Outbreak Detection

The spatial scan statistic [1] detects significant spatial clusters of disease by maximizing a likelihood ratio statistic F(S) over a large set of spatial regions, typically constrained by shape. The fast localized scan [2] enables scalable detection of irregular clusters by searching over proximity-constrained subsets of locations, using the linear-time subset scanning (LTSS) property to efficiently search over all subsets of each location and its k - 1 nearest neighbors. However, for a fixed neighborhood size k, each of the 2[k] subsets are considered equally likely, and thus the fast localized scan does not take into account the spatial attributes of a subset. Hence, we wish to extend the fast localized scan by incorporating soft constraints which give preference to spatially compact clusters while still considering all subsets within a given neighborhood.

Objective

We present a new method for efficiently and accurately detecting irregularly-shaped outbreaks by incorporating "soft" constraints, rewarding spatial compactness and penalizing sparse regions.

Block 3 of the US Military Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE) system affords routine access to multiple sources of data. These include administrative clinical encounter records in the Comprehensive Ambulatory Patient Encounter Record (CAPER), records of filled prescription orders in the Pharmacy Data Transaction Service, developed at the Department of Defense (DoD) Pharmacoeconomic Center, Laboratory test orders and results in HL7 format, and others. CAPER records include a free-text Reason for Visit field, analogous to chief complaint text in civilian records, and entered by screening personnel rather than the treating healthcare provider. Other CAPER data fields are related to case severity. DoD ESSENCE treats the multiple, recently available data sources separately, requiring users to integrate algorithm results from the various evidence types themselves. This project used a Bayes Network approach to create an ESSENCE module for analytic integration, combining medical expertise with analysis of 4 years of data using documented outbreaks.

Objective

The project objective was to develop and test a decision support module using the multiple data sources available in the U.S. DoD version of ESSENCE.

The VA has employed ESSENCE for health monitoring since 2006 [1]. Epidemiologists at the Office of Public Health (OPH) monitor the VA population at the national level. The system is also intended for facility-level monitoring to cover 152 medical centers, nearly 800 community-based outpatient clinics (CBOC), and other facilities serving all fifty states, the District of Columbia, and U.S. territories. For the entire set of facilities and current syndrome groupings, investigation of the full set of algorithmic alerts is impractical for the group of monitors using ESSENCE. Signals of interest may be masked by the nationwide alert burden. Customized querying features have been added to ESSENCE, but standardization and IP training are required to assure appropriate use.

Objective

The objective was to adapt and tailor the alerting methodology employed in the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE) used by Veterans Affairs (VA) for routine, efficient health surveillance by a small, VA headquarter medical epidemiology staff in addition to a nationwide group of infection preventionists (IPs) monitoring single facilities or facility groups.

Modern public health surveillance systems have great potential for improving public health. However, evaluating the performance of surveillance systems is challenging because examples of baseline disease distribution in the population are limited to a few years of data collection. Agent-based simulations of infectious disease transmission in highly detailed synthetic populations can provide unlimited realistic baseline data.

Objective

To create, implement, and test a flexible methodology to generate detailed synthetic surveillance data providing realistic geo-spatial and temporal clustering of baseline cases.

Public health surveillance relies on multiple systems and methodologies for data collection, analysis, and interpretation. Each component provides only part of the picture, such as detection of possible outbreaks or events of concern; geographic profiles or time courses of disease activity; or indicators of clinical severity by age, risk factors, etc. Novel, unstructured data sources like Twitter feeds and aggregated news reports are growing as a source of information about health and disease. What and where are the contributions of these nontraditional, often non-specific, data types to BSV? The answer will depend on the purpose and target population. Different data streams often have greater utility for one BSV function (e.g., outbreak detection) than another (e.g., situation awareness). Furthermore, public health agencies at different levels need and use data differently, as determined by their priorities for public health. New types of data can also be useful for disease prediction and forecasting, pandemic modeling, and developing analytic tools. Before any new data modality can be integrated into standards of surveillance practice, it needs to be evaluated for its contribution to understanding disease activity and the value added when compared to other sources of data with regard to validity, timeliness, accuracy, representativeness, and positive and negative predictive values. Furthermore, questions remain about when novel, unstructured, or nontraditional data sources are acceptable evidence to inform decision-making and public health actions. To address this, the strengths and weaknesses of different types of data for various surveillance functions need to be discussed among stakeholders that bring various perspectives from surveillance research, practice, and policy.

Objective

To gather thought leaders in informatics, public health practice, surveillance research, and strategic decision-making to provide their insights into where and how to effectively integrate novel data streams, such as social media, into biosurveillance (BSV) systems and standards of public health surveillance practice.

Syndromic surveillance involves the analysis of time series of health indicators to identify changes in disease patterns. To this end, statistical modeling is used to reduce systematic data variation. Still, there is variation that cannot be accounted for in this approach, e.g. mass gatherings, extreme weather and other high-profile events. To filter sporadic events, data transformation can be applied, e.g. proportion data from correlated data streams (Peter, Najmi and Burkom, 2011; Reis, Kohane and Mandl, 2007). However, we lack systematic criteria for applying data transformations, e.g. ratios versus geometric means. To develop guidelines, we conducted a power analysis and compared the results with empirical findings (Andersson et al, 2013).

Objective

For the purpose of optimizing baselines for point-source outbreak detection, we carried out a power analysis of the effects of data transformations. More specifically, the aim was to develop statistical criteria for using composite baselines, i.e. ratios and geometric means of data streams. The results were validated by outbreak data on acute gastroenteritis (The Swedish National Telephone Health Service 1177).

This abstract describes an Electronic Surveillance System for Infectious Disease Outbreaks used by all federal levels in Germany and comments on timelyness and comprehensiveness of informations about outbreak settings and infection sources.

To determine sensitivity and specificity of syndromic surveillance of influenza based on data from SOS Medecins, a healthcare network of emergency general practitioners (GP) in Bordeaux, France.