TITLE:  When to
Respond: A Multi-Agent Stochastic Alert Threshold Model for Declaring a Disease
Outbreak

SPEAKER:  Julie Simmons Ivy, Associate Professor, Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University 

ABSTRACT:

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.