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. 2008 Mar 29:7:12.
doi: 10.1186/1476-072X-7-12.

Locating suitable habitats for West Nile Virus-infected mosquitoes through association of environmental characteristics with infected mosquito locations: a case study in Shelby County, Tennessee

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Locating suitable habitats for West Nile Virus-infected mosquitoes through association of environmental characteristics with infected mosquito locations: a case study in Shelby County, Tennessee

Esra Ozdenerol et al. Int J Health Geogr. .

Abstract

Background: Since its first detection in 2001, West Nile Virus (WNV) poses a significant health risk for residents of Shelby County in Tennessee. This situation forced public health officials to adopt efficient methods for monitoring disease spread and predicting future outbreaks. Analyses that use environmental variables to find suitable habitats for WNV-infected mosquitoes have the potential to support these efforts. Using the Mahalanobis Distance statistic, we identified areas of Shelby County that are ecologically most suitable for sustaining WNV, based on similarity of environmental characteristics to areas where WNV was found. The environmental characteristics in this study were based on Geographic Information Systems (GIS) data, such as elevation, slope, land use, vegetation density, temperature, and precipitation.

Results: Our analyses produced maps of likely habitats of WNV-infected mosquitoes for each week of August 2004, revealing the areas that are ecologically most suitable for sustaining WNV within the core of the Memphis urban area. By comparing neighbourhood social characteristics to the environmental factors that contribute to WNV infection, potential social drivers of WNV transmission were revealed in Shelby County. Results show that human population characteristics and housing conditions such as a high percentage of black population, low income, high rental occupation, old structures, and vacant housing are associated with the focal area of WNV identified for each week of the study period.

Conclusion: We demonstrated that use of the Mahalanobis Distance statistic as a similarity index to assess environmental characteristics is a potential raster-based approach to identify areas ecologically most suitable for sustaining the virus. This approach was also useful to monitor changes over time for likely locations of infected mosquito habitats. This technique is very helpful for authorities when making decisions related to an integrated mosquito management plan and targeted health education outreach.

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Figures

Figure 1
Figure 1
Study area and distribution of mosquito traps. This figure shows locations of mosquito traps operated in August 2004 in Shelby County, TN.
Figure 2
Figure 2
Human cases of WNV and number of traps with infected mosquitoes by epidemiologic week, Shelby County, TN. This graph shows the number of WNV-infected humans and the number of traps with infected mosquitoes collected from June through October 2004. Date of onset of illness is used for the reporting of human cases. The epidemiologic week starts on Sunday and ends on Saturday.
Figure 3
Figure 3
Habitat suitability based on MD statistic model (Four weeks of August, 2004). This map shows moderately and highly suitable habitats based on P-values.
Figure 4
Figure 4
WNV high risk tracts. This map shows two tracts in Shelby County, selected based on the intersection of highly suitable habitat areas for WNV-infected mosquitoes for the four weeks of August 2004 based on P-values greater than or equal to 0.9 (P-value ≥ 0.9).
Figure 5
Figure 5
Methodology flowchart.

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References

    1. Ruiz MO, Tadesco C, McTighe TJ, Austin C, Kitron U. Environmental and social determinants of human risk during a West Nile virus outbreak in the greater Chicago area. International Journal of Health Geographics. 2004;3 - PMC - PubMed
    1. Cooke WH, Grala K, Wallis R. Avian GIS models signal human risk for West Nile virus in Mississippi. International Journal of Health Geographics. 2006;5 - PMC - PubMed
    1. Gibbs SEJ, Wimberly MC, Madden M, Masour J, Yabsley MJ, Stalknecht DE. Factors affecting the geographic distribution of West Nile virus in Georgia, USA: 2002–2004. Vector-Borne and Zoonotic Diseases. 2006;6:73–82. doi: 10.1089/vbz.2006.6.73. - DOI - PubMed
    1. Srivastava A, Nagpal B, Saxena R, Subbarao S. Predictive habitat modeling for forest malaria vector species An. dirus in India – a GIS-based approach. Current Science. 2001;80:1129–1134.
    1. Tachiiri K, Klinkenberg B, Mak S, Kazmi J. Predicting outbreaks: A spatial risk assessment of West Nile virus in British Columbia. International Journal of Health Geographics. 2006;5 - PMC - PubMed

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