Online surveillance of multivariate small area disease data: A Bayesian approach

The ability to rapidly detect any substantial change in disease incidence is of critical importance to facilitate timely public health response and, consequently, to reduce undue morbidity and mortality. Unlike testing methods (1, 2), modeling for spatio-temporal disease surveillance is relatively recent, and this is a very active area of statistical research (3). Models describing the behavior of diseases in space and time allow covariate effects to be estimated and provide better insight into etiology, spread, prediction and control.

May 02, 2019

Real-time Adaptive Monitoring of Vital Signs for Clinical Alarm Preemption

Cardiovascular event prediction has long been of interest in the practice of intensive care. It has been approached using signal-processing of vital signs [1-4], including the use of graphical models [3,4]. Our approach is novel in making data segmentation as well as hidden state segmentation an unsupervised process, and in simultaneously tracking evolution of multiple vital signs. The proposed models are adaptable to the individual patient's vitals online and in real time, without requiring patient-specific training data if the patient-specific feedback signal is available.

May 02, 2019

Optimal sequential management decisions for measles outbreaks

Optimal sequential management of disease outbreaks has been shown to dramatically improve the realized outbreak costs when the number of newly infected and recovered individuals is assumed to be known (1,2). This assumption has been relaxed so that infected and recovered individuals are sampled and therefore the rate of information gain about the infectiousness and morbidity of a particular outbreak is proportional to the sampling rate (3). We study the effect of no recovered sampling and signal delay, features common to surveillance systems, on the costs associated with an outbreak.

May 02, 2019

A spatial accuracy assessment of a Bayesian Bernoulli Spatial Scan Statistic

With the increase in GPS enabled devices, pin-point spatial data is an obvious future growth area for cluster detection research. The FBSSS handles binary labelled point data, but requires Monte Carlo testing to obtain inference [1]. In the Bayesian Poisson SSS [2], Monte Carlo is replaced by use of historic data, manifoldly speeding up processing. Following [2], [3] derived the BBSSS, replacing historic data with expert knowledge on cluster relative risk.

May 02, 2019

Building an automated Bayesian case detection system

Current practices of automated case detection fall into the extremes of diagnostic accuracy and timeliness. In regards to diagnostic accuracy, electronic laboratory reporting (ELR) is at one extreme and syndromic surveillance is at the other.

June 18, 2019

Challenges in adapting an natural language processing system for real-time surveillance

We are developing a Bayesian surveillance system for realtime surveillance and characterization of outbreaks that incorporates a variety of data elements, including free-text clinical reports. An existing natural language processing (NLP) system called Topaz is being used to extract clinical data from the reports. Moving the NLP system from a research project to a real-time service has presented many challenges.

 

Objective

Adapt an existing NLP system to be a useful component in a system performing real-time surveillance.

June 18, 2019

Characterization of communicable disease epidemics using bayesian inversion

The evolution of a communicable disease in a human population is not entirely predictable. However, the spreading process can be assumed to vary smoothly in time. The time-dependent infection process can be linked to observations of the epidemic’s evolution by convolving it with a stochastic delay model. In retrospective analyses of epidemics, when the observations are the dates of exhibition of patients’ symptoms, the delay is the incubation period. In case of biosurveillance data, the delay is caused by incubation and a (hospital) visit delay, modeled as independent random variables.

June 18, 2019

The Severity of Pandemic H1N1 Influenza in the United States from April to July 2009: A Bayesian Analysis

For its June 2010 Literature Review, the ISDS Research Committee invited Anne Presanis, Medical Research Council Biostatistics Unit, Cambridge, UK, to present her paper "The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis" published in the December 2009 issue of PLoS Medicine.

Presenter

Anne Presanis, Medical Research Council Biostatistics Unit

Date

Thursday, June 24, 2010

Host

ISDS Research Committee

October 20, 2017

Applications of Bayesian Statistics for Biosurveillance

For its January 2010 meeting, the ISDS Research Committee hosted a topical webinar on the "Applications of Bayesian Statistics for Biosurveillance," to address questions including:

September 25, 2017

Bayesian Methods for Syndromic Surveillance

Syndromic surveillance needs to be (1) transparent, (2) actionable, and (3) flexible. Traditional frequentist approaches to syndromic surveillance, such as cusum charts and scan statistics, tend to fail on all three criteria.

July 30, 2018

Pages

Contact Us

NSSP Community of Practice

Email: syndromic@cste.org

 

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