Course ID: | EPID 8560. 3 hours. |
Course Title: | Analysis of Infectious Disease Data |
Course Description: | A variety of approaches for analyzing and interpreting data
commonly encountered in infectious disease studies are covered,
such as case counts, longitudinal measures of prevalence and
incidence, time-series data, and more. |
Oasis Title: | Analysis Infect Disease Data |
Prerequisite: | BIOS 7010 or BIOS 7010E or permission of department |
Grading System: | A-F (Traditional) |
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Course Objectives: | Students will be able to:
• Appraise different types of analysis approaches
commonly used to study infectious disease data
• Critically compare and evaluate the strengths and
weaknesses of different analysis approaches
• Select the appropriate analysis approach for a given
dataset
• Articulate a research question and outline a data
analysis approach suitable to answering this question for a
given dataset
• Design and implement successful infectious disease data
analyses using the R programming language
• Explain the importance of workflow management and
reproducibility for successful data analysis
• Judge the usefulness of different analysis tools
described in the primary literature on infectious disease
analysis methodology
• Evaluate state-of-the art analysis approaches from the
research literature
• Critically appraise analyses presented in published
studies, identify strengths and weaknesses of other people’s
analyses
• Assess the strengths and weaknesses of different
approaches to representing the results of data analyses
• Summarize analysis results in a way that is easily
understandable for different audiences, such as lay persons,
decision makers, and expert colleagues |
Topical Outline: | 1. Introduction to infectious disease data analysis
2. The data analysis workflow
3. Overview of data types and analysis approaches
4. Getting and pre-processing data
5. Preliminary and graphical data analyses
6. Hypothesis testing and model comparison
7. Analysis using regression models
8. Analysis using machine learning approaches
9. Analysis using dynamical, mechanistic models
10. Quantifying uncertainty
11. Model predictions
12. Presentation of analysis results |
Honor Code Reference: | All students will be expected to comply with the University's
Honor Code and Academic Honesty Policy. |