Comparing spatio-temporal methods of non-communicable disease surveillance.

Health surveillance is well established for infectious diseases, but less so for non-communicable diseases. When spatio-temporal methods are used, selection often appears to be driven by arbitrary criteria, rather than optimal detection capabilities. Our aim is to use a theoretical simulation framework with known spatio-temporal clusters to investigate the sensitivity and specificity of several traditional (e.g. SatScan and Cusum) and Bayesian (incl. BaySTDetect and Dcluster) statistical methods for spatio-temporal cluster detection of non-communicable disease.

June 18, 2019

Comparison of statistical algorithms for syndromic surveillance aberration detection

Syndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis.

January 21, 2018

A Spatial Biosurveillance Synthetic Data Generator in R

To develop a spatially accurate biosurveillance synthetic data generator for the testing, evaluation, and comparison of new outbreak detection techniques.

June 09, 2017

Using Scenarios and Simulations to Validate Syndromic Surveillance Systems

Whilst the sensitivity and specificity of traditional laboratory-based surveillance can be readily estimated, the situation is less clear cut for syndromic surveillance. Syndromic surveillance indicators based upon presenting symptoms, chief complaints or preliminary diagnoses are designed to provide public health systems with support to detect multiple potential threats to public health. There is however, no gold standard list of all the possible ‘events’ that should have been detected.

September 28, 2017

in silico Surveillance: Informing Surveillance with Simulation

Simulations of infectious disease spread have increasingly been used to inform public policy for planning and response to outbreaks. As these techniques have increased in sophistication a wider array of uses becomes appropriate. In particular, surveillance system design, evaluation, and interpretation can be greatly aided by simulation. This presentation will describe a style of highly detailed agent-based simulation and a synthetic information analysis platform that is well equipped for these tasks.

September 28, 2017

Simulation-based Testbed for Bio-Surveillance Systems

The U.S. Defense Threat Reduction Agency (DTRA) is funding multiple development efforts directed at enhanced platforms to support bio-surveillance analysts under their Bio-surveillance Ecosystem (BSVE) program. These efforts include well-integrated user interface systems and advanced algorithmic concepts to facilitate analysis of diverse, pertinent data sources including traditional bio-surveillance data sources as well as social media inputs. A central challenge in this development effort is a practical, effective, method to test these prototype systems.

May 02, 2019

Evaluating the Performance of Syndromic Surveillance System using High-fidelity Outbreak Simulations

The evaluation of outbreak detection performance remained a major challenge to every syndromic surveillance system. Owing to the scarcity and uncertainty of infectious disease outbreaks in the real world, simulated outbreak datasets have been commonly used by scholars for performance evaluation. Although this method was powerful in estimating the performance of syndromic surveillance across a variety of outbreak scenarios, the inevitable differences between simulation and authentic outbreak event limited its external validity.

Objective

April 28, 2019

Automated Surveillance of Outpatients with Pneumonia: A Performance Evaluation

Effective responses to epidemics of infectious diseases hinge not only on early outbreak detection, but also on an assessment of disease severity. In recent work, we combined previously developed ARI case-detection algorithms (CDA) [1] with text analyses of chest imaging reports to identify ARI patients whose providers thought had pneumonia. In this work, we asked if a surveillance system aimed at patients with pneumonia would outperform one that monitors the full severity spectrum of ARI.

Objective

January 24, 2018

Multiagent Simulation of the Hepatitis B Epidemic Process

The standard approaches to simulation include solving of differential equation systems. Such approach is good for obtaining general picture of epidemics (1, 2). When the detailed analysis of epidemics reasons is needed such model becomes insufficient. To overcome the limitations of standard approaches a new one has been offered. The multiagent approach has been offered to be used for representation of the society. Methods of event-driven programming give essential benefits of the processing time of the events (3).

Objective:

June 25, 2018

Parametric Uncertainty in Intra-Herd Foot-and-Mouth Disease Epidemiological Models

Epidemiological models that simulate the spread of Foot-and-Mouth Disease within a herd are the foundation of decision support tools used by governments to help advise and inform strategy to combat outbreaks. Contact transmission data used to parameterize these models, contrary to assumption, contain a significant amount of variability and uncertainty. The implications of this finding suggest that the resultant model output might not accurately simulate the spread of an outbreak.

July 09, 2018

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