndromic surveillance has received considerable investment, but little is known about how it will perform relative to routine clinical case finding for detection of an inhalational anthrax outbreak. We conducted a simulation study to compare clinical case finding with syndromic surveillance for detection of an outbreak of inhalational anthrax. After simulated release of 1 kg of anthrax spores, the proportion of outbreaks detected first by syndromic surveillance was 0.59 at a specificity of 0.9 and 0.28 at a specificity of 0.975. The mean detection benefit of syndromic surveillance was 1.0 day at a specificity of 0.9 and 0.32 days at a specificity of 0.975. When syndromic surveillance was sufficiently sensitive to detect a substantial proportion of outbreaks before clinical case finding, it generated frequent false alarms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.