Modeling the Impact of Projected Land Cover on Lyme Disease Emergence
| dc.contributor.author | Surendrababu, Jayashree | en |
| dc.contributor.committeechair | Kolivras, Korine N. | en |
| dc.contributor.committeemember | Campbell, James B. Jr. | en |
| dc.contributor.committeemember | Prisley, Stephen P. | en |
| dc.contributor.committeemember | Li, Jie | en |
| dc.contributor.department | Geography | en |
| dc.date.accessioned | 2015-11-29T07:00:27Z | en |
| dc.date.available | 2015-11-29T07:00:27Z | en |
| dc.date.issued | 2014-06-06 | en |
| dc.description.abstract | Lyme disease is a common tick borne disease in the US. Lyme disease emerged from the Northeast and in the past decade, Virginia has been witnessing a rapidly increasing trend in incidence. This thesis uses land cover projection data as a basis to look at the potential future trend of Lyme disease incidence in Virginia for the IPCC (Intergovernmental Panel on Climate change) scenarios of A1B and A2, which indicate a global and regional focus respectively. This study is a continuation of previous work done by an NSF funded research team at Virginia Tech, in exploring the variables affecting Lyme disease in Virginia. A Poisson point process is implemented in this thesis with land cover parameters (implemented land, water bodies, and edge metrics) and demographic parameters (population percentage and per capita income) as the spatial covariates. Lyme disease incidence data obtained from the Virginia Department of Health was used for model validation. The overall model was implemented using Python and its associated libraries while ArcGIS software was used for preliminary covariate analysis and data visualization. This thesis generates risk maps for A1B and A2 scenarios for each decade from 2010 through 2060. Spatial occurrence of disease incidence has been generated by the Poisson point process and the risk level of each county in Virginia has been calculated based on the incidence count predicted for it. Population and area at risk under each scenario for each decade was calculated. Results show that in A1B scenario 22.1% and 42.9% of the total population of Virginia are under high risk and in the A2 scenario, 21% and 33% of the total population of Virginia are under high risk of Lyme disease in 2010 and 2060 respectively. In terms of the area, A1B scenario has 28% under high risk in 2010 and 66% of the total area under high risk in 2060, while A2 scenarGIS, Lyme disease, Land cover projections, IPCC scenariosio has 22.4% under high risk of Lyme disease in 2010 62.7% of the total area in Virginia is under high risk in 2060. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:2996 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/64242 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | GIS | en |
| dc.subject | Lyme disease | en |
| dc.subject | Land cover projections | en |
| dc.subject | IPCC scenarios | en |
| dc.title | Modeling the Impact of Projected Land Cover on Lyme Disease Emergence | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Geography | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |