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  • PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

     

      PHW272C: Applied Spatial Data Analysis for Public Health 

    Course Syllabus   Draft Subject to Change 

         

    Table of Contents  Course Description 2 

    Course Goals 2 

    Instructor Information 5 

    Course Format 6 

    Required Course Materials 7 

    Course Schedule 8 

    Course Grading 12 

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  • PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

    Course Requirements 13 

    Completion of Course Modules 13 

    Participation in Course Activities and Discussions 13 

    Final Exam 14 

    Course Communication 14 

    Announcements 14 

    Course mail 14 

    Office hours 14 

    Policies 14 

    Due Dates 14 

    Late Assignments 15 

    Policy on Sharing, Copying, or Reusing SPH Online Materials15 

    Disability support services 15 

    Accommodation of religious creed 15 

    Course evaluations 15 

    Netiquette 16 

    Expectations of Student Conduct 16 

    Academic honesty 17   

     

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  • PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

    Course Description      Spatial analysis is a powerful set of techniques that can be used to describe and explain patterns of health and disease data  through the statistical analysis of locational data. As location information becomes routinely collected alongside health data,  including through the application of mobile and other technologies, public health researchers and practitioners are  increasingly harnessing the power of geography to increase the impact of their public health work. This course will cover the  theory and methods behind the analysis of patterns of health and disease in space. Students will increase their proficiency in  the application of spatial analysis of public health data, and will learn how to perform a wide variety of space and space-time  analyses. The course will introduce statistical techniques useful for describing, analyzing and interpreting layers of mapped  data, including the acquisition and classification of remote sensing data. Exercises will guide students to a stronger  understanding of the role of spatial data science in public health, and will provide a framework for applying spatial analysis to  common questions that arise in public health practice and research. Students will learn how to pose substantive questions  regarding spatial analysis of health data (including in the domain of spatial epidemiology), identify appropriate methods and  data necessary to address spatial questions, apply appropriate spatial statistics to diverse locational data, and report results of  analyses in a clear and interpretable manner to both public health and ion-public health audiences. This course will make  heavy use of R and will touch on different forms of spatial regression analysis. As such, students are expected to be able to use  R to a basic level and be familiar with regression analyses.      

    Course Goals 

    On successful completion of the course you should possess the following skills and knowledge:    

    1. Understanding of the power of geographical analysis and spatial data science for characterizing health and 

    health-related processes in space and time. 

    2. Working knowledge of the theory and methods behind the analysis of patterns of health and disease in space. 

  • PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

    3. Knowledge of statistical techniques useful for describing, analyzing and interpreting layers of mapped data in public 

    health applications. 

    4. Ability to apply appropriate spatial statistics to diverse locational data, and report results of analyses in a clear and 

    interpretable manner to both public health and non-public health audiences. 

         

    Instructor Information 

     

     

    Hugh Sturrock, PhD    Email: [email protected]  Office Hours: Fridays 3-4pm PST 

    Hugh Sturrock, MSc, PhD, is a Spatial Epidemiologist at the UCSF Global Health Group's Malaria Elimination Initiative (MEI)  and an Assistant Professor of Epidemiology and Biostatistics at UCSF. With the MEI, Hugh is focusing on the use and  optimization of active case detection to find and target asymptomatic infections as well as using routine surveillance data to  generate risk maps to guide interventions.    Hugh has a broad interest in optimizing