Skip to main content

Displaying results 1 - 3 of 3
  • Content Type: Abstract

    This work incorporates model learning into a Bayesian framework for outbreak detection. Our method learns the spatial characteristics of each outbreak type from a small number of labeled training examples, assuming a generative outbreak model with… read more
    … of each outbreak type from a small number of labeled training examples, assuming a generative outbreak model with … of each outbreak type from a small number of labeled training examples, assuming a generative outbreak model with … steepness parameter h) are learned from a set of labeled training examples. Since each example specifies the outbreak …
  • Content Type: Abstract

    Disease surveillance data often has an underlying network structure (e.g. for outbreaks which spread by person-to-person contact). If the underlying graph structure is known, detection methods such as GraphScan (1) can be used to identify an… read more
    … connected subgraph for each graph structure and each training example using GraphS- can. We normalize each score … by the maximum unconstrained subset score for that training example (computed efficiently using LTSS). We then compute the mean normalized score averaged over all training examples. If a given graph is close to the true …
  • Content Type: Abstract

    We propose a new method for detecting patterns of disease cases that correspond to emerging outbreaks. Our Anomaly Pattern Detector (APD) first uses a "local anomaly detector" to identify individually anomalous records and then searches over subsets… read more
    … particular rules for the current (test) and historical (training) datasets. How- ever, an outbreak may create a … we compare it to the corres- ponding subset in the training data. For each rule R, we determine the total number of corresponding records in the test and training datasets (C(R)test and C(R)train) and the number of …