Christa Pettie

Participant: PROMISE AGEP Research Symposium


Christa D. Pettie

Department: Reliability Engineering

Institution: University of Maryland College Park (UMD)



Modeling Syndromic Surveillance and Outbreaks in Subpopulations

This research is motivated by the need to help poor or resource limited communities by enhancing and/or refining their syndromic surveillance (SS) systems. Public health agencies and academic researchers have developed and implemented Syndromic Surveillance (SS) systems as a tool to detect an outbreak using pre-diagnostic data. SS systems are a supplement to public health surveillance that rely on data gathering and pattern recognition. This SS data is input into a detection algorithm which is preprogrammed with alert thresholds.

It is unrealistic to gather public health data from every available source, so gathering information about only some subpopulations may be a desirable option; however, this raises questions about the differences between subpopulations. Specifically, organizing the collected SS data into predetermined subpopulations (separated by population characteristics such as age or location) allows researchers to compare how well the data correlates to the real world disease progression. This research aims to look at reports of Influenza-like-illness (ILI) in various subpopulations and how the behavior of these subpopulations represent the complete outbreak. The first step of the research process is to understand how SS is used in environments with varying levels of resources and what gaps are present in various SS evaluation techniques. Epidemic population models and applications are assessed, specifically the Susceptible Infected Recovered “S-I-R” model. Modeling will help to understand the risk presented by inaccurate population representation. Particle filtering techniques are evaluated to present an appropriate predictive SS model. This information will inform decision making for health departments using SS systems that rely on fewer resources. It will also show what type of subpopulation organization and data analysis will best represent actual disease behavior and SS data sets.



Christa Pettie (Rogers) earned her Bachelors of Science degree in Electrical Engineering from Alabama A&M University. She graduated with her Master’s degree in Systems Engineering from UMD-College Park in December of 2014. As a member of the 2012 cohort of the LSAMP Bridge to Doctorate fellowship program, she continued her graduate education in pursuit of her PhD in Reliability Engineering.  Dr. Jeffrey Herrmann of the Mechanical Engineering Department has been advising her since she began working on her Master’s degree. Her research interests include Information Diffusion, Data Analytics, and Evaluation of Syndromic Surveillance Systems. She had the honor of being a GEM Consortium M.S. and PhD Fellow working with Aerospace Corporation. Currently, she works at the Test Resource Management Center supporting the team a cyber security analyst.



Master’s level research was focused on analyzing large Twitter data sets to evaluate how tweets can be used to benefit emergency management. The spread of information was visualized and analyzed to determine how far and how fast the information spread through a network of connected users. The results show how topic based information diffuses throughout a network and how content analysis indicate the importance of specific topics to participants. As a PhD student, the focus shifted to understand Syndromic surveillance (SS) systems. These SS systems collect and analyze pre-diagnostic data to monitor syndromes, detect diseases, and warn of bioterrorist attacks. Evaluating a SS system is an ongoing activity that identifies the system’s strengths and weaknesses, which can guide efforts to improve an existing SS system. The approaches for evaluating SS systems use a variety of evaluation attributes to describe their performance. A literature review was conducted to discuss SS systems, describe the approaches for evaluating SS systems, and examine the evaluation attributes used in these approaches. The review concludes by identifying the limitations of previous evaluation approaches and the opportunities for improving the techniques for measuring evaluation attributes. The literature review helped to narrow the focus for the PhD research effort. The current focus is understanding how subpopulations affect one another when seeking care. By analyzing SS data, one can understand the interactions of the subpopulations and how to model their behavior. Identifying population characteristics can determine leading indicators of disease behavior in various subpopulations. This data analysis will lend itself to a predictive model for each subpopulation.



  1. Richardson, G., Schmitt, K., Covert, M., & Rogers, C. (2015). Small Satellite Trends 2009-2013.
  2. Pettie, Christa, and J.W. Herrmann, Information Diffusion: A Study of Twitter During Large Scale Events, Industrial and Systems Engineering Research Conference, Nashville, Tennessee, May 30-June 2, 2015.


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