ArticleBMC medical informatics and decision making2007
Automated real time constant-specificity surveillance for disease outbreaks.
Article in BMC medical informatics and decision making, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Monitoring Multidrug-ResistantPolish journal of microbiology · 2022Article
- Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits.BMC veterinary research · 2020Article
- Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.BMC veterinary research · 2016Article
- A Web-based multidrug-resistant organisms surveillance and outbreak detection system with rule-based classification and clustering.Journal of medical Internet research · 2012Article
- Disease surveillance using a hidden Markov model.BMC medical informatics and decision making · 2009Article
- Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia.BMC medical informatics and decision making · 2008Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundFor real time surveillance, detection of abnormal disease patterns is based on a difference between patterns observed, and those predicted by models of historical data. The usefulness of outbreak detection strategies depends on their specificity; the false alarm rate affects the interpretation of alarms.
resultsWe evaluate the specificity of five traditional models: autoregressive, Serfling, trimmed seasonal, wavelet-based, and generalized linear. We apply each to 12 years of emergency department visits for respiratory infection syndromes at a pediatric hospital, finding that the specificity of the five models was almost always a non-constant function of the day of the week, month, and year of the study (p < 0.05). We develop an outbreak detection method, called the expectation-variance model, based on generalized additive modeling to achieve a constant specificity by accounting for not only the expected number of visits, but also the variance of the number of visits. The expectation-variance model achieves constant specificity on all three time scales, as well as earlier detection and improved sensitivity compared to traditional methods in most circumstances.
conclusionModeling the variance of visit patterns enables real-time detection with known, constant specificity at all times. With constant specificity, public health practitioners can better interpret the alarms and better evaluate the cost-effectiveness of surveillance systems.
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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.