Syndromic surveillance is defined generally as the collection and statistical analysis of data which are believed to be leading indicators for the presence of deleterious activities developing within a system. Conceptually, syndromic surveillance can be applied to any discipline in which it is important to know when external influences manifest themselves in a system by forcing it to depart from its baseline. Comparing syndromic surveillance systems have led to mixed results, where models that dominate in one performance metric are often sorely deficient in another. This results in a zero-sum trade off where one performance metric must be afforded greater importance for a decision to be made. This thesis presents a dynamic pooling technique which allows for the combination of competing syndromic surveillance models in such a way that the resulting detection algorithm offers a superior combination of sensitivity and specificity, two of the key model metrics, than any of the models individually. We then apply this methodology to a simulated data set in the context of detecting outbreaks of disease in an animal population. We find that this dynamic pooling methodology is robust in the sense that it is capable of superior overall performance with respect to sensitivity, specificity, and mean time to detection under varying conditions of baseline data behavior, e.g. controlling for the presence or absence of various levels of trend and seasonality, as well as in simulated out-of-sample performance tests.