Gauri Datta

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Syndromic surveillance has, so far, considered only simple models for Bayesian inference. This paper details the methodology for a serious, scalable solution to the problem of combining symptom data from a network of U.S. hospitals for early detection of disease outbreaks. The approach requires high-end Bayesian modeling and significant computation, but the(More)
Reliable surveillance models are an important tool in public health because they aid in mitigating disease outbreaks, identify where and when disease outbreaks occur, and predict future occurrences. Although many statistical models have been devised for surveillance purposes, none are able to simultaneously achieve the important practical goals of good(More)
Detection of hairline fractures, representing points or areas of discontinuity in the bone, is a clinically challenging task, especially in presence of noise. The above problem is equally appealing from a computer vision or pattern recognition perspective since (a) traditional techniques for detection of corners, denoting points of surface discontinuity,(More)
Finite mixtures of simpler component models such as mixtures of normals and mixtures of generalized linear models (GLM) have proven useful for modelling data arising from a heterogeneous population, typically under an independence assumption. Mixed-effects models are often used to handle correlation as arises in longitudinal or other clustered data. In(More)
A new security protocol for on-line transaction can be designed using combination of both symmetric and asymmetric cryptographic techniques. This protocol provides three cryptographic primitives - integrity, confidentiality and authentication. It uses elliptic curve cryptography for encryption, RSA algorithm for authentication and MD-5 for integrity.(More)
Automated detection of stable fracture points in a sequence of Computed Tomography (CT) images is a challenging task. In this paper, an innovative scheme for automatic fracture detection in CT images is presented. The input to the system is a sequence of CT image slices of a fractured human mandible. Techniques based on curvature scale-space theory and(More)
The problem of predicting a vector of ordered parameters or its part arises in contexts such as measurement error models, signal processing, data disclosure, and small area estimation. Often estimators of functions of the ordered random effects are obtained under strong distributional assumptions, e.g., normality. We discuss a simple generalized shrinkage(More)
BACKGROUND For researchers and public health agencies, the complexity of high-dimensional spatio-temporal data in surveillance for large reporting networks presents numerous challenges, which include low signal-to-noise ratios, spatial and temporal dependencies, and the need to characterize uncertainties. Central to the problem in the context of disease(More)
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