Public Release: 

NISS to work on syndomic surveillance project for NSF and DTRA

National Institute of Statistical Sciences

The National Science Foundation (NSF) and the Defense Threat Reduction Agency (DTRA) have awarded $664,019 to the National Institute of Statistical Sciences (NISS) for collaborative research to develop Bayesian methods for syndromic surveillance. The research focuses on use of conditionally auto regressive (CAR) models to provide quantified estimates of the probability that a disease is present in a particular location, on characterization of associated uncertainties, and on computational implementation at a nationwide scale.

The NISS project is one of ten supported by NSF and DTRA under a jointly funded program entitled Algorithms for Threat Detection (ATD). Collaborative awards were also given to Clemson University, the University of Georgia and the University of South Carolina to work on the project.

According to the Centers for Disease Control and Prevention (CDC), syndromic surveillance uses health-related data, such as hospital emergency room reports, that precede diagnosis and signals a sufficient probability of a case or an outbreak to warrant further public health response. This method is also now used by public health officials to detect outbreaks associated with natural causes or bioterrorism.

The research that will be conducted will help DTRA to develop technology for controlling and reducing the threat from biological and chemical attack. If a biological attack were made in the United States, early detection would also save millions of lives.

The results will also help with earlier detection of new diseases. By identifying a disease early, such as avian flu, or the next strain of H1N1, for example, health officials can help thwart the onset of a pandemic.

Researchers will also look at the intellectual issues such as scalability, complex dependences in the data, covariates, temporal and spatial variations, low quality data and how to minimize false positives.

The principal investigators involved in the research include: Alan F. Karr, Director of NISS, David Banks, Professor of Statistical Science at Duke University, Gauri Datta, Professor of Statistics at University of Georgia, James Lynch, Professor of Statistics at the University of South Carolina, and Francisco Vera, Assistant Professor of Mathematical Sciences at Clemson University. Vera was also a joint postdoctoral fellow at NISS and the Statistical and Applied Mathematical Sciences Institute (SAMSI) in 2005-06, during which he participated in the SAMSI program on National Defense and Homeland Security and NISS research on data confidentiality. Frank Zou, postdoctoral fellow at NISS, is also a member of the project team.


About NISS

The National Institute of Statistical Sciences was established in 1990 by the national statistics societies and the Research Triangle universities and organizations, with the mission to identify, catalyze and foster high-impact, cross-disciplinary and cross-sector research involving the statistical sciences. NISS is dedicated to strengthening and serving the national statistics community, most notably by catalyzing community members' participation in applied research driven by challenges facing government and industry. NISS also provides career development opportunities for statisticians and scientists, especially those in the formative stages of their careers. NISS is located in Research Triangle Park, North Carolina.

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