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Whilst the sensitivity and specificity of traditional laboratory-based surveillance can be readily estimated, the situation is less clear cut for syndromic surveillance. Syndromic surveillance indicators based upon presenting symptoms, chief complaints or preliminary diagnoses are designed to... Read more

Content type: Abstract

Research evaluating the use of spatial data for surveillance purposes is ongoing and evolving. As spatial methods evolve, it is important to characterize their effectiveness in real-world settings. Assessing the performance of surveillance systems has been difficult because there has been a... Read more

Content type: Abstract

The U.S. Defense Threat Reduction Agency (DTRA) is funding multiple development efforts directed at enhanced platforms to support bio-surveillance analysts under their Bio-surveillance Ecosystem (BSVE) program. These efforts include well-integrated user interface systems and advanced algorithmic... Read more

Content type: Abstract

Syndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in... Read more

Content type: Abstract

Immediately following September 11, 2001, the District of Columbia Department of Health began a syndromic surveillance program based on emergency room (ER) visits. ER logs are faxed on a daily basis to the health department, where health department staff code them on the basis of chief complaint... Read more

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In disease surveillance, an outbreak is often present in more than one data type. If each data type is analyzed separately rather than combined, the statistical power to detect an outbreak may suffer because no single data source captures all the individuals in the outbreak. Researchers, thus,... Read more

Content type: Abstract

Effective responses to epidemics of infectious diseases hinge not only on early outbreak detection, but also on an assessment of disease severity. In recent work, we combined previously developed ARI case-detection algorithms (CDA) [1] with text analyses of chest imaging reports to identify ARI... Read more

Content type: Abstract

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... Read more

Content type: Abstract

The 2009 H1N1 novel flu pandemic demonstrates how a rapidly spreading, contagious illness can affect the world’s population in multiple ways including health, economics, education, transportation, and national security. Pandemic disease and the threat of bio-terrorism are prompting the need for... Read more

Content type: Abstract

The standard approaches to simulation include solving of differential equation systems. Such approach is good for obtaining general picture of epidemics (1, 2). When the detailed analysis of epidemics reasons is needed such model becomes insufficient. To overcome the limitations of standard... Read more

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National Syndromic
Surveillance Program

Email:nssp@cdc.gov

The National Syndromic Surveillance Program (NSSP) is a collaboration among states and public health jurisdictions that contribute data to the BioSense Platform, public health practitioners who use local syndromic surveillance systems, Center for Disease Control and Prevention programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

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