Seminar on June 13, 2014



Modeling Infectious Diseases and the Effect of Early Detection


Emine Yaylali,

(Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention (CDC)) 



Infectious disease outbreaks are one of the leading causes of death in the US and around the world; they pose a major threat to public health causing human casualties and economic burden (CDC 2011). In this study, we develop mathematical models for optimizing the timing of alerting for and mitigation of infectious diseases while considering the public health structure as an integral part of this decision. First, we present a novel multi-agent stochastic model for exploring the dynamics of local and state health departments under the potential threat of an outbreak, such as a H1N1 outbreak. The model seeks to capture the relationship between local and state entities and the effect of this relationship on the optimal policy for responding to an outbreak. We model the public health system as a two-agent (or decentralized) partially observable Markov decision process (POMDP) where local and state health departments are decision makers. 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 the public health system while improving immediate response and care in the event of a public health emergency. In the second model, we consider the response of a local health department to a pertussis outbreak using discrete-event simulation. The model combines epidemiologic spread with the effects of varying levels of health alerts and the resource availability. Our results suggest that the time to initiate the response and contact tracing significantly affects the magnitude and duration of the outbreak, whereas the effect of resource level is significant when time allocated for contact tracing per nurse is short.


Short Bio: 

Emine Yaylali is a Steven M. Teutsch Prevention Effectiveness Fellow in the Division of HIV/AIDS Prevention at the Centers for Disease Control and Prevention (CDC). She earned her BS in Industrial Engineering from Bogazici University and her MS and PhD degrees in Operations Research from North Carolina State University. Her research interests include sequential decision making under uncertainty, multi-agent models, infectious disease modeling, optimization in healthcare, simulation and resource allocation models.  


All interested are cordially invited.  

DATE:  June 13, 2014 

TIME:  Friday, 15:00-16:00