ISDS Conference

There is a need for regular evaluation of surveillance strategies. The emergence of new diagnostic tests and new sources of data, changes in the spatio-temporal distribution of diseases and other factors must be periodically assessed to guarantee that the objectives of the surveillance effort are met. Underlying this evaluation process is the need to increase the efficient use of resources.

OBJECTIVE

We have developed a flexible model which can evaluate surveillance strategies at different hierarchical levels. It identifies key elements in the performance of the surveillance and recommends optimal sampling designs.

Bio-surveillance is an area providing real time or near real time data sets with a rich structure. In this area, the new wave of interest lies in incorporating medical-based data such as percentage of Influenza-Like-Illnesses (ILI) or count of ILI observed during visits to Emergency Room as intelligence function; since many different bioterrorist agents present with flu-like symptoms. Developing a control technique for ILI however is a complex process which involves the unpredictability of the time of emergence of influenza, the severity of the outbreak and the effectiveness of influenza epidemic interventions. Furthermore, the need to detect the beginning of epidemic in an on-line fashion as data are received one at the time and sequentially make the problems surrounding ILI's even more challenging. Statistical tools for analyzing these data are currently well short of being able to capture all their important structural details. Tools from statistical process control are on the face of it ideally suited for the task, since they address the exact problem of detecting a sudden shift against a background of random variability. Bayesian statistical methods are ideally suited to the setting of partial but imperfect information on the statistical parameters describing time series data such as are gathered in BioSense and Sentinel settings.

Objective

This paper presents a Bayesian approach to quality control through the use of sequential update technique in order built a fast detection method for influenza outbreak and potential intentional release of biological agents. The objective is to find evidence of outbreaks against a background in which markers of possible intentional release are non-stationary and serially dependent. This work takes on the US Sentinel ILI data to find this evidence and to address some issues related to the control of infectious diseases. A sensitivity analysis is conducted through simulation to assess timeliness, correct alarm and missed alarm rates of our technique.

Foot-and-mouth disease (FMD) is one of the most devastating diseases of farm animals. There is a critical need for countries to have a global FMD situational awareness. Monitoring the online news sources for FMD-related news is an important component of situational awareness. The FMD Lab at UC Davis (http://fmd.ucdavis.edu/) has developed models and systems for global FMD surveillance, including the FMD BioPortal web-based system jointly with the AI Lab at the University of Arizona. They have also been gathering and processing FMD-related news from the FMD World Reference Laboratory, the OIE, the FAO, among others. However, manual searches are necessary identify and integrate the FMD news into their models and systems. This manual work is not only time consuming and labor intensive but may also lead to the loss of some important information.

OBJECTIVE

This paper describes an FMD news monitoring and classification system which can automatically monitor FMD related news from online news data sources, generate news summarization and classify the news into three categories defined by domain experts. The report research is a collaborative effort between the Artificiall Intelligence Lab at the University of Arizona and the FMD Lab at UC Davis.

The Public Health Information Network (PHIN) Messaging Service (PHINMS) is a PHIN-certified messaging system, initiated and supported by the Centers for Disease Control & Prevention. PHINMS is widely used by many hospitals in the state(s) to send their Electronic Lab Reports. The PHINMS architecture allows for multiple data streams and routing configurations. However, many states are still using the legacy File Transport Protocol for their syndromic data transfer. There are many benefits in utilizing PHINMS that will be outlined in this presentation. PHINMS contains two components: sender and receiver. A PHINMS entity (either a hospital or DOH) can act as both/either a sender and/or a receiver. This makes two-way communication possible via the same PHINMS connection.

OBJECTIVE

This presentation describes the secure and reliable data transfer methodology of syndromic data between hospitals and public health agencies using the PHINMS. Included is an overview of PHINMS and several programs South Carolina has developed including Auto Send, Data Extract, Email Notification, and Self-Issued Security Certificates. These programs are configurable for different hospitals and run automatically. The system can be easily adopted and customized by other states.

The statistical process control (SPC) community has developed a wealth of robust, sensitive monitoring methods in the form of control charts [1]. Although such charts have been implemented for a wide variety of health monitoring purposes [2], some implementations monitor data that violate basic assumptions required by the control charts [3] yielding alerting methods with uncertain detection performance. This problem highlights an inherent obstacle to the use of traditional SPC methods for syndromic surveillance: the nature of the data. Syndromic data streams are based not on physical science, as are manufacturing processes, but on changing population behavior and evolving data acquisition and classification procedures. To overcome this obstacle, either more sophisticated detection algorithms must be developed or the data must be preconditioned so that it is appropriate for traditional monitoring tools. Objective: For robust detection performance, alerting algorithms for biosurveillance require input data free of trends, day-of-week effects, and other systematic behavior. Time series forecasting methods may be used to remove this behavior by subtracting forecasts from observations to form residuals for algorithmic input. This abstract examines and compares methods for the automatic preconditioning of health indicator data to enable the timely prospective monitoring required for effective syndromic surveillance.

Objective

For robust detection performance, alerting algorithms for biosurveillance require input data free of trends, day-of-week effects, and other systematic behavior. Time series forecasting methods may be used to remove this behavior by subtracting forecasts from observations to form residuals for algorithmic input. This abstract examines and compares methods for the automatic preconditioning of health indicator data to enable the timely prospective monitoring required for effective syndromic surveillance.

The Los Angeles County (LAC) Bioterrorism Preparedness and Response Unit has made significant progress in automating the syndromic surveillance system. The surveillance system receives electronic data on a daily basis from different hospital information systems, then standardizes and generates analytical results.

OBJECTIVE

This article describes architecture, analytical method, and software applications used in automating the LAC syndromic surveillance system.

OBJECTIVE

Syndromic surveillance systems (SSS) seek early detection of infectious diseases outbreaks by focusing on pre-diagnostic symptoms. We do not yet know which respiratory syndrome should be monitored for a SSS to discover an influenza epidemic as soon as possible. This works compares the delay and workload required to detect an influenza epidemic using a SSS that targets either (1) all cases of acute respiratory infections (ARI) or (2) only those ARI cases that are febrile and satisfy CDC's definition for an influenza-like illness.

BioSense is a national automated surveillance system designed to enhance the nation's capability to rapidly detect and quantify public health emergencies, by accessing and analyzing diagnostic and prediagnostic health data. The BioSense system currently receives near real-time data from more than 540 civilian hospitals, as well as national daily batched data from over 1100 Department of Defense and Veterans Affairs medical facilities. BioSense maps chief complaint and diagnosis data to 11 syndromes and 78 sub-syndromes. This project was spurred by the recent detection of several clusters with chief complaints containing the term “exposure” only some of which map to current BioSense sub-syndromes. BioSense currently does not have a generic “exposure” sub-syndrome.

OBJECTIVE

To identify hospital visits with chief complaints concerning exposures, characterize them, and develop methods for detecting exposure clusters.

In 2006, approximately 6.8 million children and 16.1 million adults were reported to have asthma in the US. The CDC BioSense System currently receives data from >540 hospital emergency departments (EDs; 522 send patient chief complaints and 182 send physician diagnoses), and captures about 11% of all U.S. ED visits.

OBJECTIVE

To describe the potential utility of BioSense data for surveillance of asthma.

Integration of information from multiple disparate and heterogeneous sources is a labor and resource intensive task. Heterogeneity can come about in the way data is represented or in the meaning of data in different contexts. Semantic Web technologies have been proposed to address both representational and semantic heterogeneity in distributed and collaborative environments. We introduce an automated semantic information integration platform for public health surveillance using RDF and the Simple Knowledge Organization Standard developed by the Semantic Web community.

OBJECTIVE

This paper proposes the use of Semantic Web technologies to integrate heterogeneous data generated by disparate systems for public health use.