Link: http://dx.doi.org/10.21079/11681/35618
Report Number: ERDC/GRL TN-20-1
Title: Spatial Downscaling Disease Risk Using Random Forests Machine Learning
By Sean P. Griffin
Approved for Public Release; Distribution is Unlimited February 2020
Purpose: Mosquito-borne illnesses are a significant public health concern, both to the Department of Defense (DoD) and the broader national and international public health community. A thorough grasp of the spatial distribution, patterns, and determinants of these diseases is needed to truly understand the threats they impose on public health (Pages et al. 2010). This information, when available, is often only at a sub-national to regional scale. Such data fails to meet tactical-level applications when diseases exhibit high local variation (Rytkonen 2004; Linard and Tatem 2012). Additionally, finer spatial resolution is also required to target disease burden successfully within the population and reduce exposure. This technical note (TN) describes a methodology aimed at improving coarse epidemiological information to much finer resolutions than achieved in previous studies by combining machine-learning with open-source, high-performance cloud computing. The result is a 1,000 meter (m) gridded raster product that provides a pixel-wise magnitude of risk that can be used directly for tactical mapping applications or serve as an input dataset for additional modeling applications.
11 pages / 835.7 Kb