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BioComplex Lab: Journal Paper Accepted

10:50 AM on November 2, 2012

Congratulations to Srividhya Venugopal (PhD student) and Evan Stoner (BSc student) for for having their paper entitled "Understanding Organ Transplantation in the USA using Geographical Social Networks" has been accepted for publication at the Journal of Social Network Analysis and Mining (SNAM) published by Springer Verlag.

This paper is a collaboration between our group and the group from Martin Cadeiras, MD, PhD from UCLA.

Srividhya Venugopal, Evan Stoner, Martin Cadeiras, and Ronaldo Menezes


As of December 2011, 110,629 Americans are waiting for an organ transplant and yet only 28,664 people received organ transplants in 2010. This fact alone demonstrates the USA is facing an organ shortage crisis. Added to this, there is strong evidence that the organs made available are not being used efficiently, with 20% of them going unused (for kidneys). It is tempting to investigate how new allocation policies could be implemented, but it would be more prudent to first understand the structure of the organ donation system in the USA. In spite of availability of data on transplants, to our knowledge no proper analysis has been done using the available data. This paper looks at organ transplantation data and what its structure may reveal about the allocation process currently in place. In order to structure the data, we used techniques from network sciences to build a network of locations (henceforth called a geographical social network, GSN) representing all transplants in the USA since 1987---locations represent states or zip codes in the USA. This social structure is then analyzed using techniques from network sciences to bring clarity to the organ donation process. One of the main items in organ donation policies is the issue of locality which argues that allocation mechanisms should try to prioritize patients who are near the location where the organ became available. This is an important issue because the longer the organ takes to be transplanted the worse its quality. We show that network science techniques may shed some light on this process and demonstrate inconsistencies across different organs.