Anthrax

Anthrax is a widely spread zoonotic disease with natural transmissive cycle involving wildlife, livestock and humans [1]. It is caused by Bacillus anthracis, a highly pathogenic gram-positive, spore-producing bacterium, which poses a serious threat to public and animal health due to its mortality both for animals and for humans [2, 3, 4]. The ability of B. anthracis spores to remain viable in soils for decades enables their isolation from freely accessible environment [5]. This unique feature to form highly resistant spores in the environment plays a major role in the ecology and evolution of this pathogen [6]. During the spore phase, evolution is greatly reduced in rate, which limits the amount of genetic diversity found among isolates of this species [1]. All these factors demonstrate the need for reliable anthrax diagnosis and trace-back methods. This comprises bio forensic capabilities including state-of-the-art methods for accurate genotyping of B. anthracis strains.

Objective: Due to the lack of information about the phylogenetic origins of Ukrainian Bacillus anthracis strains, the goal of this work was to make phylogenetic analysis of Ukrainian isolates obtained from various sources (soil, clinical material from infected humans and animal products) for better understanding of phylogenetic origins of this pathogen in Ukraine and Eastern Europe.

The use of syndromic surveillance systems by state and local health departments for the detection of bioterrorist events and emerging infections has greatly increased since 2001. While these systems have proven useful for tracking influenza and identifying large outbreaks, the value of these systems in the early detection of bioterrorism events has been under constant evaluation [3,4].

Objective

The 2001 U.S. anthrax mailings, which followed a week after the tragic events of September 11th, highlighted the nation's vulnerability to bioterrorist attacks. This event, known by its FBI code name "Amerithrax," resulted in 22 known infections and five deaths in various east coast locations, including Connecticut [1]. These cases enforced the need for an effective, federal, state, and locally-integrated biosurveillance system network that can provide early warnings to reduce casualties, as called for in U.S. Homeland Security Presidential Directive-21 (HSPD-21) and emphasized in recent CDC reports [2]. This presentation reviews several post-2001 anthrax cases and the roles played by various biosurveillance systems in their identification. Recommendations for the use of modeling and the development of regional and national coordinated surveillance systems are also discussed.

We developed a probabilistic model of how clinicians are expected to detect a disease outbreak due to an outdoor release of anthrax spores, when the clinicians only have access to traditional clinical information (e.g., no computer-based alerts). We used this model to estimate an upper bound on the amount of time expected for clinicians to detect such an outbreak. Such estimates may be useful in planning for outbreaks and in assessing the usefulness of various computer-based outbreak detection algorithms.

The Connecticut Department of Public Health (DPH), like all public health agencies, is constantly challenged by new health threats and emerging diseases. A major responsibility of these agencies is the rapid and effective communication of information on emerging threats to members of the public who may be potentially exposed. This responsibility for effective risk communication is critical when the public perception of risk is high. The September 11, 2001 terrorist attacks and subsequent anthrax mail attacks (Amerithrax) resulted in a new era of public risk perception and concern. Many new and advanced surveillance systems, developed in response to these events, have increased the need for effective risk communication. For example, the DPH developed its first syndromic surveillance system in September 2001 to monitor for possible bioterrorism events and emerging infections. This resulted in the implementation of a number of risk communication and response protocols. These and other protocols were tested in responding to the recent anthrax contamination of a drum maker’s residence and a multistate rash outbreak.

Objective

This paper describes various risk communications techniques used in Connecticut to provide health information to the public following surveillance signal alerts. The use of hotlines and contemporary social networking systems to quickly communicate with targeted populations are compared to the use of news releases and other traditional approaches.

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.

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.

The Public Health Agency of Canada is currently utilizing a syndromic surveillance prototype called the Canadian Early Warning System (CEWS). This system monitors several live data feeds, including emergency room chief complaint records from all seven local hospitals, Telehealth (24/7 nurse hotline) calls, and over-the-counter drug sales from a number of the large chain drug stores. Data trends are analysed for aberrations as early indicators of outbreak events. Collaborators on this Winnipeg, Manitoba-based pilot include the Winnipeg Regional Health Authority and IBM Business Solutions. Algorithms currently in CEWS include the 3, 5 and 7-day moving averages, CUSUM and the CDC’s EARS. We seek to investigate the performance of these algorithms in view of the fact that their detection ability may be dependent upon data source and/or the type of outbreak event.

Objective

To determine the sensitivity, specificity and days to detection of several commonly used algorithms in syndromic surveillance systems.

The objective of this work was to elucidate clinical features for distinguishing inhalational anthrax (IA) from ED patients.

Most research in syndromic surveillance has emphasized early detection, but clinical diagnosis of the index case will tend to occur before detection by syndromic surveillance for certain types of outbreaks [1]. Syndromic surveillance may, however, still play an important role in rapidly characterizing the outbreak size because there will be additional non-diagnosed symptomatic cases in the medical system when the index case is identified. Other authors have shown that the temporal pattern of symptomatic cases could be used to project the total outbreak size, but their approach requires a priori knowledge of the incubation curve for the specific anthrax strain and exposure level [2]. In this paper, we focus on estimating the number of non-diagnosed symptomatic cases at the time of detection without making assumptions about the exposure level or disease course.

Objective 

Upon detection of an inhalational anthrax attack, a critical priority for the public health response would be to characterize the size and extent of the outbreak. Our objective is to assess the potential role of syn-dromic surveillance in estimating the outbreak size.