A method for reducing the severity of epidemics by allocating vaccines according to centrality

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 inuenza epidemics and used mathematical models of epidemic spread to determine the effectiveness of proposed methods. Our work applies com- putational 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 central- ity 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 al- location methods based on in-degree and inverse between- ness centralities tended to be the most effective at mitigating epidemics.