Poh, Karen Chin (2018-12). Determinants of Spatial and Temporal Variation of West Nile Virus Transmission in Texas. Doctoral Dissertation. | Thesis individual record

West Nile virus (WNV) is a zoonotic vector-borne virus that infects avian and mammal
hosts. In Texas, WNV was first reported in 2002 in Harris County and has since been reported
annually throughout the state. With variable funding available for mosquito surveillance in
Texas, predictive modeling is an economical method for mosquito control, but has not been
parameterized for major metropolitan areas of central and southeast Texas. Thus, this dissertation
uses historical databases to create predictive models that are specifically tailored for major cities
in Texas.
To investigate the 2012 WNV epidemic in Dallas County, TX, logistic regression models
identified an index of urbanization (composed of greater population density, lower normalized
difference vegetation index, higher coverage of urban land types, and more impervious surfaces),
lower elevation, and older populations as key factors in predicting the risk of WNV in Culex
quinquefasciatus. Our model was then extrapolated as a risk map, which highlighted north and
central Dallas County as areas of high risk for WNV-positive mosquitoes.
A similar study for Harris County was conducted, where the best-fit model found that
areas with higher elevation, more impervious surfaces, greater median income, and
predominantly Hispanic populations will have higher vector indexes, which measure the average
number of WNV-infected female Culex mosquitoes collected per trap night. The predictive map
based on this model emphasized high-risk areas in central and north Harris County.
Harris County's long-term database was also used to investigate temporal patterns
between vector abundance, WNV infection in Cx. quinquefasciatus, and weather patterns. A
time-series analysis revealed correlations between abundance and environmental variability
measurements, following our hypothesis of Schmalhausen's law that states organisms are
susceptible to mean (average) temperature and precipitation measurements as well as extreme or
variability in weather. The infection rate model identified temperature with an 8-month lag as a
significant covariate for WNV infection rates, highlighting the importance of overwintering
temperatures preceding the WNV season.
These models (landscape, demographic, and meteorological conditions) can be used by
local mosquito control agencies to predict WNV infection in Cx. quinquefasciatus for proactive
and effective control efforts.

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