Computational, statistical and graph-theoretical methods for disease mapping and cluster detection
Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2008
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/42204 |
| _version_ | 1826212678085902336 |
|---|---|
| author | Wieland, Shannon Christine |
| author2 | Kenneth Mandl and Bonnie Berger. |
| author_facet | Kenneth Mandl and Bonnie Berger. Wieland, Shannon Christine |
| author_sort | Wieland, Shannon Christine |
| collection | MIT |
| description | Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007. |
| first_indexed | 2024-09-23T15:33:00Z |
| format | Thesis |
| id | mit-1721.1/42204 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:33:00Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/422042019-04-11T03:41:34Z Computational, statistical and graph-theoretical methods for disease mapping and cluster detection Wieland, Shannon Christine Kenneth Mandl and Bonnie Berger. Harvard University--MIT Division of Health Sciences and Technology. Harvard University--MIT Division of Health Sciences and Technology. Harvard University--MIT Division of Health Sciences and Technology. Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007. Includes bibliographical references (p. 107-119). Epidemiology, the study of disease risk factors in populations, emerged between the 16th and 19th centuries in response to terrifying epidemics of infectious diseases such as yellow fever, cholera and bubonic plague. Traditional epidemiological studies have led to modifications in hygiene, diet, and many other practices that have profoundly altered the dynamic between humans and diseases. In this thesis, we develop mathematical techniques to address modern challenges, including emerging diseases such as SARS and West Nile virus, the threat of bioterrorism, and stringent legislation protecting patient privacy. Within spatial epidemiology, one problem is to map the risk of disease across space (i.e., disease mapping), and another is to analyze the data for clustering. We propose a general technique, cartograms created from exact patient location data, that can address both of these problems. We also develop a graph-theoretical method to detect spatial clusters of any shape based on Euclidean minimum spanning trees. For mapping applications, we present an optimal strategy for mapping patient locations that preserves both privacy and spatial patterns within the data. For real-time disease surveillance, in which the goal is early detection of outbreaks based on time-series data, we introduce a generalized additive model that maintains constant specificity on various time scales. by Shannon Christine Wieland. Ph.D. 2008-09-03T14:53:10Z 2008-09-03T14:53:10Z 2007 2007 Thesis http://hdl.handle.net/1721.1/42204 230822374 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 119 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Harvard University--MIT Division of Health Sciences and Technology. Wieland, Shannon Christine Computational, statistical and graph-theoretical methods for disease mapping and cluster detection |
| title | Computational, statistical and graph-theoretical methods for disease mapping and cluster detection |
| title_full | Computational, statistical and graph-theoretical methods for disease mapping and cluster detection |
| title_fullStr | Computational, statistical and graph-theoretical methods for disease mapping and cluster detection |
| title_full_unstemmed | Computational, statistical and graph-theoretical methods for disease mapping and cluster detection |
| title_short | Computational, statistical and graph-theoretical methods for disease mapping and cluster detection |
| title_sort | computational statistical and graph theoretical methods for disease mapping and cluster detection |
| topic | Harvard University--MIT Division of Health Sciences and Technology. |
| url | http://hdl.handle.net/1721.1/42204 |
| work_keys_str_mv | AT wielandshannonchristine computationalstatisticalandgraphtheoreticalmethodsfordiseasemappingandclusterdetection |