When to Respond
Presented by Prof. Julie Simmons Ivy
DATE: Wednesday, October 6, 2010
TIME: 11:00 AM – 12:00 PM
A Multi-Agent Stochastic Alert Threshold Model for Declaring a Disease Outbreak
Influenza pandemics are considered one of the most significant and widely spread threats to public health. In this research, we explore the relationship between local and state health departments with respect to issuing alerts and responding to a potential disease outbreak such as influenza. We modeled the public health system as a multi-agent (or decentralized) partially observable Markov decision process where local and state health departments are decision makers. The model is used to determine when local and state decision makers should issue an alert or initiate mitigation actions such as vaccination in response to the existence of a disease threat. The model incorporates the fact that health departments have imperfect information about the exact number of infected people. The objective of the model is to minimize both false alerts and late alerts while identifying the optimal timing for alerting decisions. Providing such a balance between false and late alerts has the potential to increase the credibility and efficiency of the public health system while improving immediate response and care in the event of a public health emergency. Using data from the 2009-2010 H1N1 influenza outbreak to estimate model parameters including observations and transition probabilities, computational results for near optimal solutions are obtained. In order to gain insight regarding the structure of optimal policies at the local and state levels, various model parameters including false and late alerting costs are explored.
This research is a part of the North Carolina Preparedness and Emergency Response Research Center (NCPERRC) and was supported by the Centers for Disease Control and Prevention (CDC) Grant 1PO1 TP 000296-02.
Speaker’s Bio: Julie Ivy is an Assistant Professor at North Carolina State University in the Edward P. Fitts Department of Industrial & Systems Engineering. She previously spent several years on the faculty of the Stephen M. Ross School of Business at the University of Michigan. Dr. Ivy is actively involved in INFORMS and is a past president of the Health Applications Section of INFORMS. She has co-authored more than twenty journal articles, working papers, and conference proceedings.
Dr. Ivy's primary research interests are in the mathematical modeling of stochastic dynamic systems with emphasis on statistics and decision analysis as applied to health care, manufacturing, and service environments. The focus of her research is decision making under conditions of uncertainty with the objective of improving the decision quality. Dr. Ivy's research program seeks to develop novel concepts of maintenance and monitoring policies and associated scientific theories, and apply them specifically to two important application domains: industrial and medical decision making. She has experience in medical decision making as it relates to women's health including studying breast cancer screening and treatment policy development, policies for complex patients, health disparities and modeling of the patient and physician decision problem associated with birth delivery choice.