ISDS Conference

Time series analysis is very common in syndromic surveillance. Large scale biosurveillance systems typically perform thousands of time series queries per day: for example, monitoring of nationwide over-thecounter (OTC) sales data may require separate time series analyses on tens of thousands of zip codes. More complex query types (e.g. queries over various combinations of patient age, gender, and other characteristics, or spatial scans performed over all potential disease clusters) may require millions of distinct queries. Commercial OLAP databases provide data cubes to handle such ad hoc queries, but these methods typically suffer from long build times (typically hours), huge memory requirements (requiring the purchase of high-end database servers), and high maintenance costs. Additionally, data cubes typically require 1 second or more to respond to each complex query. This delay is an inconvenience to users who want to perform multiple queries in an online fashion; additionally, data cubes are far too slow for statistical analyses requiring millions of complex queries, which would require days of processing time.

Objective

We present T-Cube, a new tool for very fast retrieval and analysis of time series data. Using a novel method of data caching, T-Cube performs time series queries approximately 1,000 times faster than standard state-of-the-art data cube technologies. This speedup has two main benefits: it enables fast anomaly detection by simultaneous statistical analysis of many thousands of time series, and it allows public health users to perform many complex, ad hoc time series queries on the fly without inconvenient delays.

BioSense currently receives demographic and chief complaint data from more than 360 hospitals and text radiology reports from 36 hospitals. Detection of pneumonia is an important as several Category A bioterrorism diseases as well as avian influenza can manifest as pneumonia. Radiology text reports are often received within 1-2 days and may provide a faster way to identify pneumonia than coded diagnoses. Objective To study the performance of a simple keyword search of radiology reports for identifying pneumonia.

Yearly epidemics of respiratory diseases occur in children. Early recognition of these and of unexpected epidemics due to new agents or as acts of biological/chemical terrorism is desirable. In this study, we evaluate the ordering of chest radiographs as a proxy for early identification of epidemics of lower respiratory tract disease. This has the potential to act as a sensitive real-time surveillance tool during such outbreaks.

Objective:

Create a tool for monitoring respiratory epidemics based on chest radiograph ordering patterns.

Sixty-one percent of known disease-causing agents that infect humans can also infect animals [1]. While humans are the primary reservoir for only 3% of zoonoses, detection of zoonotic disease outbreaks remains mostly dependant on the identification of human cases [2]. Very few of the diseases that are a threat to humans are reportable in pets. Over onethird of American households include at least one pet [3]. Pets can present with clinical signs of disease earlier than people after becoming infected at the same time [4]. Pets can also become infected first and act as a source of infection for humans [5]. Detection of an outbreak in pets may then provide for warning of an outbreak that could affect humans.

Objective

This paper describes occurrences of possible co-morbidity in pets and humans discovered in a retrospective study of veterinary microbiology records and through the application of syndromic surveillance methods in a prospective outbreak detection system using veterinary laboratory orders.

Evaluation is a major topic in order to enhance syndromic surveillance. In May 2004, a CDC working group developed a framework for evaluating public health surveillance systems for early detection of outbreaks. This framework has been used to evaluate some civilian and also some military syndromic surveillance systems, as the French system 2SE FAG (Surveillance spatiale des épidémies au sein des forces armées en Guyane) and the UK system RMS (Real time Medical Surveillance). Those systems have been set up since the 2002 Prague summit. But because the objectives and the functioning of those systems have some military specificities, the current CDC framework was not totally adapted for their evaluation. This study presented a proposal of a new framework for evaluating military syndromic surveillance systems.

Objective

The objective of this study was to propose a new framework for evaluating military surveillance systems for early detection of outbreaks. This one was based on the French and UK military real time surveillance systems.

Management of software development projects involves a collection of well understood issues which are not often found in other project management areas. Identifying and managing these issues primarily requires that the manager is aware of the potential problems which can arise while developing software and what are the appropriate measures to control such problems.

Objective

Interest in syndromic surveillance through automated software systems is becoming more common and with this interest is an increase in small to medium sized software development projects. This paper discusses some of the common project management problems which occur when developing software in a community which does not have a long history of working in this area.

Protecting U.S. animal populations requires constant monitoring of disease events and conditions which might lead to disease emergence, both domestically and globally. Since 1999, the Center for Emerging issues (CEI has actively monitored global information sources to provide early detection impact assessments and increased awareness of emerging disease events and conditions. The importance of these activities was reinforced after September 11, 2001, and these processes are now part of the U.S. Department of Agriculture’s response to Homeland Security Presidential Directive 9. Electronic information sources available through the Internet have recently changed the way animal health information is gathered, processed and shared. To respond to these changes, CEI developed a dynamic system containing automated and semiautomated components that process information from various sources to identify, track, and evaluate emerging disease situations.

Objective

This paper describes a system of automatic and semiautomatic processes for data gathering, assessment, and event tracking used by the CEI to enhance monitoring of global animal health events and conditions.

With the widespread deployment of near real time population health monitoring, there is increasing focus on spatial cluster detection for identifying disease outbreaks. These spatial epidemiologic methods rely on knowledge of patient location to detect unusual clusters. In hospital administrative data, patient location is collected as home address but use of this precise location raises privacy concerns. Regional locations, such as center points of zip codes, have been deployed in many existing systems. However, this practice could distort the geographic properties of the raw data and affect subsequent spatial analyses. The impact of location error due to centroid assignment on the statistical analyses underlying these systems requires study.

Objective

To investigate the impact of address precision (exact latitude and longitude versus the center points of zip codes) on spatial cluster detection.

In response to increasing reports of avian influenza being identified throughout the eastern hemisphere, the U.S. Homeland Security Council, the Infectious Disease Society of America, and others have called for expansion of enhanced, real-time electronic syndromic and other advanced surveillance systems to supplement the traditional surveillance systems recommended in U.S. Department of Health & Human Services pandemic influenza preparedness plan guidance. Like many states, the Connecticut Department of Public Health, has updated its own Pandemic Influenza Response Plan to reflect its expanding arsenal of surveillance systems. These systems include a syndromic surveillance system, known as the Hospital Admissions Surveillance System (HASS), developed in September 2001 to monitor for possible bioterrorism events and emerging infections. HASS data has been utilized to supplement information received from laboratoryconfirmed influenza test results, influenza-like-illness reporting, and pneumonia influenza mortality to track seasonal influenza since 2003.

Objective

This paper summarizes the results of a continued review of state pandemic influenza preparedness plans and compares various approaches for routine influenza surveillance during interpandemic periods with approaches for enhanced surveillance during pandemic alerts. The increased reliance of syndromic and other advanced surveillance systems by U.S. states for seasonal influenza tracking and pandemic preparedness planning is documented.

In response to increasing reports of avian influenza being identified throughout the eastern hemisphere, the World Health Organization and the U.S. Department of Health and Human Services have published pandemic influenza preparedness plans. These plans include detailed recommendations for routine influenza surveillance during ongoing interpandemic periods as well as recommendations for enhanced influenza surveillance during episodes of international, national, and local pandemic alerts. Like many states, the Connecticut Department of Public Health (DPH), prepared its own Pandemic Influenza Response Plan. The DPH has also been expanding its arsenal of surveillance systems. These systems include a syndromic surveillance system, known as the Hospital Admissions Surveillance System (HASS), developed in September 2001 to monitor for possible bioterrorism events and emerging infections. HASS data has been utilized to supplement information received from laboratoryconfirmed influenza test, influenza-like-illness reporting, and pneumonia influenza mortality to track seasonal influenza.

Objective

This paper examines the results of a review of state pandemic influenza preparedness plans and compares various approaches for routine influenza surveillance during interpandemic periods with approaches for enhanced surveillance during pandemic alerts. The results of this review are compared with the experience of using a hospital-based syndromic surveillance system as a supplement to laboratory and clinical influenza surveillance systems.