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We describe an automated system that can detect multiple outbreaks of infectious diseases from emergency department reports. A case detection system obtains data from electronic medical records, extracts features using natural language processing, then infers a probability distribution over the... Read more

Content type: Abstract

This paper describes a Bayesian network model for estimating and predicting an epicurve. Furthermore, it presents results of an experiment testing the accuracy of the predictions.

Content type: Abstract

Traditional infectious disease epidemiology is built on the foundation of high quality and high accuracy data on disease and behavior. Digital infectious disease epidemiology, on the other hand, uses existing digital traces, re-purposing them to identify patterns in health-related processes.... Read more

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This paper develops a new Bayesian method for cluster detection, the ìBayesian spatial scan statistic,î and compares this method to the standard (frequen-tist) scan statistic approach on the task of prospective disease surveillance.

Content type: Abstract

Traditional influenza surveillance relies on reports of influenzalike illness (ILI) by healthcare providers, capturing individuals who seek medical care and missing those who may search, post, and tweet about their illnesses instead. Existing research has shown some promise of using data from... Read more

Content type: Abstract

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.

Content type: Abstract

Taking into account reporting delays in surveillance systems is not methodologically trivial. Consequently, most use the date of the reception of data, rather than the (often unknown) date of the health event itself. The main drawback of this approach is the resulting reduction in sensitivity... Read more

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Non-temporal Bayesian network outbreak detection methods only look at data from the most recent day. For example, PANDA-CDCA (PC) only looks at data from the last 24 hours to determine how likely an outbreak is occurring. PC is a Bayesian network disease outbreak detection system that models 12 ... Read more

Content type: Abstract

Influenza is a contagious disease that causes epidemics in many parts of the world. The World Health Organization estimates that influenza causes three to five million severe illnesses each year and 250,000-500,000 deaths. Predicting and characterizing outbreaks of influenza is an important... Read more

Content type: Abstract

Bio-surveillance is an area providing real time or near real time data sets with a rich structure. In this area, the new wave of interest lies in incorporating medical-based data such as percentage of Influenza-Like-Illnesses (ILI) or count of ILI observed during visits to Emergency Room as... Read more

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This Knowledge Repository is made possible through the activities of the Centers for Disease Control and Prevention Cooperative Agreement/Grant #1 NU500E000098-01, National Surveillance Program Community of Practice (NSSP-CoP): Strengthening Health Surveillance Capabilities Nationwide, which is in the interest of public health.

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