ISyE Assistant Professor Turgay Ayer received a prestigious National Science Foundation CAREER award to study the design of optimal population-based disease surveillance policies and treatment prioritization strategies for chronic infectious diseases in resource-limited settings.

The project particularly focuses on the optimal control of hepatitis C Virus. This study will use data-driven mathematical models to underpin some of such complex health policy decisions.  From an educational standpoint several high school and undergraduate students will be engaged into these research activities.

The abstract of Ayer’s grant reads:

The research objective of this Faculty Early Career Development (CAREER) Program award is to study the design of optimal population-based disease surveillance policies and treatment prioritization strategies for chronic infectious diseases in resource-limited settings. The project particularly focuses on the optimal control of hepatitis C Virus (HCV), an epidemic affecting nearly 2 percent of the entire US population, fastest-growing cause of cancer-related deaths in the US. If successful, results from this research may improve hepatocellular carcinoma surveillance and HCV treatment prioritization decisions in practice and can inform prevention and treatment decisions at the national level in terms of guideline development. Under this project, in collaboration with several medical researchers, a PhD student will be rigorously trained to apply systems modeling methodologies to the healthcare area. In addition, several high school and undergraduate students will be engaged into these research activities. The results of the project will be widely disseminated to both engineering and medical communities through paper publications and conference presentations.

Many health policy questions, such as optimal disease surveillance and treatment prioritization decisions for chronic infectious diseases, cannot be answered through the traditional research methods in medicine, such as randomized control trials and observational studies. This study will use data-driven mathematical models to underpin some of such complex health policy decisions. The resulting stochastic optimization models are non-standard, large scale, and computationally challenging. Therefore, this study will either create new approaches or extend the existing methodology to account for the critical features of the disease epidemiology, resource limitations, and management strategies. An important component of the research is careful parameterization and calibration of the models using some of the largest datasets in the nation. While this project mainly focuses on hepatocellular carcinoma and HCV, the themes of this study can be generalized and applied to several other disease management problems.

Ayer conducts research on stochastic modeling and optimization, with applications in medical decision making, health policy, healthcare and humanitarian operations.