A Global Approach to Disease Prevention: Predicting High Risk Areas for West Nile Infection in the Us

WN virus has spread for over 60 years creating endemic and epidemic areas throughout Africa, Asia, and Europe, affecting human, bird, and equine populations. Its 1999 appearance in New York shows the ability of the virus to cross barriers and travel great distances, emerging into new territories pr...

Full description

Bibliographic Details
Main Author: DallaPiazza, Kristin Lee
Other Authors: Geography
Format: Others
Published: Virginia Tech 2014
Subjects:
Online Access:http://hdl.handle.net/10919/33083
http://scholar.lib.vt.edu/theses/available/etd-05212009-173330/
id ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-33083
record_format oai_dc
spelling ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-330832020-09-26T05:38:11Z A Global Approach to Disease Prevention: Predicting High Risk Areas for West Nile Infection in the Us DallaPiazza, Kristin Lee Geography Kolivras, Korine N. Carstensen, Laurence William Jr. Resler, Lynn M. mosquito-borne West Nile virus medical geography GIS modeling risk prediction WN virus has spread for over 60 years creating endemic and epidemic areas throughout Africa, Asia, and Europe, affecting human, bird, and equine populations. Its 1999 appearance in New York shows the ability of the virus to cross barriers and travel great distances, emerging into new territories previously free of infection. Spreading much faster than expected, WN virus has infected thousands of birds, equine, and humans throughout the conterminous United States (US). Case and serological studies performed in the Eastern hemisphere prior to 1999 offer detailed descriptions of endemic and epidemic locations in regards to geography, land cover, land use, population, climate, and weather patterns. Based on the severity of WN activity within each study area, the patterns associated with these environmental factors allow for the identification of values associated with different levels of risk. We can then model the landscape of the disease within the US and identify areas of high risk for infection. State and county public health officials can use this model as a decision-making tool to allocate funding for disease prevention and control. Dynamic factors associated with increased transmission, such as above average temperature and precipitation, can be closely monitored and measures of prevention can be implemented when necessary. In turn, detailed information from higher resolution analyses can be documented to an online GIS (Geographic Information System) that would contribute to a global collaboration on outbreaks and prevention of disease. Master of Science 2014-03-14T20:37:54Z 2014-03-14T20:37:54Z 2009-05-04 2009-05-21 2010-04-08 2009-06-05 Thesis etd-05212009-173330 http://hdl.handle.net/10919/33083 http://scholar.lib.vt.edu/theses/available/etd-05212009-173330/ KLD_THESIS.pdf In Copyright http://rightsstatements.org/vocab/InC/1.0/ application/pdf Virginia Tech
collection NDLTD
format Others
sources NDLTD
topic mosquito-borne
West Nile virus
medical geography
GIS modeling
risk prediction
spellingShingle mosquito-borne
West Nile virus
medical geography
GIS modeling
risk prediction
DallaPiazza, Kristin Lee
A Global Approach to Disease Prevention: Predicting High Risk Areas for West Nile Infection in the Us
description WN virus has spread for over 60 years creating endemic and epidemic areas throughout Africa, Asia, and Europe, affecting human, bird, and equine populations. Its 1999 appearance in New York shows the ability of the virus to cross barriers and travel great distances, emerging into new territories previously free of infection. Spreading much faster than expected, WN virus has infected thousands of birds, equine, and humans throughout the conterminous United States (US). Case and serological studies performed in the Eastern hemisphere prior to 1999 offer detailed descriptions of endemic and epidemic locations in regards to geography, land cover, land use, population, climate, and weather patterns. Based on the severity of WN activity within each study area, the patterns associated with these environmental factors allow for the identification of values associated with different levels of risk. We can then model the landscape of the disease within the US and identify areas of high risk for infection. State and county public health officials can use this model as a decision-making tool to allocate funding for disease prevention and control. Dynamic factors associated with increased transmission, such as above average temperature and precipitation, can be closely monitored and measures of prevention can be implemented when necessary. In turn, detailed information from higher resolution analyses can be documented to an online GIS (Geographic Information System) that would contribute to a global collaboration on outbreaks and prevention of disease. === Master of Science
author2 Geography
author_facet Geography
DallaPiazza, Kristin Lee
author DallaPiazza, Kristin Lee
author_sort DallaPiazza, Kristin Lee
title A Global Approach to Disease Prevention: Predicting High Risk Areas for West Nile Infection in the Us
title_short A Global Approach to Disease Prevention: Predicting High Risk Areas for West Nile Infection in the Us
title_full A Global Approach to Disease Prevention: Predicting High Risk Areas for West Nile Infection in the Us
title_fullStr A Global Approach to Disease Prevention: Predicting High Risk Areas for West Nile Infection in the Us
title_full_unstemmed A Global Approach to Disease Prevention: Predicting High Risk Areas for West Nile Infection in the Us
title_sort global approach to disease prevention: predicting high risk areas for west nile infection in the us
publisher Virginia Tech
publishDate 2014
url http://hdl.handle.net/10919/33083
http://scholar.lib.vt.edu/theses/available/etd-05212009-173330/
work_keys_str_mv AT dallapiazzakristinlee aglobalapproachtodiseasepreventionpredictinghighriskareasforwestnileinfectionintheus
AT dallapiazzakristinlee globalapproachtodiseasepreventionpredictinghighriskareasforwestnileinfectionintheus
_version_ 1719342691300409344