Surveillance Systems

Infectious disease outbreaks require rapid access to information to support a coordinated response from healthcare providers and public health officials. They need to know the size, spread, and location of the outbreak, and they also need access to models that will help them to determine the best strategy to contain the outbreak. 

There are numerous software tools for outbreak detection, and there are also surveillance systems that depend on communication between health care professionals. Most of those systems use a single type of surveillance data (e.g., syndromic, mandatory reporting, or laboratory) and focus on human surveillance.

However, there are fewer options for planning responses to outbreaks. Modeling and simulation are complex and resource-intensive. For example, EpiSims and EpiCast, developed by the National Institute of Health Models of Infectious Disease Agent Study involve large, diverse datasets and require access to high-performance computing.

Cyberenvironments are an integrated set of tools and services tailored to a specific discipline that allows the community to leverage the national cyberinfrastructure in their research and teaching. They provide data stores, computational capabilities, analysis and visualization services, and interfaces to shared instruments and sensor networks.

The National Center for Supercomputing Applications is applying the concept of cyberenvironments to infectious disease surveillance to produce INDICATOR.

Objective

This paper describes INDICATOR, a biosurveillance cyberenvironment used to analyze hospital data and generate alerts for unusual values.

If the next influenza pandemic emerges in Southeast Asia, the identification of early detection strategies in this region could enable public health officials to respond rapidly. Accurate, real-time influenza surveillance is therefore crucial. Novel approaches to the monitoring of infectious disease, especially respiratory disease, are increasingly under evaluation in an effort to avoid the cost- and timeintensive nature of active surveillance, as well as the processing time lag of traditional passive surveillance. In response to these issues, we have developed an indications and warning (I&W) taxonomy of pandemic influenza based on social disruption indicators reported in news media.

Objective

Our aim is to analyze news media for I&W of influenza to determine if the signals they create differ significantly between seasonal and pandemic influenza years.

Since October 2004, the Indiana State Health Department and the Marion County Health Department have been developing and using a syndromic surveillance system based on emergency department admission data. The system currently receives standards-based HL7 emergency department visit data, including free-text chief complaints from 72 hospitals throughout the state. Fourteen of these hospitals are in Marion County, which serves the Indianapolis metropolitan region (population 865,000).

Objective

This paper describes how a syndromic surveillance system based on emergency department data may be leveraged for other public health uses.

The North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS) serves public health users across North Carolina at the local, regional and state levels, providing syndromic surveillance capabilities.  At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health.  NC BEIPS currently receives daily data from the North Carolina Emergency Department Database (NCEDD), Carolina Poison Control Center (CPC), Prehospital Medical Information System (PreMIS) and the Piedmont Wildlife Center (PWC). Future data sources will include the North Carolina State University College of Veterinary Medicine Laboratories.  The PWC is a non-profit organization dedicated to wildlife rehabilitation, education, and scientific study of health and disease in wildlife populations.  PWC admits approximately 3,000 animals annually, including mammals, birds, and reptiles, the majority of which are from 21 counties in central North Carolina.  

Objective

This poster will illustrate how a novel data source, wildlife health center data, is being incorporated and used in a syndromic surveillance system.

In order to assess the use of rabies post-exposure prophylaxis in Indiana, the Communicable Disease Reporting Rule, adopted October 11, 2000, requires the reporting of rabies PEP administration.

Indiana is a “home rule” state; that is to say, local (county) health departments (LHD) are responsible for health issues within their jurisdiction.  Reportable diseases are passively reported to the ISDH through local health department by hospitals and physicians.   Often this results in under-reporting of things such as rabies PEP.

While the primary purpose of the PHESS is to enable early detection of acts of bioterrorism, naturally occurring outbreaks, and as a situational awareness tool, PHESS staff have continually worked to find other practical public health applications for the syndromic data.  The Epidemiology Resource Center at the ISDH houses subject matter experts in many areas of public health, including veterinary epidemiology.  Until fall of 2006, the veterinary epidemiologist received all reports of rabies PEP via hard copy.

Objective

The purpose of this paper is to describe how the Indiana State Department of Health (ISDH) leverages syndromic surveillance data to improve statewide rabies post-exposure prophylaxis (PEP) reporting by hospitals. The Public Health Emergency Surveillance System (PHESS) is Indianaís syndromic surveillance system and resides at the ISDH.

The performance of even the most advanced syndromic surveillance systems can be undermined if the monitored data is delayed before it arrives into the system.  In such cases, an outbreak may be detected only after it is too late for appropriate public health response. Surveillance systems can experience delays in data availability for a number of reasons: The process of transmitting data from data sources to the surveillance system can involve delays, especially in large systems where data is first aggregated across a national network of data sources before being transmitted to the surveillance system. Delays can also arise in the course of care, where, for example, a diagnosis is not available for a few days after the healthcare encounter.  It is important to minimize delays in data availability in order to maintain timeliness of detection [1].  When this is not possible, it is desirable to compensate for these data delays to minimize their effects.

Objective

This paper describes an approach to improving the detection timeliness of real-time health surveillance systems by modeling and correcting for delays in data availability.

The purpose of this paper is to describe the use of the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) and its ability to use hospital emergency room data for situational awareness.

The increased threat of bioterrorism and naturally occurring diseases, such as pandemic influenza, continually forces public health authorities to review methods for evaluating data and reports. The objective of bio-surveillance is to automatically process large amounts of information in order to rapidly provide the user with a situational awareness. Most systems currently deployed in health departments use only statistical algorithms to filter data for decision-making. These algorithms are capable of high sensitivity, but this sensitivity comes at the cost of excessive false positives [2], especially when multiple syndrome groups and data types are processed.

Objective

An intelligent information fusion approach is proposed to identify and provide early alerting of naturally-occurring disease outbreaks, as well as bioterrorist attacks, while reducing false positives. The proposed system statistically preprocesses information from multiple sources and fuses it in a manner comparable with the domain expert's decision-making process. Currently, system users lower the false alarm rate by "explaining away" the statistical data anomalies with alternative hypotheses derived from external, non-syndromic knowledge. We seek to incorporate this heuristic decision-making into a probabilistic network that accepts the outputs of statistical algorithms in a hybrid model of domain knowledge and data inference.

We sought to evaluate the validity of pneumonia and influenza hospitalizations (PI) data gathered by our biosurveillance system.

As part of public health protection activities conducted in support of the G8 Summit in Sea Island, GA, June 2004, DPH implemented SS in the state’s coastal region using information provided from ED visits, 911 calls, and pharmacy sales. Following this high-profile event, questions arose about whether to maintain the ED system and about whether and where to extend its use in GA.  Despite the emergence of practice-based guidance for conducting SS and the growing experience of public health agencies, little guidance is available regarding strategies for identifying sites where SS should be targeted.

 

Objective

This paper describes the strategy used by the Georgia Division of Public Health (DPH) in implementing syndromic surveillance (SS), including criteria for prioritizing localities and the early results of applying these criteria in initiating new emergency department (ED)-visit based systems.