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Dr. Madhav Erraguntla and Dr. Mark Lawley, professors in the Department of Industrial and Systems Engineering at Texas A&M University, were awarded a $1 million contract from the Defense Health Program in the U.S. Department of Defense. The researchers will work collaboratively with Knowledge Based Systems, Inc. (KBSI) to design and develop a data integration and predictive analysis system (IPAS).

“Healthcare workers want protective equipment and protocols that keep them safe and Residents want to ride the subway or bus without fear of being infected,” Lawley said. “This grant will support our ongoing research in designing the global surveillance and response systems of the 21st century with the goal of containing infectious outbreaks at their source.”

The IPAS system will enable prediction, analysis and response management of infectious diseases.

Image of Madhav Erraguntla

“The method we have developed in phase I is generic, but focuses on predicting influenza-like illness (ILI) in the U.S. and health and human services regions,” Erraguntla said. “Phase I also studied the effectiveness of Twitter-based flu signal data for predicting the disease intensity. The goal of phase II is to extend the methodology to diseases such as Ebola and the Zika virus.”

Prior knowledge of disease incidents will allow for proactive steps such as the development of medical interventions, preventive healthcare for disease hazards and containment of disease paths. The timely prediction of disease outbreaks will begin the effective coordination and mobilization of medical, human and pharmaceutical resources.

“These features will benefit many organizations such as the Center for Disease Control, National Institutes of Health, and other public health officials who can mobilize resources and develop plans and contingencies to respond to global and local disease outbreaks,” Erraguntla said.

This research is a collaborative effort between the Department of Industrial and Systems Engineering and KBSI, a company in College Station, Texas, specializing in data mining, machine learning, engineering and systems analysis, consulting and systems/software development.