Spatio-temporal cluster analysis of county-based human West Nile virus incidence in the continental United States.
BACKGROUND
West Nile virus (WNV) is a vector-borne illness that can severely affect human health. After introduction on the East Coast in 1999, the virus quickly spread and became established across the continental United States. However, there have been significant variations in levels of human WNV incidence spatially and temporally. In order to quantify these variations, we used Kulldorff's spatial scan statistic and Anselin's Local Moran's I statistic to uncover spatial clustering of human WNV incidence at the county level in the continental United States from 2002-2008. These two methods were applied with varying analysis thresholds in order to evaluate sensitivity of clusters identified.


RESULTS
The spatial scan and Local Moran's I statistics revealed several consistent, important clusters or hot-spots with significant year-to-year variation. In 2002, before the pathogen had spread throughout the country, there were significant regional clusters in the upper Midwest and in Louisiana and Mississippi. The largest and most consistent area of clustering throughout the study period was in the Northern Great Plains region including large portions of Nebraska, South Dakota, and North Dakota, and significant sections of Colorado, Wyoming, and Montana. In 2006, a very strong cluster centered in southwest Idaho was prominent. Both the spatial scan statistic and the Local Moran's I statistic were sensitive to the choice of input parameters.


CONCLUSIONS
Significant spatial clustering of human WNV incidence has been demonstrated in the continental United States from 2002-2008. The two techniques were not always consistent in the location and size of clusters identified. Although there was significant inter-annual variation, consistent areas of clustering, with the most persistent and evident being in the Northern Great Plains, were demonstrated. Given the wide variety of mosquito species responsible and the environmental conditions they require, further spatio-temporal clustering analyses on a regional level is warranted.
Sugumaran
Ramanathan
R
GeoInformatics Training, Research, Education, and Extension Center, Geography Department, University of Northern Iowa, Cedar Falls, IA, USA. sugu@uni.edu
Larson
Scott R
SR
Degroote
John P
JP
D000818:Animals; D016000:Cluster Analysis; D003710:Demography; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D040362:Geographic Information Systems; D006801:Humans; D015994:Incidence; D014481:United States / Q000453:epidemiology; D014901:West Nile Fever / Q000453:epidemiology*; D014902:West Nile virus