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1.  Use of outcomes to evaluate surveillance systems for bioterrorist attacks 
Syndromic surveillance systems can potentially be used to detect a bioterrorist attack earlier than traditional surveillance, by virtue of their near real-time analysis of relevant data. Receiver operator characteristic (ROC) curve analysis using the area under the curve (AUC) as a comparison metric has been recommended as a practical evaluation tool for syndromic surveillance systems, yet traditional ROC curves do not account for timeliness of detection or subsequent time-dependent health outcomes.
Using a decision-analytic approach, we predicted outcomes, measured in lives, quality adjusted life years (QALYs), and costs, for a series of simulated bioterrorist attacks. We then evaluated seven detection algorithms applied to syndromic surveillance data using outcomes-weighted ROC curves compared to simple ROC curves and timeliness-weighted ROC curves. We performed sensitivity analyses by varying the model inputs between best and worst case scenarios and by applying different methods of AUC calculation.
The decision analytic model results indicate that if a surveillance system was successful in detecting an attack, and measures were immediately taken to deliver treatment to the population, the lives, QALYs and dollars lost could be reduced considerably. The ROC curve analysis shows that the incorporation of outcomes into the evaluation metric has an important effect on the apparent performance of the surveillance systems. The relative order of performance is also heavily dependent on the choice of AUC calculation method.
This study demonstrates the importance of accounting for mortality, morbidity and costs in the evaluation of syndromic surveillance systems. Incorporating these outcomes into the ROC curve analysis allows for more accurate identification of the optimal method for signaling a possible bioterrorist attack. In addition, the parameters used to construct an ROC curve should be given careful consideration.
PMCID: PMC2876990  PMID: 20459679
2.  Syndromic Surveillance and Bioterrorism-related Epidemics 
Emerging Infectious Diseases  2003;9(10):1197-1204.
To facilitate rapid detection of a future bioterrorist attack, an increasing number of public health departments are investing in new surveillance systems that target the early manifestations of bioterrorism-related disease. Whether this approach is likely to detect an epidemic sooner than reporting by alert clinicians remains unknown. The detection of a bioterrorism-related epidemic will depend on population characteristics, availability and use of health services, the nature of an attack, epidemiologic features of individual diseases, surveillance methods, and the capacity of health departments to respond to alerts. Predicting how these factors will combine in a bioterrorism attack may be impossible. Nevertheless, understanding their likely effect on epidemic detection should help define the usefulness of syndromic surveillance and identify approaches to increasing the likelihood that clinicians recognize and report an epidemic.
PMCID: PMC3033092  PMID: 14609452
3.  Syndromic surveillance: A local perspective 
The promise of syndromic surveillance extends beyond early warning for bioterrorist attacks. Even if bioterrorism is first detected by an astute clinician, syndromic surveillance can help delineate the size, location, and tempo of the epidemic or provide reassurance that a large outbreak is not occurring when a single case or a small, localized cluster of an unusual illness is detected. More broadly, however, as public health and medicine proceed in our information age, the use of existing electronic data for public health surveillance will not appear to be an untested experiment for long. The challenge is to allow these systems to flower without burdening them with unrealistic expectations, centralized control, and unbalanced funding. To help syndromic surveillance systems reach their full potential, we need data standards, guidance to the developers of clinical information systems that will ensure data flow and interoperability, evaluations of best practices, links to improved laboratory diagnostics, regulations that protect privacy and data security, and reliable sustained funding for public health infrastructure to ensure the capacity to respond when the alarm sounds.
PMCID: PMC3456537  PMID: 12892064
4.  Establishing a nationwide emergency department-based syndromic surveillance system for better public health responses in Taiwan 
BMC Public Health  2008;8:18.
With international concern over emerging infectious diseases (EID) and bioterrorist attacks, public health is being required to have early outbreak detection systems. A disease surveillance team was organized to establish a hospital emergency department-based syndromic surveillance system (ED-SSS) capable of automatically transmitting patient data electronically from the hospitals responsible for emergency care throughout the country to the Centers for Disease Control in Taiwan (Taiwan-CDC) starting March, 2004. This report describes the challenges and steps involved in developing ED-SSS and the timely information it provides to improve in public health decision-making.
Between June 2003 and March 2004, after comparing various surveillance systems used around the world and consulting with ED physicians, pediatricians and internal medicine physicians involved in infectious disease control, the Syndromic Surveillance Research Team in Taiwan worked with the Real-time Outbreak and Disease Surveillance (RODS) Laboratory at the University of Pittsburgh to create Taiwan's ED-SSS. The system was evaluated by analyzing daily electronic ED data received in real-time from the 189 hospitals participating in this system between April 1, 2004 and March 31, 2005.
Taiwan's ED-SSS identified winter and summer spikes in two syndrome groups: influenza-like illnesses and respiratory syndrome illnesses, while total numbers of ED visits were significantly higher on weekends, national holidays and the days of Chinese lunar new year than weekdays (p < 0.001). It also identified increases in the upper, lower, and total gastrointestinal (GI) syndrome groups starting in November 2004 and two clear spikes in enterovirus-like infections coinciding with the two school semesters. Using ED-SSS for surveillance of influenza-like illnesses and enteroviruses-related infections has improved Taiwan's pandemic flu preparedness and disease control capabilities.
Taiwan's ED-SSS represents the first nationwide real-time syndromic surveillance system ever established in Asia. The experiences reported herein can encourage other countries to develop their own surveillance systems. The system can be adapted to other cultural and language environments for better global surveillance of infectious diseases and international collaboration.
PMCID: PMC2249581  PMID: 18201388
5.  Implementing Syndromic Surveillance: A Practical Guide Informed by the Early Experience 
Syndromic surveillance refers to methods relying on detection of individual and population health indicators that are discernible before confirmed diagnoses are made. In particular, prior to the laboratory confirmation of an infectious disease, ill persons may exhibit behavioral patterns, symptoms, signs, or laboratory findings that can be tracked through a variety of data sources. Syndromic surveillance systems are being developed locally, regionally, and nationally. The efforts have been largely directed at facilitating the early detection of a covert bioterrorist attack, but the technology may also be useful for general public health, clinical medicine, quality improvement, patient safety, and research. This report, authored by developers and methodologists involved in the design and deployment of the first wave of syndromic surveillance systems, is intended to serve as a guide for informaticians, public health managers, and practitioners who are currently planning deployment of such systems in their regions.
PMCID: PMC353021  PMID: 14633933
6.  Enhanced drop-in syndromic surveillance in New York City following September 11, 2001 
After the 2001 World Trade Center disaster, the New York City Department of Health was under heightened alert for bioterrorist attacks in the city. An emergency department (ED) syndromic surveillance system was implemented with the assistance of the Centers for Disease Control and Prevention to ensure early recognition of an increase or clustering of disease syndromes that might represent a disease outbreak, whether natural or intentional. The surveillance system was based on data collected 7 days a week at area EDs. Data collected were translated into syndromes, entered into an electronic database, and analyzed for aberrations in space and time within 24 hours. From September 14–27, personnel were stationed at 15 EDs on a 24-hour basis (first staffing period); from September 29–October 12, due to resource limitations, personnel were stationed at 12 EDs on an 18-hour basis (second staffing period). A standardized form was used to obtain demographic information and classify each patient visit into 12 syndrome categories. Seven of these represented early manifestations of bioterrorist agents. Data transfer and analysis for time and space clustering (alarms) by syndrome and age occurred daily. Retrospective analyses examined syndrome trends, differences in reporting between staffing periods, and the staff’s experience during the project. A total of 67,536 reports were received. The system captured 83.9% of patient visits during the first staffing period, and 60.8% during the second staffing period (P<01). Five syndromes each accounted for more than 1% of visits: trauma, asthma, gastrointestinal illness, upper/lower respiratory infection with fever, and anxiety. Citywide temporal alarms occurred eight times for three of the major bioterrorism-related syndromes. Spatial clustering alarms occurred 16 time by hospital location and 9 times by ZIP code for the same three syndromes. No outbreaks were detected. On-site staffing to facilitate data collection and entry, supported by daily analysis of ED visits, is a feasible short-term approach to syndromic surveillance during high-profile events. The resources required to operate such a system, however, cannot be sustained for the long term. This system was changed to an electronic-based ED syndromic system using triage log data that remains in operation.
PMCID: PMC3456534  PMID: 12791782
7.  Integrating Syndromic Surveillance Data across Multiple Locations: Effects on Outbreak Detection Performance 
Syndromic surveillance systems are being deployed widely to monitor for signals of covert bioterrorist attacks. Regional systems are being established through the integration of local surveillance data across multiple facilities. We studied how different methods of data integration affect outbreak detection performance. We used a simulation relying on a semi-synthetic dataset, introducing simulated outbreaks of different sizes into historical visit data from two hospitals. In one simulation, we introduced the synthetic outbreak evenly into both hospital datasets (aggregate model). In the second, the outbreak was introduced into only one or the other of the hospital datasets (local model). We found that the aggregate model had a higher sensitivity for detecting outbreaks that were evenly distributed between the hospitals. However, for outbreaks that were localized to one facility, maintaining individual models for each location proved to be better. Given the complementary benefits offered by both approaches, the results suggest building a hybrid system that includes both individual models for each location, and an aggregate model that combines all the data. We also discuss options for multi-level signal integration hierarchies.
PMCID: PMC1479922  PMID: 14728233
8.  Time series modeling for syndromic surveillance 
Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates.
Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks.
Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity.
Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization.
PMCID: PMC149370  PMID: 12542838
9.  An Epidemiological Network Model for Disease Outbreak Detection 
PLoS Medicine  2007;4(6):e210.
Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most.
Methods and Findings
To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health–surveillance system, developed by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology on behalf of the Massachusetts Department of Public Health. We performed experiments with semi-synthetic outbreaks of different magnitudes and simulated baseline shifts of different types and magnitudes. The results show that the network models provide better detection of localized outbreaks, and greater robustness to unpredictable shifts than a reference time-series modeling approach.
The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events.
Most surveillance systems are not robust to shifts in health care utilization. Ben Reis and colleagues developed network models that detected localized outbreaks better and were more robust to unpredictable shifts.
Editors' Summary
The main task of public-health officials is to promote health in communities around the world. To do this, they need to monitor human health continually, so that any outbreaks (epidemics) of infectious diseases (particularly global epidemics or pandemics) or any bioterrorist attacks can be detected and dealt with quickly. In recent years, advanced disease-surveillance systems have been introduced that analyze data on hospital visits, purchases of drugs, and the use of laboratory tests to look for tell-tale signs of disease outbreaks. These surveillance systems work by comparing current data on the use of health-care resources with historical data or by identifying sudden increases in the use of these resources. So, for example, more doctors asking for tests for salmonella than in the past might presage an outbreak of food poisoning, and a sudden rise in people buying over-the-counter flu remedies might indicate the start of an influenza pandemic.
Why Was This Study Done?
Existing disease-surveillance systems don't always detect disease outbreaks, particularly in situations where there are shifts in the baseline patterns of health-care use. For example, during an epidemic, people might stay away from hospitals because of the fear of becoming infected, whereas after a suspected bioterrorist attack with an infectious agent, hospitals might be flooded with “worried well” (healthy people who think they have been exposed to the agent). Baseline shifts like these might prevent the detection of increased illness caused by the epidemic or the bioterrorist attack. Localized population surges associated with major public events (for example, the Olympics) are also likely to reduce the ability of existing surveillance systems to detect infectious disease outbreaks. In this study, the researchers developed a new class of surveillance systems called “epidemiological network models.” These systems aim to improve the detection of disease outbreaks by monitoring fluctuations in the relationships between information detailing the use of various health-care resources over time (data streams).
What Did the Researchers Do and Find?
The researchers used data collected over a 3-y period from five Boston hospitals on visits for respiratory (breathing) problems and for gastrointestinal (stomach and gut) problems, and on total visits (15 data streams in total), to construct a network model that included all the possible pair-wise comparisons between the data streams. They tested this model by comparing its ability to detect simulated disease outbreaks implanted into data collected over an additional year with that of a reference model based on individual data streams. The network approach, they report, was better at detecting localized outbreaks of respiratory and gastrointestinal disease than the reference approach. To investigate how well the network model dealt with baseline shifts in the use of health-care resources, the researchers then added in a large population surge. The detection performance of the reference model decreased in this test, but the performance of the complete network model and of models that included relationships between only some of the data streams remained stable. Finally, the researchers tested what would happen in a situation where there were large numbers of “worried well.” Again, the network models detected disease outbreaks consistently better than the reference model.
What Do These Findings Mean?
These findings suggest that epidemiological network systems that monitor the relationships between health-care resource-utilization data streams might detect disease outbreaks better than current systems under normal conditions and might be less affected by unpredictable shifts in the baseline data. However, because the tests of the new class of surveillance system reported here used simulated infectious disease outbreaks and baseline shifts, the network models may behave differently in real-life situations or if built using data from other hospitals. Nevertheless, these findings strongly suggest that public-health officials, provided they have sufficient computer power at their disposal, might improve their ability to detect disease outbreaks by using epidemiological network systems alongside their current disease-surveillance systems.
Additional Information.
Please access these Web sites via the online version of this summary at
Wikipedia pages on public health (note that Wikipedia is a free online encyclopedia that anyone can edit, and is available in several languages)
A brief description from the World Health Organization of public-health surveillance (in English, French, Spanish, Russian, Arabic, and Chinese)
A detailed report from the US Centers for Disease Control and Prevention called “Framework for Evaluating Public Health Surveillance Systems for the Early Detection of Outbreaks”
The International Society for Disease Surveillance Web site
PMCID: PMC1896205  PMID: 17593895
10.  Bioterrorism in Canada: An economic assessment of prevention and postattack response 
The present paper calculates the human and economic consequences of a bioterrorist attack on Canadian soil using aerosolized Bacillus anthracis and Clostridium botulinum. The study assumed that 100,000 people in a Canadian suburban neighbourhood were exposed over a 2 h period to an infectious dose of one of the agents. Using an epidemic curve based on the epidemiology and management of anthrax and botulinum poisoning, the costs of intervention and treatment after an attack were compared with the costs of preparedness before a bioterrorist attack. The results show that an investment in planning and preparedness to manage the consequences of an attack can reduce morbidity, mortality and economic costs. The sooner that an intervention program is instituted, the more significant are the health and economic benefits. The greatest benefits were realized when postattack intervention was initiated before day 3 after the event. The economic impact of a bioterrorist attack in Canada could range from $6.4 billion/100,000 exposed to B anthracis to $8.6 billion/100,000 exposed in an attack using C botulinum. Without the benefit of an effective consequence management program, predicted deaths totalled 32,875 from anthrax and 30,000 from botulinum toxin. Rapid implementation of a postattack prophylaxis program that includes the stockpiling of antibiotics, vaccines and antitoxins; training of first responders in the diagnosis, handling and treatment of pathogens; and the general enhancement of Canada's response capability would reduce both human and economic losses.
PMCID: PMC2094836  PMID: 18159350
Actuarially fair premium; Anthrax; Bioterrorism; Botulinum toxin; Economic consequences
11.  Data, network, and application: technical description of the Utah RODS Winter Olympic Biosurveillance System. 
Given the post September 11th climate of possible bioterrorist attacks and the high profile 2002 Winter Olympics in the Salt Lake City, Utah, we challenged ourselves to deploy a computer-based real-time automated biosurveillance system for Utah, the Utah Real-time Outbreak and Disease Surveillance system (Utah RODS), in six weeks using our existing Real-time Outbreak and Disease Surveillance (RODS) architecture. During the Olympics, Utah RODS received real-time HL-7 admission messages from 10 emergency departments and 20 walk-in clinics. It collected free-text chief complaints, categorized them into one of seven prodromes classes using natural language processing, and provided a web interface for real-time display of time series graphs, geographic information system output, outbreak algorithm alerts, and details of the cases. The system detected two possible outbreaks that were dismissed as the natural result of increasing rates of Influenza. Utah RODS allowed us to further understand the complexities underlying the rapid deployment of a RODS-like system.
PMCID: PMC2244477  PMID: 12463938
12.  Using automated medical records for rapid identification of illness syndromes (syndromic surveillance): the example of lower respiratory infection 
BMC Public Health  2001;1:9.
Gaps in disease surveillance capacity, particularly for emerging infections and bioterrorist attack, highlight a need for efficient, real time identification of diseases.
We studied automated records from 1996 through 1999 of approximately 250,000 health plan members in greater Boston.
We identified 152,435 lower respiratory infection illness visits, comprising 106,670 episodes during 1,143,208 person-years. Three diagnoses, cough (ICD9CM 786.2), pneumonia not otherwise specified (ICD9CM 486) and acute bronchitis (ICD9CM 466.0) accounted for 91% of these visits, with expected age and sex distributions. Variation of weekly occurrences corresponded closely to national pneumonia and influenza mortality data. There was substantial variation in geographic location of the cases.
This information complements existing surveillance programs by assessing the large majority of episodes of illness for which no etiologic agents are identified. Additional advantages include: a) sensitivity, uniformity and efficiency, since detection of events does not depend on clinicians' to actively report diagnoses, b) timeliness, the data are available within a day of the clinical event; and c) ease of integration into automated surveillance systems.
These features facilitate early detection of conditions of public health importance, including regularly occurring events like seasonal respiratory illness, as well as unusual occurrences, such as a bioterrorist attack that first manifests as respiratory symptoms. These methods should also be applicable to other infectious and non-infectious conditions. Knowledge of disease patterns in real time may also help clinicians to manage patients, and assist health plan administrators in allocating resources efficiently.
PMCID: PMC60002  PMID: 11722798
13.  Syndromic Surveillance for Bioterrorism Using Computerized Discharge Diagnosis Databases 
We have built a surveillance system to facilitate early detection of possible bioterrorist attacks. Data are collected from emergency departments and a primary care clinic using computerized electronic discharge diagnostic databases. Heterogeneous data are transformed to XML (eXtensible Markup Language) for transmission to a central database system for analysis.
PMCID: PMC2243670
14.  Preparing for a Bioterrorist Attack: Legal and Administrative Strategies1 
Emerging Infectious Diseases  2003;9(2):241-245.
This article proposes and discusses legal and administrative preparations for a bioterrorist attack. To perform the duties expected of public health agencies during a disease outbreak caused by bioterrorism, an agency must have a sufficient number of employees and providers at work and a good communications system between staff in the central offices of the public health agency and those in outlying or neighboring agencies and hospitals. The article proposes strategies for achieving these objectives as well as for removing legal barriers that discourage agencies, institutions, and persons from working together for the overall good of the community. Issues related to disease surveillance and special considerations regarding public health restrictive orders are discussed.
PMCID: PMC2901954  PMID: 12603997
bioterrorism; disease outbreaks; public health; jurisprudence; quarantine; patient isolation; perspective
15.  Treatment of Neuroterrorism 
Neurotherapeutics  2012;9(1):139-157.
Bioterrorism is defined as the intentional use of biological, chemical, nuclear, or radiological agents to cause disease, death, or environmental damage. Early recognition of a bioterrorist attack is of utmost importance to minimize casualties and initiate appropriate therapy. The range of agents that could potentially be used as weapons is wide, however, only a few of these agents have all the characteristics making them ideal for that purpose. Many of the chemical and biological weapons can cause neurological symptoms and damage the nervous system in varying degrees. Therefore, preparedness among neurologists is important. The main challenge is to be cognizant of the clinical syndromes and to be able to differentiate diseases caused by bioterrorism from naturally occurring disorders. This review provides an overview of the biological and chemical warfare agents, with a focus on neurological manifestation and an approach to treatment from a perspective of neurological critical care.
Electronic supplementary material
The online version of this article (doi:10.1007/s13311-011-0097-2) contains supplementary material, which is available to authorized users.
PMCID: PMC3271146  PMID: 22227729
Neuroterrorism; Bioterrorism; Warfare Agents
16.  The economic impact of a bioterrorist attack: are prevention and postattack intervention programs justifiable? 
Emerging Infectious Diseases  1997;3(2):83-94.
Understanding and quantifying the impact of a bioterrorist attack are essential in developing public health preparedness for such an attack. We constructed a model that compares the impact of three classic agents of biologic warfare (Bacillus anthracis, Brucella melitensis, and Francisella tularensis) when released as aerosols in the suburb of a major city. The model shows that the economic impact of a bioterrorist attack can range from an estimated $477.7 million per 100,000 persons exposed (brucellosis scenario) to $26.2 billion per 100,000 persons exposed (anthrax scenario). Rapid implementation of a postattack prophylaxis program is the single most important means of reducing these losses. By using an insurance analogy, our model provides economic justification for preparedness measures.
PMCID: PMC2627615  PMID: 9204289
17.  Biological warfare agents 
The recent bioterrorist attacks using anthrax spores have emphasized the need to detect and decontaminate critical facilities in the shortest possible time. There has been a remarkable progress in the detection, protection and decontamination of biological warfare agents as many instrumentation platforms and detection methodologies are developed and commissioned. Even then the threat of biological warfare agents and their use in bioterrorist attacks still remain a leading cause of global concern. Furthermore in the past decade there have been threats due to the emerging new diseases and also the re-emergence of old diseases and development of antimicrobial resistance and spread to new geographical regions. The preparedness against these agents need complete knowledge about the disease, better research and training facilities, diagnostic facilities and improved public health system. This review on the biological warfare agents will provide information on the biological warfare agents, their mode of transmission and spread and also the detection systems available to detect them. In addition the current information on the availability of commercially available and developing technologies against biological warfare agents has also been discussed. The risk that arise due to the use of these agents in warfare or bioterrorism related scenario can be mitigated with the availability of improved detection technologies.
PMCID: PMC3148622  PMID: 21829313
Anthrax; biological warfare agents; botulism; detection of BW agents; plague
18.  The Microbial Rosetta Stone Database: A compilation of global and emerging infectious microorganisms and bioterrorist threat agents 
BMC Microbiology  2005;5:19.
Thousands of different microorganisms affect the health, safety, and economic stability of populations. Many different medical and governmental organizations have created lists of the pathogenic microorganisms relevant to their missions; however, the nomenclature for biological agents on these lists and pathogens described in the literature is inexact. This ambiguity can be a significant block to effective communication among the diverse communities that must deal with epidemics or bioterrorist attacks.
We have developed a database known as the Microbial Rosetta Stone. The database relates microorganism names, taxonomic classifications, diseases, specific detection and treatment protocols, and relevant literature. The database structure facilitates linkage to public genomic databases. This paper focuses on the information in the database for pathogens that impact global public health, emerging infectious organisms, and bioterrorist threat agents.
The Microbial Rosetta Stone is available at . The database provides public access to up-to-date taxonomic classifications of organisms that cause human diseases, improves the consistency of nomenclature in disease reporting, and provides useful links between different public genomic and public health databases.
PMCID: PMC1127111  PMID: 15850481
19.  The Role of Internists During Epidemics, Outbreaks, and Bioterrorist Attacks 
Internists are well-positioned to play significant roles in recognizing and responding to epidemics, outbreaks, and bioterrorist attacks. They see large numbers of patients with various health problems and may be the patients’ only interaction with the medical community for symptoms resulting from infectious diseases and injuries from radiation, chemicals, and/or burns. Therefore, Internists must understand early warning signs of different bioterrorist and infectious agents, proper reporting channels and measures, various ways that they can assist the public health response, and roles of different local, state, and federal agencies. In addition, it is important to understand effects of a public health disaster on clinic operations and relevant legal consequences.
PMCID: PMC1824729  PMID: 17351853
infectious diseases; bioterrorism; public health
20.  The Role of Internists During Epidemics, Outbreaks, and Bioterrorist Attacks 
Internists are well-positioned to play significant roles in recognizing and responding to epidemics, outbreaks, and bioterrorist attacks. They see large numbers of patients with various health problems and may be the patients’ only interaction with the medical community for symptoms resulting from infectious diseases and injuries from radiation, chemicals, and/or burns. Therefore, Internists must understand early warning signs of different bioterrorist and infectious agents, proper reporting channels and measures, various ways that they can assist the public health response, and roles of different local, state, and federal agencies. In addition, it is important to understand effects of a public health disaster on clinic operations and relevant legal consequences.
PMCID: PMC1824729  PMID: 17351853
infectious diseases; bioterrorism; public health
21.  Animals as Early Detectors of Bioevents: Veterinary Tools and a Framework for Animal-Human Integrated Zoonotic Disease Surveillance 
Public Health Reports  2008;123(3):300-315.
The threat of bioterrorism and emerging infectious diseases has prompted various public health agencies to recommend enhanced surveillance activities to supplement existing surveillance plans. The majority of emerging infectious diseases and bioterrorist agents are zoonotic. Animals are more sensitive to certain biological agents, and their use as clinical sentinels, as a means of early detection, is warranted.
This article provides design methods for a local integrated zoonotic surveillance plan and materials developed for veterinarians to assist in the early detection of bioevents. Zoonotic surveillance in the U.S. is currently too limited and compartmentalized for broader public health objectives. To rapidly detect and respond to bioevents, collaboration and cooperation among various agencies at the federal, state, and local levels must be enhanced and maintained. Co-analysis of animal and human diseases may facilitate the response to infectious disease events and limit morbidity and mortality in both animal and human populations.
PMCID: PMC2289983  PMID: 19006972
22.  Rabbit and Nonhuman Primate Models of Toxin-Targeting Human Anthrax Vaccines 
The intentional use of Bacillus anthracis, the etiological agent of anthrax, as a bioterrorist weapon in late 2001 made our society acutely aware of the importance of developing, testing, and stockpiling adequate countermeasures against biological attacks. Biodefense vaccines are an important component of our arsenal to be used during a biological attack. However, most of the agents considered significant threats either have been eradicated or rarely infect humans alive today. As such, vaccine efficacy cannot be determined in human clinical trials but must be extrapolated from experimental animal models. This article reviews the efficacy and immunogenicity of human anthrax vaccines in well-defined animal models and the progress toward developing a rugged immunologic correlate of protection. The ongoing evaluation of human anthrax vaccines will be dependent on animal efficacy data in the absence of human efficacy data for licensure by the U.S. Food and Drug Administration.
PMCID: PMC539006  PMID: 15590776
23.  The Contributions of Biomedical Informatics to the Fight Against Bioterrorism 
A comprehensive and timely response to current and future bioterrorist attacks requires a data acquisition, threat detection, and response infrastructure with unprecedented scope in time and space. Fortunately, biomedical informaticians have developed and implemented architectures, methodologies, and tools at the local and the regional levels that can be immediately pressed into service for the protection of our populations from these attacks. These unique contributions of the discipline of biomedical informatics are reviewed here.
PMCID: PMC344565  PMID: 11861623
24.  Decision Theoretic Analysis of Improving Epidemic Detection 
The potentially catastrophic impact of an epidemic specially those due to bioterrorist attack, makes developing effective detection methods essential for public health. Current detection methods trade off reliability of alarms for early detection of outbreaks. The performance of these methods can be improved by disease-specific modeling techniques that take into account the potential costs and effects of an attack to provide optimal warnings and the cost and effectiveness of interventions. We study this optimization problem in the framework of sequential decision making under uncertainty. Our approach relies on estimating the future benefit of true alarms and the costs of false alarms. Using these quantities it identifies optimal decisions regarding the credibility of outputs from a traditional detection method at each point in time. The key contribution of this paper is to apply Partially Observable Markov Decision Processes (POMDPs) on outbreak detection methods for improving alarm function in the case of anthrax. We present empirical evidence illustrating that at a fixed specificity, the performance of detection methods with respect to sensitivity and timeliness is improved significantly by utilizing POMDPs in detection of anthrax attacks.
PMCID: PMC2655796  PMID: 18693857
25.  Business and public health collaboration for emergency preparedness in Georgia: a case study 
BMC Public Health  2006;6:285.
Governments may be overwhelmed by a large-scale public health emergency, such as a massive bioterrorist attack or natural disaster, requiring collaboration with businesses and other community partners to respond effectively. In Georgia, public health officials and members of the Business Executives for National Security have successfully collaborated to develop and test procedures for dispensing medications from the Strategic National Stockpile. Lessons learned from this collaboration should be useful to other public health and business leaders interested in developing similar partnerships.
The authors conducted a case study based on interviews with 26 government, business, and academic participants in this collaboration.
The partnership is based on shared objectives to protect public health and assure community cohesion in the wake of a large-scale disaster, on the recognition that acting alone neither public health agencies nor businesses are likely to manage such a response successfully, and on the realization that business and community continuity are intertwined. The partnership has required participants to acknowledge and address multiple challenges, including differences in business and government cultures and operational constraints, such as concerns about the confidentiality of shared information, liability, and the limits of volunteerism. The partnership has been facilitated by a business model based on defining shared objectives, identifying mutual needs and vulnerabilities, developing carefully-defined projects, and evaluating proposed project methods through exercise testing. Through collaborative engagement in progressively more complex projects, increasing trust and understanding have enabled the partners to make significant progress in addressing these challenges.
As a result of this partnership, essential relationships have been established, substantial private resources and capabilities have been engaged in government preparedness programs, and a model for collaborative, emergency mass dispensing of pharmaceuticals has been developed, tested, and slated for expansion. The lessons learned from this collaboration in Georgia should be considered by other government and business leaders seeking to develop similar partnerships.
PMCID: PMC1676007  PMID: 17116256

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