by Jess Behrens
While with Northwestern Medical School, I collaborated on a research project that resulted in a patent (Serial No. 62/287,164). This idea is mine & goes back to a conversation I had with a fellow teaching assistant in the Dept. of Geography-Geology at the University of Nebraska-Omaha (UNO) in December on 1997. The discussion was around the limits to which a geographer could interpolate, or impute, data that had already been aggregated. At that time, I used data from a project at UNO and applied something called 'pycnophylactic interpolation' by way of ESRI's scripting language Avenue. When I went on to study Ecology at Colorado State University, I learned more about the field of geo-statistics, and even attempted a limited version of this work using data from a different project.
The goal of the work, and what we successfully accomplished, was to combine a limited public health dataset with US Census Data to develop a fine resolution, geo-spatially referenced raster showing probable case location. The process involved a number of steps, including Monte Carlo simulations, a krig & a gaussian geo-statistical simulation. Due to publication and patent rules, I can't reproduce images or the math here. However, if you'd like to read about the work, it has been published in the Journal of the American Medical Informatics Association as well as the proceedings from the 2016 American Medical Informatics Association Conference.
Small Area Estimates to Predict Where Medicaid Patients Reside, Journal of the American Medical Informatics Association Conference, 2016.
Behrens, J., Pah, A.R., Goel, S., & Kho, A.N. A Novel Method to Impute the Probability of Disease in Small Areas. Journal of the American Medical Informatics Association
Behrens, J., Wen, X., Goel, S., Zhou, J., Fu, L., & Kho, A.N. Using Monte Carlo/Gaussian Based Small Area Estimates to Predict Where Medicaid Patients Reside AMIA Annu Symp Proc, 2016: 305-309.