Regression models have broad applications in data analytics. Gaussian process regression is a nonparametric regression model that learns nonlinear maps from input features to real-valued output using a kernel function that constructs the covariance matrix among all pairs of data. Gaussian process regression often performs well in various applications. However, the time complexity of Gaussian process regression is O(n3) O(n3) for a training dataset of size n n. The cubic time complexity hinders Gaussian process regression from scaling up to large datasets. Guided by the properties of Gaussian distributions, we developed a variance-adjusted gradient boosting algorithm for approximating a Gaussian process regression (VAGR). VAGR sequentially approximates the full Gaussian process regression model using the residuals computed from variance-adjusted predictions based on randomly sampled training subsets. VAGR has a time complexity of O(nm3) O(nm3) for a training dataset of size n n and the chosen batch size m m. The reduced time complexity allows us to apply VAGR to much larger datasets compared with the full Gaussian process regression. Our experiments suggest that VAGR has a prediction performance comparable to or better than models that include random forest, gradient boosting machines, support vector regressions, and stochastic variational inference for Gaussian process regression.

Border safety is a critical part of national and international security. The Department of Homeland Security (DHS) searches vehicles entering the country at land borders for drugs and other contraband. However, this process is time-consuming and operational efficiency is needed for smooth operations at the border. To aid in the screening of vehicles, we propose to examine traffic patterns at checkpoints using burst detection algorithms. Our results show that the overall traffic at the border shows bursting patterns attributable to week days and the holiday seasons. In addition, using local law-enforcement data we also find that traffic with prior contacts with law-enforcement shows a bursting pattern distinct from other traffic. We also find that such bursts in suspicious traffic can be attributable to increases in vehicular traffic associated with certain kinds of criminal activity. This information can be used to specifically target vehicles searches during primary screening at ports and in the surrounding areas.

Accurate and timely detection of infectious disease outbreaks provides valuable information which can enable public health officials to respond to major public health threats in a timely fashion. However, disease outbreaks are often not directly observable. For surveillance systems used to detect outbreaks, noises caused by routine behavioral patterns and by special events can further complicate the detection task. Most existing detection methods combine a time series filtering procedure followed by a statistical surveillance method. The performance of this "two-step” detection method is hampered by the unrealistic assumption that the training data are outbreak-free. Moreover, existing approaches are sensitive to extreme values, which are common in real-world data sets. We considered the problem of identifying outbreak patterns in a syndrome count time series using Markov switching models. The disease outbreak states are modeled as hidden state variables which control the observed time series. A jump component is introduced to absorb sporadic extreme values that may otherwise weaken the ability to detect slow-moving disease outbreaks. Our approach outperformed several state-of-the-art detection methods in terms of detection sensitivity using both simulated and real-world data.

The threat of infectious disease outbreaks and bioterrorism attacks has stimulated the development of syndromic surveillance systems, which focus on using pre-diagnostic data such as emergency department chief complaints and over-the-counter (OTC) drug sales to detect bioterrorism events in a timely manner. A key function of syndromic surveillance systems is detecting possible bioterrorism events from time series data. In this paper, we propose a novel temporal outbreak detection method based on the Markov switching model, a special case of hidden Markov models. The model is motivated to address several computational problems with existing detection schemes concerning the inconsistency in parameter estimation and the resulting undesired detection performance. Preliminary evaluation using simulated outbreaks injected on authentic time series shows that our method outperforms benchmark methods in terms of outbreak detection speed and detection sensitivity at given levels of false alarm rates.

Infectious disease informatics, as a sub-field of security informatics, is concerned with development of the science and technologies needed for collecting, sharing, reporting, analyzing, and visualizing infectious disease data; and for providing data and decision-making support for infectious disease prevention, detection, and management. Syndromic surveillance is a major study area of infectious disease informatics, focusing on identifying in a timely manner possible infectious disease outbreaks based on pre-diagnostic data. Free-text chief complaints (CCs), short phrases describing reasons for patients' emergency department visits, are a major source of data for syndromic surveillance. For surveillance purposes, CCs need to be classified into syndromic categories. However, the lack of standard vocabulary and high-quality encoding of CCs hinder effective classification. To meet this challenge, we have developed an ontology-enhanced automatic CC classification approach. Exploiting semantic relations in the UMLS, a medical ontology, this approach is motivated to address the CC word variation problem in general and to meet the specific need for a flexible classification approach capable of handling multiple sets of syndrome categories.

This paper presents a novel ontology-based approach to classify free-text chief complaints (CCs) into syndrome categories. This approach exploits the semantic relations in a medical ontology to address the CC word variation problem. Initial computational experiments indicate that this ontology-based approach is able to improve significantly the probability that a CC can be correctly classified as a syndrome.