ISDS Conference

An important problem in biosurveillance is the early detection and characterization of outdoor aerosol releases of B. anthracis. The Bayesian Aerosol Release Detector (BARD) is a system for simulating, detecting and characterizing such releases. BARD integrates the analysis of medical surveillance data and meteorological data. The existing version of BARD does not account for the fact that many people might be exposed at a location other than their residence due to mobility. Incorporation of a mobility model in biosurveillance has been investigated by several other researchers. In this paper, we describe a refined version of the BARD simulation algorithm which incorporates a model of work-related mobility and report the results of an experiment to measure the effect of this refinement.

Objective 

To refine the simulation algorithm used in the BARD so that it takes into account the work-related mobility and to compare the refined simulator with the existing one.

On June 7, 2008, federal food protection and public health agencies alerted consumers of a nationwide outbreak of Salmonella Saintpaul infections. As of June 30, 2008, 851 persons infected with Salmonella Saintpaul with the same genetic fingerprint had been identified in 36 states and the District of Columbia since April 20081. On June 13, 2008, Maryland confirmed its first case of Salmonella Saintpaul infection matching the national outbreak strain and as of June 30, 2008, 29 cases of Salmonella related to the outbreak have been identified.

Objective 

The purpose of this paper is to describe the use of syndromic surveillance emergency department data as a tool for enhanced case finding of outbreak-related illnesses.

San Francisco has the highest rate of TB in the US. Although in recent years the incidence of TB has been declining in the San Francisco general population, it has remained relatively constant in the homeless population. Spatial investigations of disease outbreaks seek to identify and determine the significance of spatially localized disease clusters by partitioning the underlying geographic region. The level of such regional partitioning can vary depending on the available geospatial data on cases including towns, counties, zip codes, census tracts, and exact longitude-latitude coordinates. It has been shown for syndromic surveillance data that when exact patients’ geographic coordinates are used, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. While the benefits of using a finer spatial resolution, such as patients’ individual addresses, have been examined in the context of spatial epidemiology, the effect of varying spatial resolution on detection timeliness and the amount of historical data needed have not been investigated.

Objective

The objective of this study is to investigate the effect of varying the spatial resolution in a variant of space-time permutation scan statistic applied to the tuberculosis data on the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed for training the model.

The 2003/04 influenza season included a more pathogenetic organism and had an earlier onset. There were noticeably more deaths in otherwise healthy children than in previous seasons. Following this season, States were asked by the Centers for Disease Control and Prevention to increase their surveillance efforts for influenza illness.

Objective 

This paper describes data that was available in Ohio for analysis and considered valuable to determine the occurrence of influenza-like illness (ILI). These data sources were studied to determine their value to ILI surveillance and to develop an improved method of establishing influenza activity levels.

A syndromic surveillance system has been implemented at Kingston, Frontenac and Lennox & Addington Public Health in Kingston Ontario as part of a pilot project funded by the Ontario Ministry of Health and Long-Term Care – Public Health Division. The information captured by the Real-time Outbreak and Disease Surveillance-based syndromic surveillance system includes Febrile Respiratory Illness screening results (implemented since SARS) for Emergency Department (ED) visits and information detailing hospital admissions.

Objective

To use an electronic real-time ED monitoring tool to involve public health, acute care and laboratory stakeholders in an integrated alerting and response process for community-wide influenza.

Syndromic surveillance is focused upon organizing data into categories to detect medium to large scale clusters of illness. Detection often requires that a critical threshold be surpassed. Data mining searches through data to identify records containing keywords. New Hampshire has combined data mining with syndromic surveillance since January 2003 to improve detection capacity.

Objective

1. Understand the principles behind the use of syndromic surveillance and data mining. 2. Understand how New Hampshire's unique approach combining data mining with syndromic surveillance has enhanced disease surveillance efforts. 3. Describe the steps and code necessary to implement and enhance data mining.

The threat of terrorism and high-profile disease outbreaks has drawn attention to public health syndromic surveillance systems for early detection of natural or man-made disease events. In this sense, the Miami-Dade County Health Department has implemented ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) in 2005; which has been developed and updated by the Johns Hopkins University.

Objective 

This paper describes the dual monitoring process of Influenza-like Illness (ILI) syndrome in Miami-Dade County using the ESSENCE syndromic surveillance system, and their potential use as part of the seasonal influenza and pandemic influenza surveillance strategies.

Early warning systems must not always rely on geographical proximity for modeling the spread of contagious diseases. Instead, graph structures such as airways or social networks are more adequate in those situations. Nodes, associated to cities, are linked by means of edges, which represent routes between cities. Scan statistics are highly successful for the evaluation of clusters in maps based on geographical proximity. The more flexible neighborhood structure of graphs presents difficulties for the direct usage of scan statistics, due to the highly irregular structures involved. Besides, the traffic intensity between connected nodes plays a significant role which is not usually present in scan statistic based models.

Objective 

We describe a model for cluster detection and inference on networks based on the scan statistic. Our aim is to detect as early as possible the appearance of an emerging cluster of syndromes due to a real outbreak (signal) amidst unrelated syndromes (noise).

Though spatio-temporal patterns of influenza spread have often suggested that environmental factors, such as temperature, solar radiation and humidity play a key role, few studies have directly assessed their effect on the timing of annual epidemics. Finkelman et al observed a significant positive relationship between the latitudinal position of temperate countries and epidemic timing. It is hypothesized that during winter months, in temperate regions, decreased skin exposure to sunlight affects immune function by altering the production of certain immunomodulators (e.g. melatonin and Vitamin D3). Other studies have linked temperature and humidity conditions to the rate of transmission of the influenza virus.

Objective 

To assess the strength of the association between peak influenza activity and dew point, average daily temperature, solar radiation, latitude and longitude so that we may better understand the factors that affect virus transmission and/or innate immunity and to determine whether these climate variables should be used as covariates in the surveillance of influenza.

The purpose of syndromic surveillance is the early identification of disease outbreaks. Classification of chief complaints into syndromes and the type of statistics used for aberration detection can affect outbreak detection sensitivity and specificity. Few data are available on the relationship between chief complaints and demographics such as gender, age, or race. For example, myocardial infarction in women would be misclassified using definitions based solely on “male” symptoms such as chest pain because women more commonly report neck, jaw, and back pain.

Objective

We evaluated the sensitivity and specificity of a gastrointestinal syndrome group using the Boston Public Health Commission syndromic surveillance system.