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Metadata Details
Spatial Epidemiology
Contributed by Dr. Geoffrey M. Jacquez, President o |
| version |
Summer 2002 |
| status |
Final |
| rights_restrictions |
unknown |
| rights_description |
unknown |
| rights_cost |
no |
| resource_type |
Lesson Plans/ Syllabi |
| metametadata_contributor_role |
Creator |
| metametadata_contributor_entity |
David Fearon, fear@umail.ucsb.edu |
| metametadata_contributor_date |
2002-07-25 |
| location |
| http://www.sph.umich.edu/geomed/course/ |
| learning_time |
12:00:00 |
| keywords |
epidemiology, Environmental Studies & Policy, ecology, community health, spatial-temporal dynamics, space-time processes, autocorrelation, spatial patterns of diseases, exploratory spatial data analysis, CDC, disease, disease clusters and diffusion, dispe |
| format |
text/html |
| end_user_role |
Teacher |
| description |
Course schedule and portal to html reading modules.This is a graduate level course in the conceptual and analytic tools used to understand how spatial distributions of exposure impact on processes and patterns of disease, introducing students to the special design, measurement, and analysis issues associated with spatial patterns of diseases. We will address contemporary diseases of public health importance and present the statistical and inferential skills that can be used in understanding how spatial patterns arise and what they imply for intervention. [The course objectives are to] provide students with the knowledge, theory, and methodological skills for analyzing and interpreting the spatial patterns of various diseases in order to elucidate underlying exposure processes giving rise to the observed patterns. [The target audience includes] Ph.D. and second-year Masters students in epidemiology, environmental health, ecology and various aspects of community health. Course Description: The course will begin with a description of spatial components of human health data, and the characteristics and covariates of such data. The objectives of spatial analysis are then presented, with an emphasis on (1) quantification of spatial pattern, and (2) mechanisms for inferring past space-time processes from spatial pattern. The CDC guidelines for investigating health event clusters will be reviewed. Spatial statistical methods for quantifying spatial pattern will then be presented, including spatial autocorrelation statistics (both local and general), disease cluster tests (both focused and general) and methods for disease surveillance through both space and time. The framework for using these techniques will be Exploratory Spatial Data Analysis (ESDA), whose objective is the quantification of spatial pattern in order to generate testable hypotheses. Laboratory exercises will use appropriate software (e.g. ArcView, Stat!, GeoMed, Gamma) to analyze example data sets, which will include concrete examples of infectious, toxic-exposure, and other non-infectious diseases. Students will be expected to complete a research project using their own or supplied data, and to produce a manuscript-style report as well as a web-ready presentation (e.g. powerpoint presentation) which will be given in class. |
| CSISS_interest_area |
spatial autocorrelation,spatial-temporal dynamics,spatial diffusion |
| CSISS_discipline |
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| contributor_role_1 |
Author |
| contributor_entity_1 |
Dr. Geoffrey M. Jacquez, President o |
| contributor_date_1 |
2000-04-15 |
| contributor_role_2 |
Author |
| contributor_entity_2 |
Dr. Mark L. Wilson and Dr. Andrew E. |
| contributor_date_2 |
2000-04-15 |
| aggregation_level |
3 |
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