  One long-standing question in epidemiological research is how best to
allocate limited amounts of vaccine or similar preventative measures in order
to minimize the severity of an epidemic. Much of the literature on the problem
of vaccine allocation has focused on influenza epidemics and used mathematical
models of epidemic spread to determine the effectiveness of proposed methods.
Our work applies computational models of epidemics to the problem of
geographically allocating a limited number of vaccines within several Texas
counties. We developed a graph-based, stochastic model for epidemics that is
based on the SEIR model, and tested vaccine allocation methods based on
multiple centrality measures. This approach provides an alternative method for
addressing the vaccine allocation problem, which can be combined with more
conventional approaches to yield more effective epidemic suppression
strategies. We found that allocation methods based on in-degree and inverse
betweenness centralities tended to be the most effective at containing
epidemics.
