The MIDAS Webinar Series features research by MIDAS members, and is open to the public.
Date: Friday, April 5, 2024 (rescheduled)
Speaker: Dr. Sherrie Xie
Bio:
Sherrie Xie is a post-doctoral research fellow in infectious disease epidemiology in the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania. Her research focuses on applying computational methods to improve the control of infectious diseases, such as canine rabies and multi-drug resistant bacterial infections, and to better understand the spread of bed bugs in urban environments.
Abstract:
The effective delivery of vaccines is a cornerstone of public health emergency preparedness. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or queues, which discourage individuals from waiting to be vaccinated and can thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine delivery. We developed an algorithm that integrates queueing theory with a classic spatial optimization framework to optimize the placement of vaccination sites for a mass dog vaccination campaign in Arequipa, Peru and compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to that obtained from a queue-naive version of the same algorithm. The optimization of vaccination site placement via the queue-conscious algorithm resulted in a 9-19% decrease in attrition and a 1-2% increase in vaccination coverage compared to the queue-naive algorithm. While queue-conscious optimization yielded modest gains in vaccination coverage, the negative cascade that may result from attrition highlights the importance of considering queue formation in the placement of vaccination sites.