Mnatsakanyan Zaruhi

A number of syndromic surveillance systems include tools that quickly identify potentially large disease outbreak events. However, the high falsepositive rate continues to be a problem in all of these systems. Our earlier work has showed that multi-source information fusion can improve specificity of the syndromic surveillance systems. However, an anomalous health event that presents as only a few cases may remain undetected because the chief complaint data does not contain enough details. New linked data sources need to be used to enhance detection capabilities. The focus of this project examining the incorporation of laboratory, prescription medications and radiology data linked to the patient encounter within syndromic surveillance systems. These data source linkings may enhance the sensitivity of syndromic surveillance.

Objective

To enable the early detection of pandemic influenza, we have designed a system to differentiate between severe and mild influenza outbreaks. Historic information about previous pandemics suggested the evaluation of two specific discriminants: (1) the rapid development of disease to pneumonia within 1-2 days and (2) patient age distribution, as the virus usually targets specific age groups. The system is based on the hypothesis that an increased number of diagnosed pneumonia cases offers an early indication of severe influenza outbreaks. This approach is based on the fact that pneumonia cases will appear promptly in a severe influenza outbreak and can be diagnosed immediately in a physician office visit, while a confirmed influenza diagnosis requires a laboratory test. Furthermore, laboratory tests are unlikely to be ordered outside of the expected influenza season.

Although rare in the US, the CDC reports 13-14 drinking-water-related disease outbreaks per year, affecting an average of about 1000 people. The US EPA has determined that the distribution system is the most vulnerable component of a drinking water system. Recognizing this vulnerability, water utilities are increasingly measuring disinfectant levels and other parameters in their distribution systems. The US EPA is sponsoring an initiative to fuse this distribution system water quality data with health data to improve surveillance by providing an assessment of the likelihood of the occurrence of a waterborne disease outbreak. This fused analysis capability will be available via a prototype water security module within a population-based public health syndromic surveillance system.

Objective

The objective of this paper is to illustrate a technique for combining water quality and population-based health data to monitor for water-borne disease outbreaks.

One of the significant challenges that multi-user biosurveillance systems have is alarm management. Currently deployed syndromic surveillance systems [1–3] have a single user interface. However, different users have different objectives; the alarms that are important for one category of user are irrelevant to the objectives of another category of user. For example, a physician wants to identify disease on an individual-patient level, a county health authority is interested in identifying disease outbreak as early as possible within his local region, while an epidemiologist at the national level is interested in global situational awareness. The objective of a multi-agent decision support system is not only to recognize patterns of epidemiologically significant events but also to indicate their relevance to particular user groups’ objectives. Thus, instead of simply providing alerts of anomaly detections, the system architecture needs to provide analyzed information supporting multiple users’ decisions.

Every public health monitoring operation faces important decisions in its design phase. These include information sources to be used, the aggregation of data in space and time, the filtering of data records for required sensitivity, and the design of content delivery for users. Some of these decisions are dictated by available data limitations, others by objectives and resources of the organization doing the

surveillance. Most such decisions involve three characteristic tradeoffs: how much to monitor for exceptional vs customary health threats, the level of aggregation of the monitoring, and the degree of automation to be used.

The first tradeoff results from heightened concern for bioterrorism and pandemics, while everyday threats involve endemic disease events such as seasonal outbreaks. A system focused on bioterrorist attacks is scenario-based, concerned with unusual diagnoses or patient distributions, and likely to include attack hypothesis testing and tracking tools. A system at the other end of this continuum has broader syndrome groupings and is more concerned with general anomalous levels at manageable alert rates. 

Major aggregation tradeoffs are temporal, spatial, and syndromic. Bioterrorism fears have shortened the time scale of health monitoring from monthly or weekly to near-real-time. The spatial scale of monitoring is a function of the spatial resolution of data recorded and allowable for use as well as the monitoring institution’s purview and its capacity to collect, analyze and investigate localized outbreaks.

Automation tradeoffs involve the use of data processing to collect information, analyze it for anomalies, and make investigation and response decisions. The first of these uses has widespread acceptance, while in the latter two the degree of automation is a subject of ongoing controversy and research. To what degree can human judgment in alerting/response decisions be automated? What are the level and frequency of human inspection and adjustment? Should monitoring frequency change during elevated threat conditions?

All of these decisions affect monitoring tools and practices as well as funding for related research.

Objective

This purpose of this effort is to show how the goals and capabilities of health monitoring institutions can shape the selection, design, and usage of tools for automated disease surveillance systems.

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.