Infectious Disease

Objective: To investigate seasonal patterns of gastrointestinal (GI) illness among children and adults.

This paper describes a cluster of influenza-like illness (ILI) prospectively identified through emergency department (ED) syndromic surveillance (SS).

Objective: Emerging and re-emerging infectious diseases (EID/REID) involve large populations at risk and thus they might lead to rapidly increasing cases or case fatality rates. Living in this global village, cross-country or cross-continent spread has occurred more frequently in recent decades, implying that epidemics of any infectious disease can expand from local to national to international if control efforts are not effective.

The objective of this research is to describe infectious disease surveillance on military population on board ships.

Developing and evaluating outbreak detection is challenging for many reasons.  A central difficulty is that the data the detection algorithms are “trained” on are often relatively short historical samples and thus do not represent the full range of possible background scenarios.  Once developed, the same dearth of historical data complicates evaluation.  In systems where only a count of cases is provided, plausible synthetic data are relatively easy to generate.  When precise location data is available, simple approaches to generating hypothetical cases is more difficult.

Advances in epidemiological modeling have allowed for increasingly realistic simulations of infectious disease spread in highly detailed synthetic populations. These agent-based simulations are capable of better representing real-world stochastic disease transmission process and thus show highly variable results even under identical initial conditions. Due to their ability to mimic a wide range outcomes and more fully represent the unknowns in a system, models of this class have become increasingly used to help inform decisions about public policies about hypothetical situations (eg pandemic influenza [1]).  This characteristic also makes them a powerful tool to represent the processes that create surveillance information.

Objective

Developing and evaluating detection algorithms in noisy surveillance data is complicated by a lack of realistic noise, meaning the surveillance data stream when nothing of public health interest is happening. These jobs are even more complex when data on the precise location of cases is available. This paper describes a methodology for plausible generation of such noise using agent-based models of infectious disease transmission based on highly resolved dynamic social networks.

A significant research topic in biosurveillance is how to group individual events—such as single emergency department (ED) visits and sales of over-thecounter healthcare (OTC) products—into counts of “similar” events. For OTC products, the goal is to find categories of individual products that have superior outbreak detection performance relative to categories that biosurveillance systems currently monitor. We have described a method to identify OTC categories that correlate more highly with disease activity than existing categories.1 However, it is an open question whether a category that correlates more highly—or according to some other model has a higher ‘association’—with disease activity than an existing category necessarily has superior detection performance. Here, we evaluate whether a linear regression procedure that clusters OTC products based on how well they ‘explain’ ED visits for influenzalike illness (ILI) can find categories with superior outbreak-detection performance for influenza.

Objective

To develop a procedure that identifies product categories with superior outbreak detection performance.

Although norovirus (NoV) is the most common cause of acute gastroenteritis (ewinter vomiting diseaseí), its contribution to mortality remains unknown and may be an unrecognized problem [1]. In Europe a genetic shift in circulating NoV strains was observed in 2002 which coincided with an unusually high number of NoV outbreaks in all but one country participating in the European NoV-surveillance network [2]. Covering a time period which included this outbreak peak, we used general practitioner (GP), hospital, and death-cause data in combination with NoV surveillance data to explore the association between NoV outbreaks and morbidity and mortality.

Classical disease monitoring in local public health jurisdictions has been based on a list of “notifiable diseases”, more or less consistent from state-to-state.  While laboratories’ compliance with this requirement is, in general, excellent, clinician reporting is extremely poor [1].  In most circumstances, laboratory reporting is inherently delayed (perhaps by weeks), and most leaders in infectious disease and bioterrorism believe that recognition of abnormal spatiotemporal patterns within hours is essential [2].  Syndromic surveillance systems based on analysis of statistical aberrations in diagnosis code, chief complaint, or analysis of other data streams have been proposed and tested, but have largely failed to meet criteria of timeliness, sensitivity and specificity [3].  In addition, the vast majority of syndromic surveillance systems do not include veterinary surveillance, which may be important given that the vast majority of diseases of human public health importance are zoonotic in origin.  Thus, we have tested the hypothesis put forward by Henderson that “the astute clinician” can serve as the best early-warning indicator [4], with minimal demands on clinician time while simultaneously providing situational awareness to the broad community of health care providers and political decision makers who require such information.

Objective

It is widely agreed that "situational awareness" in disease surveillance is essential for intervening early in an infectious disease (or intoxination) outbreak. We report on 3.5 years of experience of a clinician-based system in a 25,000 square mile area of northwest Texas, a mixed urban, semi-rural and agricultural setting.

The City of Atlanta, volunteer organizations, and the faith community operate several homeless shelters throughout the city. Services available at these shelters vary, ranging from day services, such as meals, mail collection, and medical clinics, to overnight shelter accommodations. In addition to the medical clinics available at these facilities, the Atlanta homeless population also utilizes emergency departments in Fulton County for their health care needs.

Objective

This paper describes a cluster of Streptococcus pneumoniae infections identified through emergency department syndromic surveillance.

Animals continue to be recognized as a potential source of surveillance data for detecting emerging infectious diseases, bioterrorism preparedness, pandemic influenza preparedness, and detection of other zoonotic diseases. Detection of disease outbreaks in animals remains mostly dependent upon systems that are disease specific and not very timely. Most zoonotic disease outbreaks are detected only after they have spread to humans. The use of syndromic surveillance methods (outbreak surveillance using pre-diagnostic data) in animals is a possible solution to these limitations. The authors examine microbiology orders from a veterinary diagnostics laboratory (VDL) as a possible data source for early outbreak detection. They establish the species representation in the data, quantify the potential gain in timeliness, and use a CuSum method to study counts of microorganisms, animal species, and specimen collection sites as potential early indicators of disease outbreaks. The results indicate that VDL microbiology orders might be a useful source of data for a surveillance system designed to detect outbreaks of disease in animals earlier than traditional reporting systems.