Aggregation of Location Attributes for Prediction of Infection Risk

In this study we propose an algorithm for the prediction of an infection risk that is based on the aggregation of locations and their use as prediction attributes. Our algorithm is tested on a specific instance of EpiSims simulated data for Portland, OR. The results indicate that location aggregation is very promising approach that can result in high prediction accuracy and could be helpful in modeling of epidemics.