Optimizing Provider Recruitment for Influenza Surveillance Networks
Figure 7
Predictive performance of ILINets.
Data from the 2001–2007 period were used to design ILINets and estimate multilinear regression prediction models. The predictive performance of the ILINets (y-axis) is based on a comparison between the models' predictions for 2008 hospitalizations (from mock provider reports) and actual 2008 hospitalization data. For almost all network sizes, Submodular optimization (Submodular) outperforms random selection proportional to population density (Random), greedy selection strictly in order of population density (Greedy), and geographic optimization to maximize the number of people that live within 20 miles of a provider [17] (Geographic). The leveling-off of performance around 100 providers is likely a result of over-fitting, given that there were only 222 historical time-points used to estimate the original model.