Outbreak Detection

In previous work, we described a non-disease-specific outbreak simulator for the evaluation of outbreak detection algorithms. This Template-Driven Simulator generates disease patterns using user-defined template functions. Estimation of a template function from real outbreak data would enable researchers to repetitively simulate outbreaks that resemble a single real outbreak. These simulated outbreaks can then be used to evaluate outbreak detection algorithms. To demonstrate template estimation, we employ BARD, a disease-specific outbreak model for outdoor aerosol release of B. anthracis. It uses epidemiological and atmospheric dispersion models in conjunction with geographical and meteorological data to generate anthrax cases. The home census block group and time of visit to an emergency department are available for each simulated case.

Objective

In previous work, we developed a Template-Driven Simulator, which is a non-disease specific outbreak simulator that uses templates to describe the temporal or spatial-temporal pattern of an outbreak. Here we address the problem of estimating the template from outbreak data. We then conduct a limited validation of the outbreak simulation model by estimating the template using outbreak data generated from BARD, a sophisticated state-of-the-art anthrax outbreak simulator and detector. This limited validation confirms that the outbreak simulator is capable of generating complicated disease outbreak patterns for evaluating outbreak detection algorithms.

Facing public health threats of bioterrorism and emerging infectious diseases (EID), the traditional passive surveillance system is not efficient and outmoded. Evidences reveal that several newly developed syndromic surveillance system (SSS) in different countries can provide an active, powerful, timely, and effective epidemiological investigation. Using this SSS, we can find non-specific symptoms, and set up baseline clinical data and epidemic threshold. Due to English barriers and standardized language problem in the past, we initiated to develop an emergency department-based syndromic surveillance system (ED-SSS) using clinical data involving both check-list format chief complaints (CoCo) and International Classification of Diseases, Ninth Revision (ICD-9) that best fit the situations in Taiwan.

Objective

The aims of this study are to set up a SSS for detecting newly EID outbreaks early using more standardized information of triage CoCo of hospital emergency department in metropolitan Taipei City to (1) break through Chinese language barrier; (2) investigate its feasibility to detect influenza like illness (ILI) outbreaks using integrated clinical and epidemiological information installed within information technology system; and (3) compare the sensitivity, specificity, and kappa value of ILI between ICD-9 and CoCo.

The Centre for Health Protection in Hong Kong has operated a sentinel surveillance system for infectious diseases at child care centre (CCC) since March 2004, among its multi-faceted disease surveillance systems. Forty-six CCCs have participated in the system and are contributing data weekly on absenteeism and common infectious disease symptoms such as fever, diarrhea, vomiting, and cough. The system was originally driven by a manual data collection mechanism via fax, followed by secondary data input and subsequent analysis. However, such mechanism might sometimes result in delayed data transmission and data loss. As an alternative to accommodate these limitations, a web-based platform is developed to increase the timeliness of data submission by the sentinel CCCs. The new platform not only speeds up data collection and eliminates the need for human data entry, but at the same time delivers summary statistics directly on the web through computer programmes on a real time basis, as soon as data is entered by the provider.

Objective

This paper describes the attempt to develop an internet-based community surveillance network to enhance timeliness and sensitivity in detecting community-wide infectious disease outbreaks among young children at CCCs in Hong Kong.

The effectiveness of public health interventions during a disease outbreak depends on rapid, accurate characterization of the initial outbreak and spread of the pathogen. Computer-based simulation using mathematical models provides a means to characterize both and enables practitioners to test intervention strategies. While compartmental differential equation models can be used to represent epidemics, they are unsuitable for early time simulations (first few days) when a small number of people are infected (and even fewer symptomatic), nor are they capable of representing spatial disease spread. Numerous models for disease propagation have been explored, including national scale network models for influenza and social network-based and probabilistic models for smallpox. To be useful in a public health context, a model for disease propagation should be efficient (e.g., simulating several weeks of real time in an hour) and flexible enough to simultaneously represent multiple diseases and attack scenarios.

Objective

This paper describes biologically-based mathematical models and efficient methods for early epoch simulation of disease outbreaks and bioterror attacks.

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 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.

Spatial scan finds the most anomalous region that has shown increase in observed counts when compared to the expected baseline. As there can be infinitely many regions to search for, most state-of-the-art algorithms assumes a specific shape of the attack region (circles for Kulldorff and rectangles for Ultra-Fast Spatial Scan Statistics). This assumption might reduce the detection power as real world attacks don't follow standard geometric shapes.

Objective

We propose discriminative random field approach for detecting a disease outbreak. Given observed data on a spatial grid, the goal is to label each node as being under attack and non-attack.

In May 2000 accidental contamination of the water supply led to an outbreak of severe gastroenteritis in Walkerton Ontario, Canada. Of 1346 cases associated with exposure to Walkerton water, 65 were admitted to hospital, 27 developed Hemolytic-Uremic Syndrome, and six died. Estimates that 42% of cases were unreported indicate that the actual number of cases was likely 2321.

Objective

This abstract reports preliminary results of a retrospective study of the effectives of ER syndromic surveillance in detecting this outbreak.

Electronic laboratory-based surveillance can significantly improve the diagnostic specificity and response time of traditional infectious disease surveillance. Under the project “Models of Infectious Disease Agent Study”, we wished to evaluate the application of space-time outbreak detection algorithms utilizing SaTScan to a national database of routinely collected microbiology laboratory data.

Objective

This paper describes the application of the WHONET software integrated with SaTScan to the detection of Shigella outbreaks in a national database using a space-time cluster detection algorithm in simulated real-time and comparison of findings to outbreaks reported to the Ministry of Health.

Outbreaks of infectious diseases are identified in a variety of ways by clinicians and public health practitioners but not usually by analytic methods typically employed in syndromic surveillance. Systematic spatial-temporal analysis of statewide data may enable earlier detection of outbreaks and identification of multi-jurisdictional outbreaks.

Objective

Clusters of cases of individually-reportable infectious diseases were identified by a spatial-temporal retrospective analysis. Clusters were examined to determine association with previously reported outbreaks.