Faculty Publications

Title

National and regional associations between human West Nile virus incidence and demographic, landscape, and land use conditions in the coterminous United States

Document Type

Article

Keywords

Culex, Distribution, GIS, Landscape, West Nile virus

Journal/Book/Conference Title

Vector-Borne and Zoonotic Diseases

Volume

12

Issue

8

First Page

657

Last Page

665

Abstract

The incidence of human West Nile virus (WNV) varies spatially and temporally and is influenced by a wide range of biotic and abiotic factors. There are numerous important vector species, with variable geographic ranges and ecologies, considered crucial to the transmission of WNV in the coterminous United States. To date there has been a lack of a systematic investigation in the United States, at a regional scale, of the wide variety of landscape, land use, and demographic influences on WNV incidence. In this study, we use published vector species distribution maps, as well as prominent landscape features, to define six distinct regions of the coterminous United States. We relate data on demographic, landscape, and land use conditions to the incidence of human WNV by region recorded at county level in the coterminous United States from 2002-2009. The observed relationships varied by region with the Great Plains, Northwest, and Southwest regions showing high WNV incidence associated with rural irrigated landscapes, indicating the importance of Culex tarsalis as the primary vector. In the Southeast, the percent of the population in poverty was positively associated with high WNV incidence, potentially indicating the quality of housing in relation to the vector Culex quinquefasciatus, a mosquito that often feeds indoors. The Northeast region human WNV incidence was positively associated with agricultural landscapes, potentially implying the importance of Culex restuans in a region generally thought of as being dominated by Culex pipiens transmission. There was strong spatial autocorrelation in most of the regions, but with a spatial autologistic term accounted for in binary logistic regression models, there were significant landscape, land use, and demographic covariates for each region. © Copyright 2012, Mary Ann Liebert, Inc. 2012.

Original Publication Date

8-1-2012

DOI of published version

10.1089/vbz.2011.0786

Share

COinS