Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data

Abstract

BackgroundThe Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF) worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE) uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level.MethodsInfluenza-Like Illness (ILI) was used as a test syndrome to develop methods to improve the spatial accuracy of detected alerts. Simulated incident clusters of various sizes were superimposed on real ILI incidents from the 2008/2009 influenza season. Clusters were detected using the spatial scan statistic and their displacement from simulated loci was measured. Detected cluster size distributions were also evaluated for compliance with simulated cluster sizes.ResultsRelative to the ESSENCE zip code based method, clusters detected using street level incidents were displaced on average 65% less for 2 and 5 mile radius clusters and 31% less for 10 mile radius clusters. Detected cluster size distributions for the street address method were quasi normal and sizes tended to slightly exceed simulated radii. ESSENCE methods yielded fragmented distributions and had high rates of zero radius and oversized clusters.ConclusionsSpatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids. Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases. Thus, further advances in spatial detection accuracy are dependant on systematic improvements in the collection of individual address information.

Mosquito species (Diptera, Culicidae) in three ecosystems from the Colombian Andes: identification through DNA barcoding and adult morphology

“…Additional specimens were obtained by aspirating mosquitoes attracted to humans during the placement and operation of the traps. All data in the sampled localities were plotted following the protocols of Foley et al (2010) using a GPS Garmin® Rino 530HCx. Weather data of average wind speed (km/h), temperature (°C), and relative humidity (%) were recorded with a Kestrel® 4000 Weather Tracker.…”

Implementing Spatial Segregation Measures in R

“…This classification follows the one in Kramer et al [2]. Zone-based measures use aggregated population counts for their calculation, and surface-based measures utilise a continuous population density surface to minimise the so-called modifiable areal unit problem (MAUP) [14].…”

Measuring the spatial accuracy of the spatial scan statistic

“…The advantage of a single measure is that it is straightforward to rank different detection algorithms in terms of their spatial accuracy. Unfortunately, the presence of multiple true and detected clusters within one data set complicates this approach, except when the correspondence of each true and detected cluster is very obvious, as in Savory et al (2010). Also, the distance measure approach does not take account of the size and shape of the true and detected clusters.…”

“…The support of this function is those parts of the study region where this function is defined.This may be a very limited set of points, e.g. Read et al (2009) and Savory et al (2010), where one is only concerned with the exact centre of the true and detected clusters. It may be a more extensive set of points, e.g.…”

“…Read et al (2009) and Savory et al (2010) conducted benchmark studies where both the true and detected clusters have a clearly defined centre. Here the Euclidean distance between these centres provides a single, easily understandable measure of spatial accuracy.…”