Spread of HIV and HBV in Colorado Springs, CO
http://www.sciencedirect.com/science/article/pii/0277953694903026
This scientific study sought to determine whether it was possible to predict the spread of physical diseases through the characteristics of the network consisting of persons in a community. By the end of the paper, it was concluded that this was possible. To do this, A. S. Klovdahl et. al. collaborated to study the potential spread of HIV and HBV in a network of prostitutes, injecting drug users (IDU), and their associates in Colorado Springs, CO, an urban setting. The experimenters selected their subjects and determined their contacts. Their contacts were separated into “close personal contacts,” which would include those who the subjects would share meals, share homes, have sexual contact with, etc, and “other linked persons,” which included contacts excluded from the “close personal contacts.” From there, the network was constructed by dividing the participants in mutual exclusive and exhaustive categories based on their interconnection. This network involved 36 with no connections past their personal connections, 3 dyads, 1 triad, 1 4-cluster, 1 6-cluster, and 1 8-cluster, and 600 others not included in these smaller structures, but involved in the network. Given the created network, the researchers found the density of interconnections and reachability for the subjects. It was found that within the core of the connected region, everyone was within 6 steps of being reached by the HIV, with an average step of 3. Everybody else was less than 7 steps away from the virus.
The paper proposes the use of creating structural models of networks to predict how diseases will spread in a population. The paper uses techniques we have studied in class. From participants and their associates, the researchers created nodes in their model. From the nature of their relationship, the researchers labeled the links as either strong ties or weak ties. From an analysis of how these ties laid out between the participants, the researchers then found smaller subsections in the larger network. Then, from both graphical analysis and mathematical density analysis, it was determined whom in the network were most at risk and the extent to which the disease would spread. These are all techniques we have learned in class, applied to real lives. With such an analysis, preventative measures could be taken to suppress or even cut off the spread of this disease in this population before it starts to spread.