Data Analytics

This paper describes how powerful detectors of adverse events manifested in multivariate series of bio-surveillance data can be learned using only a few labeled instances of such events.

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 latent center. We show that using the learned models to calculate prior probabilities for a Bayesian scan statistic significantly improves detection performance.

This paper describes our integrated visual analytics framework for analyzing both human emergency room data and veterinary hospital data.

Geographic visualization methods allow analysts to visually discover clusters in multivariate, spatially-referenced data. Computational and statistical cluster detection techniques can automatically detect spatial clusters of high values of a variable of interest. The authors propose that the two approaches can be complementary; and present an integration of the GeoViz Toolkit and Proclude software suites as proof-of-concept.

The objective of this communication is to demonstrate an approach for modeling time-distributed effects of exposures to cases of infection which can be utilized in syndromic surveillance systems for characterizing, detecting, and forecasting a potential outbreak.

To understand the types of false positive cases identified by an Influenza-like illness (ILI) text classifier by measuring the prevalence of ILI-related concepts that are negated, hypothetical, include explicit mention of temporality, experienced by someone other than the patient, or described in templated text that is difficult to process.

Objective: Ideal anomaly detection algorithms should detect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. Our objective was to develop an anomaly detection algorithm that adapts to the time series being analyzed and reduces false positive signals. Background: Earlier we have presented studies with HWR, where the alerts were generated using a logical OR of several different criteria [1]. The anomaly detection contest required a continuous score for each day of the time series. This gave the impetus to develop a new version of our algorithm.

Space-time detection of disease clusters can be a computationally intensive task which defies the real time constraint for disease surveillance. At the same time, it has been shown that using exact patient locations, instead of their representative administrative regions, result in higher detection rates and accuracy while improving upon detection timeliness. Using such higher spatial resolution data, however, further exacerbates the computational burden on real time surveillance. The critical need for real time processing and interpretation of data dictate highly responsive models that may be best achievable utilizing high performance computing platforms.

Objective

Space-time detection techniques often require computationally intense searching in both the time and space domains. We introduce a high performance computing technique for parallelizing a variation of space-time permutation scan statistic applied to real data of varying spatial resolutions and demonstrate the efficiency of the technique by comparing the parallelized performance under different spatial resolutions with that of serial computation.

By capturing the spatio-temporal organization of the data using a graph, GraphScan avoids the challenges associated with trying to “fit” incoming data into moving windows of predefined shapes and sizes. Whereas the popular space-time permutation scan statistic [1] attempts to find clusters within spacetime volumes of predefined shape, GraphScan employs no such preconceptions about the form of the clusters.  Instead, clusters are allowed to “evolve” freely to better reflect the structural properties of the data.  Moreover, GraphScan is capable of tracking possible causal relationships between spatio-temporal events.

Objective

This paper proposes an efficient and flexible algorithm applicable to spatio-temporal aberration detection in public health data.

The spatial scan statistic is the usual measure of strength of a cluster [1]. Another important measure is its geometric regularity [2]. A genetic multiobjective algorithm was developed elsewhere to identify irregularly shaped clusters [3]. A search is executed aiming to maximize two objectives, namely the scan statistic and the regularity of shape (using the compactness concept). The solution presented is a Pareto-set, consisting of all the clusters found which are not simultaneously worse in both objectives. A significance evaluation is conducted in parallel for all clusters in the Pareto-set through Monte Carlo simulation, then finding the most likely cluster. \

Objective

Situations where a disease cluster does not have a regular shape are fairly common. Moreover, maps with multiple clustering, when there is not a clearly dominating primary cluster, also occur frequently. We would like to develop a method to analyze more thoroughly the several levels of clustering that arise naturally in a disease map divided into m regions.