Bioterrorism

The first prototype syndromic surveillance in Japan was used during the G8 summit meeting in 2000 with two local prefectures involved. The second trial syndromic surveillance and the first internet-based surveillance used in 2002 for the Japan-Korea 2002 World Cup soccer games. Since 2002, surveillances on over-the-counter medications, ambulance call, and outpatient visits were explored as syndromic approach candidates for early detection. Internet-based events and case reporting frame work has been reviewed for outpatient visits daily reporting concurrently. Limited spread of electrical patient record and vast range of commercialized medical record formats posed obstacles to nationwide syndromic surveillance implementation.

Recent threats from bioterrorism and influenza pandemic empowered Japanese government introducing surveillance of rapid detection mechanism. In line with the revision of the Infection Control Law took place in 2007 April, national syndromic surveillance system was implemented.

Objective

This paper describes recent establishment of national surveillance system for early detection of infectious diseases in Japan. With diagnostic data fed from existed routine surveillance, newly introduced system is expected to provide timely information for control response. We aim to facilitate cross-informative regional surveillance by sharing our experience and system frame work.

The aerosol release of a pathogen during a bioterrorist incident may not always be caught on environmental sensors - it may be too small, may consist of a preparation that is coarse and heavy (and consequently precipitates quickly) or may simply have occurred in an uninstrumented location. In such a case, the first intimation of an event is the first definitive diagnosis of a patient. Being able to infer the size of the attack, its time, and the dose received has important ramifications for planning a response. Estimates drawn from such a short observation period will have limited accuracy, and hence establishing confidence levels (i.e., error bounds) on these estimates is an major concern. These estimates of outbreak characteristics can be also be used as initial conditions for epidemic models to predict the evolution of disease (along with error bounds in the predictions), in particular, communicable diseases in which the contagious period starts soon after infection (e.g., plague).

In this paper, we will consider anthrax and smallpox as our model pathogens. Since the contagious period of smallpox usually starts after the long incubation period (7–17 days), and the early epoch will consist only of index cases, we will model it as a non-contagious disease. Inputs will be obtained from simulated outbreaks as well as from the Sverdlovsk anthrax outbreak of 1979.

Objective

This paper presents a method that infers the number of infected people, the time of infection and the dose received from an aerosol release of a pathogen during a bioterrorism incident. Inputs into the inference process are the number of new diagnosed patients showing symptoms each day as observed over a short duration (3–4 days) during the early epoch of the outbreak.

Bioterrorism surveillance is an integral component of DCHD’s Comprehensive Emergency Management Plan. This study was a collaborative effort between Duval County Health Department, University of South Florida’s Center for Biological Defense (CBD), and DataSphere, LLC. DCHD’s role in the project was to identify surveillance sites, involve community partners, share data/info with surrounding agencies, counties and the state department of health, and secure funding for the system. CBD’s role in the project was facilitating the operational and technical implementation of the system and serving as a liaison between hospitals, health departments, and DataSphere, LLC. DataSphere, LLC owns and operates BioDefend and was responsible for the technical setup and maintenance of the system. The study addressed the feasibility of automated data collection by healthcare facilities and issues related to implementation of a syndromic surveillance system.

Objective

The purpose of this study was to evaluate the implementation of the BioDefend syndromic surveillance system for its use.

Syndromic surveillance is an investigational approach used to monitor trends of illness in communities. It relies on pre-diagnostic health data rather than laboratory-confirmed clinical diagnoses. Its primary purpose is to detect disease outbreaks, incidents and unusual public health events earlier than possible with traditional public health surveillance methods.

Objective

To describe how epidemiological principles are utilized to distinguish a real alert from statistically significant alerts in order to monitor and create daily reports in the Miami-Dade County Health Department using Electronic Surveillance System for the Early Notification of Community Based Epidemics. 

On 27 April 2005, a simulated bioterrorist event—the aerosolized release of Francisella tularensis in the men’s room of luxury box seats at a sports stadium—was used to exercise the disease surveillance capability of the National Capital Region (NCR). The objective of this exercise was to permit all of the health departments in the NCR to exercise inter-jurisdictional epidemiological investigations using an advanced disease surveillance system. Actual system data could not be used for the exercise as it both is proprietary and contains protected, though de-identified, health information about real people; nor is there much historical data describing how such an outbreak would manifest itself in normal syndromic data. Thus, it was essential to develop methods to generate virtual health care records that met specific requirements and represented both ‘normal’ endemic visits (the background) as well as outbreak-specific records (the injects).

Objective

This paper describes a flexible modeling and simulation process that can create realistic, virtual syndromic data for exercising electronic biosurveillance systems.

Real-time disease surveillance is critical for early detection of the covert release of a biological threat agent (BTA). Numerous software applications have been developed to detect emerging disease clusters resulting from either naturally occurring phenomena or from occult acts of bioterrorism. However, these do not focus adequately on the diagnosis of BTA infection in proportion to the potential risk to public health.

GUARDIAN is a real-time, scalable, extensible, automated, knowledge-based BTA detection and diagnosis system. GUARDIAN conducts real-time analysis of multiple pre-diagnostic parameters from records already being collected within an emergency department. The goal of this system is to move from simple trend anomaly detection to an infectious disease specific expert system in order to assist clinicians in detecting potential BTAs as quickly and effectively as possible. GUARDIAN improves the diagnostic process for BTA infection through the capture and automated application of associated clinical expertise. The automated application of this knowledge provides the focus and accuracy necessary for effective BTA infection diagnosis. The continuity of this process improves the efficiency by which diagnoses of BTA infections can be made.

Infection Control Law in Japan has asked doctors to cooperate in syndromic surveillance for pandemic flu and smallpox since 2007. However, doctors have to report by typing the number of patients on the web site, or by sending a fax to local public health centers. It imposes the heavy burden of reporting, and thus it has not worked well yet. Therefore, we need an automatic system for routine syndromic surveillance.

Objective

We performed some syndromic surveillance system for the Hokkaido Toyako G8 summit meeting in July 2008 in Japan as a counter-measure to bioterrorism attack or other health emergency. This presentation shows the workable syndromic surveillance systems in Japan.

The University of Washington has been working since 2000 with partners in Washington State to advance bioterrorism (BT) detection and preparedness. This project collects data on patients presenting with influenza-like illnesses and other potentially BT-related syndromes at emergency departments and primary care clinics (Kitsap, Clallam, and Jefferson counties) using a secure automated informatics approach. Local health jurisdiction epidemiologists use a web-based interface to view de-identified data and use a version of CDC’s EARS disease detection algorithms to watch for variances in patterns of diagnoses, volume, time and space as part of the public health real-time disease surveillance system. This processed hospital data is also made available back to the officials and administrators at the reporting hospital.

Objective

To understand GIS issues in a rural-tourban setting and demonstrate limitations of ZIPcode-only approaches compared to census tract and block approaches.

In the past year, three major health care organizations – the American Veterinary Medical Association, the American Medical Association and the Society for Tropical Veterinary Medicine – have officially endorsed the concept of “One Health” recognizing the continuum of communicable infectious disease from humans to animals and animals to humans. Further, there is widespread recognition that continuous robust surveillance of animals is beneficial not only to animal health but to food safety for humans and for early warning of naturally-occurring novel diseases (all of significance have been zoonotic for the past 20 years in the US and elsewhere) and for detecting bioterrorism events (with only one exception, all human bioterrorism agents are animal diseases.)

NC BEIPS is a system designed and developed by the NC Division of Public Health (DPH) for early detection of disease and bioterrorism outbreaks or events. It analyzes emergency department (ED) data on a daily basis from 33 (29%) EDs in North Carolina. With a new mandate requiring the submission of ED data to DPH, NC BEIPS will soon have data from all 114 EDs. NC BEIPS also receives data on a daily basis from the Carolinas Poison Center, the Prehospital Medical Information System and the Piedmont Wildlife Center, although syndromic surveillance output from these data sources is still in testing.

Objective

 This paper describes the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS). NC BEIPS is the syndromic surveillance arm of NC PHIN.