Dod

Primary for DOD

Primary for DOD

Primary for DOD

Primary for DOD

Transit system

Primary for system

Supportive

CDC indicates Centers for Disease Control and Prevention; WHO, World Health Organization; FDA, Food and Drug Administration; EPA, Environmental Protection Agency; USDA, U.S. Department of Agriculture; DHS, Department of Homeland Security; DOD, Department of Defense; and Transit, airlines and mass transit systems.

CDC indicates Centers for Disease Control and Prevention; WHO, World Health Organization; FDA, Food and Drug Administration; EPA, Environmental Protection Agency; USDA, U.S. Department of Agriculture; DHS, Department of Homeland Security; DOD, Department of Defense; and Transit, airlines and mass transit systems.

4.3. Time Critical

Early detection of outbreaks is perhaps the most important requirement for a biosurveillance system. Morbidity and economic loss accumulate rapidly, beginning with the first sick individual. In a worst-case scenario, such as a surreptitious aerosol release of the organism Bacillus anthracis by a terrorist on a large city, hundreds of thousands of individuals would be exposed nearly simultaneously to a biological organism that is lethal and fast acting (Figure 1.2).

The implications of time criticality are profound for the design of biosurveillance systems. Reducing the time delay between the start of an outbreak and its detection is a key goal of research and development in biosurveillance. This requirement for early detection pervades the design of new biosurveillance systems, which are designed to collect and analyze new types of surveillance data in real time.

4.4. Probabilistic

Because early detection is important, biosurveillance increasingly involves analysis of novel types of data, such as sales of diarrhea remedies and numbers of visits to emergency departments for respiratory complaints. These data are more difficult to interpret than are definitive diagnoses (e.g., a patient has anthrax) because the former are not diagnostically precise. Detection of an outbreak depends on noticing an increase in the numbers of sales or visits relative to usual levels.

The challenge of early detection is that most outbreaks present weaker signals (increases) in the data streams earlier in the outbreak than they do in the middle of the event or after the event. This means that earlier detection requires the detection of smaller signals.

Early (and reliable) detection is necessarily probabilistic because early detection requires detection when signals are small and when few signal sources may yet be active. The goal is to detect a case or an outbreak before the signals are large enough and present in enough data streams for detection to be 100% certain.

The assessment of the probability that an anomalous event is occurring places demands on a biosurveillance system and its algorithms.Analytic techniques must handle multiple, independent, yet correlated data streams. The need for probabilistic detection from multiple data streams strongly suggests the need for a detection system based on Bayesian inference. A well-organized Bayesian approach allows for rational combination of many small indicators into a big picture. We discuss Bayesian methods in detail in this book.

4.5. Decision Oriented

Biosurveillance does not exist in a vacuum. Its purpose is to collect and analyze information that people use to guide decision making and action. Biosurveillance personnel make decisions under time pressure. They make decisions based on incomplete and uncertain information. Early in the course of an outbreak,

FIGURE 1.2 Hypothetical cumulative mortality from a surreptitious aerosol release of Bacillus anthracis by a terrorist on a major city. Such a release could expose hundreds of thousands of individuals nearly simultaneously to a biological organism that is lethal and fast acting. The window of opportunity to detect this event and administer antibiotics to those exposed is brief. (We estimated the shape of this curve from published data on the incubation period and mortality observed in the 1979 release of B. anthracis [Kirov strain] from Soviet Biological Weapons Compound 19 described in Chapter 2 and in the 2001 U.S. postal attacks).

FIGURE 1.2 Hypothetical cumulative mortality from a surreptitious aerosol release of Bacillus anthracis by a terrorist on a major city. Such a release could expose hundreds of thousands of individuals nearly simultaneously to a biological organism that is lethal and fast acting. The window of opportunity to detect this event and administer antibiotics to those exposed is brief. (We estimated the shape of this curve from published data on the incubation period and mortality observed in the 1979 release of B. anthracis [Kirov strain] from Soviet Biological Weapons Compound 19 described in Chapter 2 and in the 2001 U.S. postal attacks).

they may not know the cause of the illness in patients, the number of affected individuals in the community, or the source of the infections. Nevertheless, they must form conjectures and hypotheses based on the available information and make decisions about how to direct resources to investigate, treat, and even quarantine individuals.

Psychological research has shown that human decision makers perform most poorly under conditions of uncertainty and time pressure. The effect of uncertainty on decision making can be profound as demonstrated by tabletop exercises (see Inglesby et al., 2001; O'Toole et al., 2002). Fortunately, the sciences of decision making and of economics provide methods to improve decision making under uncertainty. These methods elucidate the tradeoff between the risk of waiting and the cost of taking the wrong action. Biosurveillance organizations can use these methods to develop guidelines for such decision situations, or they can build these methods into computer systems that provide decision support to frontline personnel facing specific decisions. We discuss the science of decision making and economic studies in detail in Part V of this book.

4.6. Data Intensive

It is perhaps obvious, but worth stating, that biosurveillance is not a vaccine or drug that can save lives directly. Biosurveillance is a process that collects and analyzes data to guide the application of vaccines, drugs, quarantine, and other disease control strategies that can save lives.

The role of biosurveillance in disease control is to gather and process data—to collect, communicate, and analyze data. A chain of data-processing steps links raw surveillance data to "actionable information,'' as illustrated by Figure 1.3.The link between biosurveillance and response occurs at the point that biosurveillance personnel make decisions to act.

Each step in the chain may involve information systems and people-all of which must function effectively if an outbreak is to be quickly detected, characterized, and controlled. Any breakdown or delay in the chain can reduce the efficacy of the biosurveillance system and its ability to contribute, ultimately, to the prevention of mortality and morbidity.

4.7. Dependent on Information Technology

Societies, especially cities, have conducted biosurveillance in some form for centuries, so it is self-evident that organizations can conduct biosurveillance without the assistance of information technology. However, information technology is of increasing importance in biosurveillance because it can address the problem of time criticality. Information technology has the potential to speed up and improve the accuracy of almost every aspect of the biosurveillance process. Information technology can assist or fully automate data collection, transmission, storage, and communication. It can assist or partially automated even the most cognitively challenging steps-patient diagnosis, outbreak detection, outbreak characterization, and decision making.

4.8. Knowledge Intensive

Biosurveillance is a knowledge-intensive process. To diagnose a patient with an infectious disease, a physician must be familiar with the symptoms, signs, radiological characteristics, and laboratory tests for hundreds of diseases. This information can fill several large textbooks and requires years of study to master. A veterinarian must master an even larger body of knowledge as veterinary medicine concerns large numbers of animal species. An epidemiologist must similarly master a large body of knowledge, including a subset of human and animal diseases, as well as the subject of epidemiology, which concerns patterns of disease transmission. This knowledge also fills large textbooks, as does the knowledge required for the conduct of infection control in hospitals.

The human ability to master and apply large bodies of knowledge varies but, in general, is imperfect. Fortunately, there are technologies such as diagnostic expert systems and knowledge-based systems that professionals in many fields use to extend the range of their competencies. We discuss these technologies in Chapter 13.

4.9. Complex

The biosurveillance process is complex. There is complexity inherent in a system that distributes its functions over a large number of individuals and organizations. There is cognitive

Physician examines child with measles

Physician correctly diagnoses measles

Physician remembers to notify health depa rtment

Physician files report using Web interface internet transfers report to health department

Health department staff log into system

Health department staff make decisions

The response actions occur

BENEFIT Reduction in mortality or morbidity

FIGURE 1.3 The indirect connection between information and benefit. A physician evaluates a child with measles. The physician must correctly diagnose the patient and remember to notify the local department of health. If the physician uses a Web-based disease reporting system, the local computer, the Internet, and the health department information systems must be functioning. Staff must review the report and make correct decisions about collection of additional information and appropriate control measures to institute. At some time later, the benefit of the information is realized by control of the outbreak and reduction in the level of morbidity and possibly mortality.

complexity inherent in reasoning and taking decisions from partial and uncertain information. There is complexity due to the number of biological agents that can cause disease and the myriad ways that they can present as outbreaks (e.g., airborne pattern, food-contamination pattern, subway system— contamination pattern, mail system-contamination pattern, and building-contamination pattern).

This complexity makes it difficult to design a biosurveillance system. In the past, organizations and people have managed the complexity of biosurveillance by specialization and prioritization. Specialization is a divide-and-conquer technique in which people or organizations manage complexity by, for example, creating separate biosurveillance capabilities for communicable diseases and for water-borne diseases. Specialization is not without its drawbacks, as demonstrated by the existence of many specialized information systems that cannot interoperate. Prioritization refers to paying more attention to certain diseases, which is a polite way of saying that, to some extent, people manage complexity by sometimes ignoring it.

One of the key benefits of information technology in professional domains, such as engineering, medicine, and biosurveillance, is that it can help to manage complexity for the professional working in that field. By managing both data and knowledge, information technology can make previously impossible or Herculean tasks possible. Information technology is a way of managing the ever-increasing complexity of biosurveillance without relying as heavily on specialization and prioritization. We discuss information systems that manage data throughout this book, and we examine systems that assist biosurveillance personnel with analytic and cognitive tasks in Parts III and V.

5. BIOSURVEILLANCE SYSTEMS_

The definitions of biosurveillance, disease surveillance, and public health surveillance all include the word systematic. A system is any organized way of doing something. Because of the numbers of individuals, organizations, and steps in the biosurveillance process, a basic property of biosurveillance is that it is systematic. A biosurveillance system may be manual, automated, or, more commonly, a mixture of manual and automated processes. Biosurveillance systems of all types exist. The systematic, process-oriented nature of biosurveillance can be represented diagrammatically, as illustrated in Figure 1.4, which represents a highly automated system. The developers of manual biosurveillance systems often represent the organization and flow of information in a system diagrammatically as well. The diagrammatic representation of biosurveillance systems finds its fullest expression in the concept of an architecture for a biosurveillance system. We discuss architecture in detail in Chapter 33.

6. SCIENTIFIC FOUNDATIONS OF BIOSURVEILLANCE_

The scientific foundations of biosurveillance have evolved over the centuries, parallel to advances in medicine, microbiology, veterinary science, laboratory science, epidemiology, mathematics, and many other fields.

Over the past 5 years, the scientific foundations of biosurveillance have changed rapidly. Bioterrorism and the threat posed by emerging infectious diseases triggered this change by creating a new requirement—very early detection of disease outbreaks (Wagner et al., 2001). New techniques are being introduced rapidly from diverse scientific fields and

Biosurveillance Brief
FIGURE 1.4 A generic biosurveillance system.The key elements of a biosurveillance system are data sources,a database, analysis,and decision making.

include mathematical models of the process of medical diagnosis and of decision making, as well as mathematical models of the process of "epidemic'' diagnosis.

Perhaps the most important new techniques can be traced to Ledley and Lusted (1959), who first introduced the idea that medical diagnosis and decision making could be modeled mathematically. This idea spawned a large body of research about how physicians use diagnostic information, how a computer could represent medical knowledge, and how to construct computer programs that perform medical (and veterinary) diagnosis. Approximately 5 years ago, it became apparent that these same techniques could be applied to epidemiological diagnosis and decision making (Wagner et al., 2001). These techniques represent new core subject matter for the professional training of researchers and practitioners. We discuss these new approaches in Parts III and V.

This recent expansion of the scientific foundations of biosurveillance has been abrupt and large. The philosopher of science Kuhn termed such changes paradigm shifts (Kuhn, 1962). A paradigm shift is associated with changes in the cur-riculums of professional schools, the structure and functions of organizations, the appearance of new journals, the workforce, and the tables of contents in standard textbooks. There is evidence of such changes in biosurveillance (Logan-Henfrey, 2000; Yasnoff et al., 2001;Wagner, 2002).

7. OPEN RESEARCH PROBLEMS IN BIOSURVEILLANCE

Biosurveillance has been an active area of research since the seminal work of John Snow (1855). However, the recent requirement for very early detection caused a change in the direction (and intensity) of research. Researchers now focus on improving the timeliness and accuracy of case detection, outbreak detection, and outbreak characterization. Researchers are developing more rapid and accurate diagnostic tests, methods for sensing microbes or their effects in the environment, new detection algorithms that can extract maximum signal from early but noisy data, and research to identify types of surveillance data that provide an earlier indication of an outbreak.

Examples of the questions that research attempts to answer related to detection of individual cases of disease include the following: What are the optimal data to collect to detect a case of disease X? What is the optimal analytic method to detect a case of disease X? What are the sensitivity, specificity, and timeliness of the current best methods for detecting a case of disease X?

Research pursues the same set of questions for outbreaks of disease but also pursues additional questions, such as the following: When can we expect to detect an outbreak of disease X that affects 1% of the population by analysis of some class of surveillance data? What is the smallest outbreak of X that we can detect?

8. THE ROLE OF BIOSURVEILLANCE IN BIODEFENSE_

Biodefense is a set of activities that together function to provide security against disease due to biological agents. Biosurveillance is one of these activities—along with sanitation, vaccination, quarantine, intelligence, interdiction (of terrorists and materiel), forensic science, and control of technologies used to create biological weapons.

Many organizations, in addition to biosurveillance organizations, play a role in biodefense, including governmental public health (in its response role), intelligence agencies, the police, the military, and pan-national organizations, such as the World Health Organization.

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