Discovery of under immunized spatial clusters using network scan statistics

Background Clusters of under-vaccinated children are emerging in a number of states in the United States due to rising rates of vaccine hesitancy and refusal. As the measles outbreaks in California and other states in 2015 and in Minnesota in 2017 showed, such clusters can pose a significant public health risk. Prior methods have used publicly-available school immunization data for analysis (except for a few, which use private healthcare patient records). School immunization data has limited demographic information—as a result, such analyses are not able to provide demographic characteristics of significant clusters. Further, the resolution of the clusters identified by prior methods is limited since they are typically restricted to disks or well-rounded shapes.