Infectious Disease

Tuberculosis (TB) has reemerged as a global health epidemic in recent years. Although several researchers have examined the use of space-time surveillance to detect TB clusters, they have not used genetic information to verify that detected clusters are due to person-to-person transmission. Using genetic fingerprinting data for TB cases, we sought to determine whether detected clusters were due to recent transmission.

Objective

This paper describes the utility of prospective spacetime surveillance to detect genetic clusters of TB due to person-to-person spread.

The impetus for the development of many first syndromic surveillance systems was the hope of detecting infectious disease outbreaks earlier than with traditional surveillance. Various data sources have been suggested as potential disease indicators. Researchers have analyzed many of these, including those resulting from behaviors that change due to illness, such as purchasing medications, missing school or work, and using health care call centers or the internet to obtain health information. To define the prodromal behavior of patients presenting for care of acute illnesses, we initiated a pilot survey in the emergency room and acute care clinics at Walter Reed Army Medical Center.

Objective

This study describes the results of a survey given to patients to determine if any changes occurred in their behavior secondary to the illness that could potentially be tracked and used to detect a disease outbreak.

Tuberculosis (TB) has reemerged as a global public health epidemic in recent years. TB remains a serious public health problem among certain patient populations, and is prevalent in many urban areas. The World Health Organization estimates that approximately nine million individuals will develop active TB disease and more than two million will die from TB. The global burden of TB remains enormous, and will likely rank high among public health problems in the coming decades. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. In this study, we used the space-time scan statistic to identify where and when the prevalence of TB is high in Fukuoka Prefecture. The ability to detect disease outbreaks is important for local and national health departments to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Because the statistic meets these needs completely, results that are effective and practical for public health officials are expected from this study.

To evaluate the potential of using the sales of Over the Counter (OTC) medicines for early detection of infections of public health concern, retrospective analysis of the sales of OTC common cold medications used for influenza-like illness (ILI) has been carried out in Japan since 2003. This presentation assess correlations and predictability of OTC sales to ILI for 2004-05 influenza season and compares with the results from 2003-04 season to discuss on robustness and versatility of OTC sales surveillance.

One of the most important goals of disease surveillance is to identify the "what" and "when" of an epidemic. Influenza surveillance is made difficult by inconsistent laboratory testing, deficiencies in testing techniques, and coding subjectivity in hospital records. We hypothesized that respiratory diseases other than influenza may serve as a useful proxy for this infection in pediatric populations, due to similarities in the seasonal characteristics of these illnesses.

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.