Spatial Forecasting of Disease Risk and Uncertainty

• Activity Number: 186
• Type: Invited
• Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics & the Environment
• Author(s): Lee DeCola*+
• Companies: U.S. Geological Survey
• Keywords: risk ; kriging ; choropleth ; uncertainty ; disease ; forecasting

Abstract

Because maps usually represent the value of a single variable over 2-D space, they must simplify such problems as multiscale complexity, temporal dynamics, and underlying uncertainty. A choropleth disease risk map based on data for polygonal regions might depict incidence (cases per 100,000 people) within each region for a year but ignore the uncertainty that results from finer-scale variation, generalization, misreporting, small numbers, future unknowns, etc. The problem of Lyme disease forecasting for the United States is used to illustrate an approach to bivariate mapping of data "quantity" and data "quality." Historical state data 1990-2000 are used in an autoregressive model to forecast both 2001-2010 disease incidence as well as a probability index of uncertainty, each of which is then kriged to provide two spatial grids. A bivariate map is produced from the combination of incidence (mapped on a blue-red hue spectrum), and probability (used to control the saturation of the hue at each grid cell). The resultant maps are easily interpretable, and the approach may be applied to such problems as detecting unusual disease occurrences.