New immunization strategy better in handling epidemics, computer viruses
New immunization approach fragments the population to be immunized into many connected clusters of equal size; by creating equal-size clusters, doses do not have to be “wasted” on isolating very small clusters, as in the traditional targeted strategy
Researchers have developed a new immunization strategy which requires up to 50 percent fewer immunization doses compared with the current most efficient strategy. The new strategy could be used to prevent the spread of human epidemics and computer viruses, and it applies to a wide variety of networks. The new method, called the “equal graph partitioning” (EGP) immunization strategy, is being proposed by a team of scientists from Boston University, Bar-Ilan University in Ramat-Gan, both in Israel, and Stockholm University. Their study is published in a recent issue of Physical Review Letters.
In real life, the number of immunization doses is often limited or expensive, so a strategy that requires fewer doses could be very useful. As the researchers explain, the question of how to immunize a network with a minimum number of doses is mathematically equivalent to asking how to fragment a network with a minimum number of node removals. Lisa Zyga writes in Physorg that, in this sense, the new EGP strategy works differently than the conventional “targeted strategy.” The main idea of the targeted strategy is to rank the importance of nodes based on how well-connected they are. Then, nodes are removed, starting with those of highest importance, until the network becomes fragmented. In the adaptive targeted strategy, node importance is recalculated after each iteration.
The main idea of the EGP method, on the other hand, is to fragment the network into many connected clusters of equal size. By creating equal-size clusters, doses do not have to be “wasted” on isolating very small clusters, as in the targeted strategy. “Disease cannot spread over the boundary of clusters, which means it will be contained in a single cluster,” co-author Yiping Chen of Boston University told PhysOrg.com. “Thus, the best strategy would be making result clusters as small as possible. Among all the clusters, the largest cluster is most important, as most disease occurs here and the spread can be broad. Thus, we need to minimize the size of the largest cluster. Because the total immunization doses are fixed, we have to save shots from small clusters to make the largest one smaller. Intuitively, the best strategy for this is to fragment the network into equal-size clusters.”
The scientists used an algorithm called nested dissection to find the minimum number of nodes to be removed in order to separate a given network into two or more equal-size
