To apply syndromic techniques in assessing whether the false-positive rate (FP rate) of a rapid oral HIV test, routinely used for screening in New York CityÃs STD clinics, deviated from the manufacturerÃs claim; results of which have important implications for assessing clinical test performance.
In this paper we investigate the use of the CUSUM algorithm on retrospective MMR and Pentacel (DTaP-IPV-Hib) immunization data to determine if this type of surveillance tool is useful for measuring changes in immunization rates.
To develop an automated system which examines Poison Control Center data and provides (1) early recognition of events, both man-made and naturally-occurring, which may pose a threat to public health, and (2) real-time notification to Poison Specialists, the on-site experts who evaluate those alerts.
We present a new method for multivariate outbreak detection, the ìnonparametric scan statisticî (NPSS). NPSS enables fast and accurate detection of emerging space-time clusters using multiple disparate data streams, including nontraditional data sources where standard parametric model assumptions are incorrect.
To propose a new space-time scan statistic taking overdispersion into account for accurate and timely detection of disease outbreaks.
Our objective in this research is to develop a national, geospatially-explicit set of human agents for use in agent-based models. [The term 'agents', in agent-based modeling, refers to computerized entities that represent individuals who interact with each other and their environment.]
Our objective in this research is to take advantage of a supercomputer grid (TeraGrid) to develop a distributed memory national scale agent-based model (ABM) to study disease outbreaks at the micro level. This has data needs at both the national data surveillance and the local community structure and outbreak levels.
This paper develops a new method for multivariate spatial cluster detection, the ìmultivariate Bayesian scan statisticî (MBSS). MBSS combines information from multiple data streams in a Bayesian framework, enabling faster and more accurate outbreak detection.
Learn about a highly infectious resistant tuberculosis outbreak among recent immigrants & the multijurisdictional public health response. Recognize basics about tuberculosis & anticipate difficulties with immigrants and resistant strains. Enhance epidemiologic response and treatment of tuberculosis that emerged across borders requiring coordinated response from employers, government, and individuals.
Real-time syndromic surveillance systems require adapted dataflow organization and tools for supporting data processing in real time, from their acquisition until the counter-measure building process. This work explores the capabilities of a specific model based architecture for fulfilling these requisites and its results during a real-size international disease surveillance exercise.