Space-time disease map surveillance with extensions to bioterrorism

Abstract

Community-Based Epidemics (ESSENCE), have applied scan statistics for early outbreak detection using both traditional and nontraditional data sources. These sources include medical data indexed by International Classification of Diseases, 9th Revision (ICD-9) diagnosis codes as well as less-specific, but potentially timelier, indicators such as over-the-counter remedy sale totals and school absenteeism records. Early efforts have employed the Kulldorff scan statistic as implemented in the SaTScan software of the National Cancer Institute. A key obstacle is that the input data streams are typically based on time-varying factors such as consumer behavior rather than simply on the populations of the component subregions. Both modeling and recent data have been used to obtain background spatial distributions. Data analyses have provided guidance for determining baseline periods to avoid excessive clustering. We used a simple covariate approach to combining data sources and are evaluating alternative fusion methods in a test bed setting. Experience with this methodology has included combinations of data sources for both retrospective studies of known outbreaks and surveillance of high-profile events of concern to local public health authorities. We have developed capability to test the detection performance of scan statistics as an outbreak unfolds. Spatial and temporal epicurve simulations are used to inject cases into the various streams of authentic data to enable day-by-day performance analysis.