Temporal Analysis and Spatial Modeling of the Distribution and Abundance of Cs. melanura, Eastern Equine Encephalitis Vector: Connecticut, 1997-2012

Persistent Link:
http://hdl.handle.net/10150/612558
Title:
Temporal Analysis and Spatial Modeling of the Distribution and Abundance of Cs. melanura, Eastern Equine Encephalitis Vector: Connecticut, 1997-2012
Author:
White, Chelsi
Issue Date:
2016
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Eastern Equine Encephalitis virus is a vector-borne virus amplified by the Culiseta melanura mosquito in an enzootic avian cycle, causing high morbidity and mortality to horses and humans when contracted as incidental hosts. The virus is distributed across most of the eastern United States, Canada, and Gulf coast, and has been expanding in geographic range and season of activity over time. Spatial-temporal trends in Cs. melanura abundance were correlated with available meteorological (temperature and precipitation) and remotely sensed environmental data for the period of 1997-2012 in Connecticut. The effects of inter-annual changes in precipitation, temperature, and groundwater levels on Cs. melanura abundances using time-series linear regression and cross-correlation analyses were inconclusive. Habitat modeling using logistic regression and landscape-based predictive variables demonstrated strong efficiency (46.2%) and acceptable sensitivity and specificity (65.6 and 78.6%, respectively) using NDVI difference and distance from palustrine areas as predictive factors. Remotely sensed data can improve the understanding of vector abundance patterns, helping to forecast future outbreaks and regional expansions by guiding surveillance efforts.
Type:
text; Electronic Thesis
Keywords:
Culiseta melanura; Eastern Equine Encephalitis (EEE); Spatial Modeling; Temporal Analysis; Vector-borne Disease; Epidemiology; Connecticut
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Epidemiology
Degree Grantor:
University of Arizona
Advisor:
Brown, Heidi

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleTemporal Analysis and Spatial Modeling of the Distribution and Abundance of Cs. melanura, Eastern Equine Encephalitis Vector: Connecticut, 1997-2012en_US
dc.creatorWhite, Chelsien
dc.contributor.authorWhite, Chelsien
dc.date.issued2016-
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractEastern Equine Encephalitis virus is a vector-borne virus amplified by the Culiseta melanura mosquito in an enzootic avian cycle, causing high morbidity and mortality to horses and humans when contracted as incidental hosts. The virus is distributed across most of the eastern United States, Canada, and Gulf coast, and has been expanding in geographic range and season of activity over time. Spatial-temporal trends in Cs. melanura abundance were correlated with available meteorological (temperature and precipitation) and remotely sensed environmental data for the period of 1997-2012 in Connecticut. The effects of inter-annual changes in precipitation, temperature, and groundwater levels on Cs. melanura abundances using time-series linear regression and cross-correlation analyses were inconclusive. Habitat modeling using logistic regression and landscape-based predictive variables demonstrated strong efficiency (46.2%) and acceptable sensitivity and specificity (65.6 and 78.6%, respectively) using NDVI difference and distance from palustrine areas as predictive factors. Remotely sensed data can improve the understanding of vector abundance patterns, helping to forecast future outbreaks and regional expansions by guiding surveillance efforts.en
dc.typetexten
dc.typeElectronic Thesisen
dc.subjectCuliseta melanuraen
dc.subjectEastern Equine Encephalitis (EEE)en
dc.subjectSpatial Modelingen
dc.subjectTemporal Analysisen
dc.subjectVector-borne Diseaseen
dc.subjectEpidemiologyen
dc.subjectConnecticuten
thesis.degree.nameM.S.en
thesis.degree.levelmastersen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineEpidemiologyen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorBrown, Heidien
dc.contributor.committeememberErnst, Kaceyen
dc.contributor.committeememberGarfin, Greggyen
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.