Surveillance Tools

Situational awareness is important for both early warning and early detection of a disease outbreak, and analytics and tools that furnish information on how an infectious outbreak would either emerge or unfold provide enhanced situational awareness for decision makers/analysts/public health officials, and support planning for prevention or mitigation. Data sharing and expert analysis of incoming information are key to enhancing situational awareness of an unfolding event. In this presentation, we will describe a suite of tools developed at Los Alamos National Laboratory (LANL) that provide actionable information and knowledge for enhanced situational awareness during an unfolding event; The biosurveillance resource directory (BRD), the biosurveillance analytics resource directory (BaRD) and the surveillance window app (SWAP).

Objective

To develop a suite of tools that provides actionable information and knowledge for enhanced situational awareness during an unfolding event such as an infectious disease outbreak.

Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute project provides graphic comparisons of both ILI-related clinical visits across jurisdictions and a national picture of ILI. Unlike other surveillance systems, Distribute is designed to work solely with summarized (aggregated) data which cannot be traced back to the un-aggregated 'raw' data. This and the distributed, voluntary nature of the project creates some unique data quality issues, with considerable site to site variability. Together with the ISDS, the University of Washington has developed processes and tools to address these challenges, mirroring work done by others in the Distribute community.

Objective

To present exploratory tools and methods developed as part of the data quality monitoring of Distribute data, and discuss these tools and their applications with other participants.

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.

Syndromic surveillance is the surveillance of healthrelated data that precedes diagnosis to detect a disease outbreak or other health related event that warrants a public health response. Though syndromic surveillance is typically utilized to detect infectious disease outbreaks, its utility to detect bioterrorism events is increasingly being explored by public health agencies. Many agencies believe that syndromic surveillance holds great promise in enhancing our ability to detect both planned and unplanned outbreaks of disease and have made significant investments to develop syndromic surveillance capabilities.

For instance, the Centers for Disease Control and Prevention has invested in Biosense and the Department of Defense has invested in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) which it has deployed in partnership with the Department of Veterans Affairs. The Department of Homeland Security has invested heavily in the National Bio-surveillance Integration System which integrates a broad spectrum of bio-surveillance information including data from Biosense and ESSENCE. The University of Pittsburgh has also developed a prominent tool and is considered a thought leader in this space.

Despite the significant investments in the area of syndromic surveillance, the technology is young and the relatively small field remains fragmented. As a result, there is limited public information that addresses the field as a whole.

Objective

The objective of this assessment is to research, develop and maintain a national syndromic surveillance registry that describes each system’s configuration. By collecting current information on the leading systems we will gain a greater understanding of the syndromic surveillance landscape and capabilities.

Modern surveillance systems use statistical process control (SPC) charts such as Cumulative Sum and Exponentially Weighted Moving Average charts for monitoring daily counts of such quantities as ICD-9 codes from ED visits, sales of medications, and doctors’ office visits. The working assumption is that such pre-clinical data contain an early signature of disease outbreaks, manifested as an increase in the count levels. However, the direct application of SPC charts to the raw counts leads to unreliable performance. A popular statistical solution is to precondition the data before applying the charts by modeling or removing explainable patterns from the data and then monitoring the residuals. Although the general idea is common practice, the specifics of how to identify the existing explainable components and how to account for them are domain-specific. Therefore, we seek to present a set of modeling and data-driven tools that are useful for syndromic data.

Objective

SPC charts are widely used in disease surveillance. The charts are very effective when monitored data meet the requirements of temporal independence, stationarity, and normality. However, when these assumptions are violated, the SPC charts will either fail to detect special cause variations or will alert frequently even in the absence of anomalies. Currently collected biosurveillance data contain predictable factors such as day-of-week effects, seasonal effects, holidays, autocorrelation, and global trends that cause the data to violate these assumptions. This work (1) describes a set of tools for identifying such explainable patterns and (2) examines several data preconditioning methods that account for these factors, yielding data better suited for monitoring by traditional SPC charts.