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Support Vector Machines for Syndromic Surveillance

Description

Early and reliable detection of anomalies is a critical challenge in disease surveillance. Most surveillance systems collect data from multiple data streams but the majority of monitoring is performed at univariate time series level. Purely statistical methods used in disease surveillance look at each time series separately and tend to generate a large number of false alarms. Support Vector Machines can be used to develop rich multivariate models that allow detecting abnormal relationships between different time series leading to greatly reduced number of false alarms.

 

Objective

This paper depicts a novel method for reliable detection of disease outbreaks. The methodology and initial results obtained on ESSENCE data are presented.

Submitted by elamb on